## 1

U.S. Environmental Protection Agency

Mail Code 28221T

1200 Constitution Ave. NW

Washington, D.C. 20460

George M. Bridgers

Air Quality Assessment Division

Office of Air Quality Planning and Standards

U.S. Environmental Protection Agency

Mail Code C439-01

Research Triangle Park, NC 27711

Via Regulations.gov Electronic Filing

Re: Revisions to the Guideline on Air Quality Model s: Enhancements to the AERMOD

Dispersion Modeling System and Incorporation of App roaches to Address Ozone and

Fine Particulate Matter, Docket No. EPA-HQ-OAR-2015 -0310

The Sierra Club submits the enclosed technical anal ysis and comments concerning EPA’s

proposed Revisions to the Guideline on Air Quality Models: Enhancements to the AERMOD

Dispersion Modeling System and Incorporation of App roaches to Address Ozone and Fine

Particulate Matter, demonstrating that the proposed changes to AERMOD will have the effect of

undermining model efficacy, and causing the model t o improperly underpredict air quality

impacts. Accordingly, the proposed model changes s hould be abandoned.

Specifically, while EPA proposes elevating the AERM ET ADJ_U* and AERMOD

LOWWIND3 beta options to regulatory defaults, the e nclosed technical analysis by expert air

modeler Camille Sears demonstrates that use of thes e options—rather than improving model

performance, as EPA claims—decrease model accuracy. As explained in greater detail in Ms.

Sears’s analysis, applying these options to the ori ginal validation studies performed for

AERMOD erratically and in some cases quite signific antly reduces modeled impacts,

particularly so in the case of the Tracy validation study data.

Indeed, EPA’s proposal is predicated not on these v alidation studies, but on testing the options

against severely flawed and outdated datasets that are wholly inappropriate for evaluating model

performance. The Idaho Falls Diffusion Study data is of 1974 vintage, for example, and both

involved a very small sample size of low wind speed gas releases and as well as used improper

wind speed measure methodologies. The Idaho Falls data is thus nearly irrelevant for the task of

ascertaining the accuracy of the ADJ_U* and LOWWIND 3 options. Similarly, the Cordero

Rojo Mine study data is not based on emissions moni toring or tracer gas release rates that could

be known and controlled; thus, the relationship bet ween observed concentrations of air pollutants

and emission rates is unknown, making the data set effectively useless for model evaluation.

## 2

Accordingly, even if EPA had made its ADJ_U* and LO WWIND3 model evaluation analyses

available for public review (which it has not), the se analyses are inherently flawed and cannot

support changes to AERMOD’s regulatory default. Th is is particularly so where validation trial

analyses show the options undermine model accuracy.

In short, the proposed ADJ_U* and LOWWIND3 options do nothing to improve model

performance, and EPA lacks justification for foldin g them into the AERMOD regulatory default.

Indeed, doing so would cripple the efficacy of AERM OD, and lead to significant underprediction

of air pollution impacts, to the detriment of the e nvironment and public health. EPA should not

give these options its regulatory blessing, and sho uld at most retain them as mere beta options.

In addition to these serious concerns with the AERM OD-weakening, the Sierra Club also wishes

to highlight issues with the proposal’s treatment o f ozone and fine particulate air pollution

(PM2.5).

First, EPA needs to establish expedient time frames for developing the proposed tools and

rulemaking for assessing single-source impacts on o zone and secondary PM2.5.

Second, as regards model emission rates for precurs ors (MERPs), any MERPs developed must

be based on levels that protect PSD increments in a ll Class I and II impact areas affected by the

evaluated source, and should further be considered on a location-specific basis, based on both

near-field and long-range transport impacts. Becau se using MERPs as a screening method in

PM2.5 and ozone nonattainment areas is likely to in terfere with progress towards attaining

national ambient air quality standards (NAAQS) for these pollutants, PM2.5 emissions as well as

PM2.5 and ozone precursor emissions that impact unc lassified and nonattainment areas should

be offset by a ratio greater than 1:1.

Third, EPA should develop specific guidance for per forming first tier assessments (i.e., for

emissions greater than any established MERP). For such assessments, any empirical

relationships between precursor pollutants and seco ndary impacts must be based on methods that

protect PSD increments in all Class I and II impact areas affected by the subject source.

Similarly, EPA should develop specific guidance for performing second tier assessments:

allowing facilities or local permitting authorities which modeling system (and which inputs) to

use allows “model-shopping,” and is no improvement over the current situation.

Sincerely,

/s/

Zachary M. Fabish

Staff Attorney

The Sierra Club

50 F Street, NW - 8th Floor

Washington, DC 20001

(202) 675-7917

(202) 547-6009 (fax)

zachary.fabish@sierraclub.org

# Camille Sears 502 W. Lomita Ave., Ojai, CA 93023

Tel: (805) 646-2588 e-mail: camille.marie@sbcglobal.net



## October 25, 2015

EPA Docket Center

1301 Constitution Ave., NW., Room 3334

Washington, DC 20004

Re: Docket#: EPA-HQ-OAR-2015-0310

Proposed Rulemaking and the 11 th 

## Conference on Air Quality Modeling:

Revision to the Guideline on Air Qualit y Models: Enhancements to the AERMOD

Dispersion Modeling System and Incorporatio n of Approaches To Address Ozone and

Fine Particulate Matter

Thank you for the opportunity to comment on th e Proposed Revision to the Guideline on Air

Quality Models and the associated 11 th 

## Conference on Air Quality Modeling, held August 12-13,

2015 at USEPA’s RTP campus. On behalf of Sierra Club, I am submitting comments on the

proposed AERMET ADJ_U* and AERMOD LOWWIND3 changes to the AERMOD Dispersion

Modeling System (FR 80, 145, pp. 45340-45387).

As explained more fully below, the proposed AERMET ADJ_U* and AERMOD LOWWIND3

changes do not improve AERMOD performance, and in fact decrease model accuracy

(unpredictably, and in some cases quite signifi cantly underestimating impacts), based on testing

those changes against the original AERMOD vali dation trial datasets. Moreover, the purported

justification for EPA’s proposal to include t hose changes is based on severely flawed and

outdated datasets from the 1970s that are wholly inappropr iate for evaluating AERMOD

performance. Finally, EPA has failed to ma ke publicly available the AERMET ADJ_U* and

AERMOD LOWWIND3 model evaluation analyses, significantly calling into question the

transparency of a model revision process that has heavily incorporated input from regulated

industry.

Accordingly, the proposed AERMET ADJ_U* and AERMOD LOWWIND3 changes should not

be incorporated into the AERMOD mode ling system as regulatory defaults.

### Docket#: EPA-HQ-OAR-2015-0310

October 25, 2015

Page - 2



## I. Introduction

Among USEPA’s proposed revisions to the Guidel ine on Air Quality Models, I am commenting

on the following two AERMOD and AERMET updates:

2. Updates to EPA’s AERMOD Modeling System

Based on studies presented and discussed at the Tenth Modeling Conference, and

additional relevant resear ch since 2010, the EPA and other researchers have

conducted additional model evaluations and developed changes to the model

formulation of the AERMOD modeling system to improve model performance in

its regulatory applicatio ns. We propose the following updates to the AERMOD

modeling system to address a number of technical concer ns expressed by

stakeholders:

1. A proposed option incorporated in AERMET to adjust the surface friction

velocity (u*) to address issues with AERMOD model overpredi ction under stable,

low wind speed conditions. This proposed op tion is selected by the user with the

METHOD STABLEBL ADJ_U* record in the AERMET Stage 3 input file.

2. A proposed low wind option in AERMOD to address issues with model

overprediction under low wind speed c onditions. The low wind option will

increase the minimum value of the lateral turbulence intensity (sigma-v) from 0.2

to 0.3 and adjusts the dispersion coefficient to account for the e ffects of horizontal

plume meander on the plume centerline con centration. It also eliminates upwind

dispersion which is incongruous with a stra ightline, steady-state plume dispersion

model such as AERMOD. The proposed option is selected by specifying

‘‘LOWWIND3’’ on the CO MODELOPT ke yword in the AERMOD input file. 1



## The notice of proposed rulemaking invite s comments on these and other possible

revisions to the Guideline on Air Quality Models:

We invite comments on whether we ha ve reasonably addressed the technical

concerns expressed by the stakeholder community and are on sound footing to

recommend these updates to the regulat ory default version of the AERMOD

modeling system which includes its repla cement of BLP as an Appendix A model

for the intended regulatory applications. 2

1 

### Federal Register / Vol. 80, No. 145 / Wednesday, July 29, 2015 / Proposed Rules, p. 45345. This document is

available at: http://www3.epa.gov/ttn/scram/11thmodc onf/EPA-HQ-OAR-2015-0310-0001.pdf

. 2 

### Id.

### Docket#: EPA-HQ-OAR-2015-0310

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Page - 3



## To begin with, I suggest that the proposed rule making notice be modified and redistributed for

additional public comment. 3 

## Taking this concern one step furt her, I suggest that the non-default

beta AERMET ADJ_U* and AERMOD LOWWIND3 options in version 15181 should remain

as non-default even after the proposed rulemaking is complete. My comments below provide

evidence that the non-default beta AERMET ADJ_U* and AERMOD LOWWIND3 options in

version 15181 actually decrease mo del performance in many situa tions, and that the bases for

updating these options to regulat ory default status are based on outdated, flawed, and very

limited amounts of data.

I recommend that version 15181 AERMET ADJ _U* and AERMOD LOWWIND3 non-default

options should only be applied on a case-by-case basis, and only after an alte rnative model

demonstration per Section 3.2.2 of the current Gu ideline on Air Quality Models. These options

should not be updated to re gulatory default status.

II. Application of the Proposed AERMET ADJ_U* and AERMOD LOWWIND3

Options Generally Degrad es Model Performance

This comment addresses five field study eval uations of AERMOD v. 15181. The five field study

evaluations include four tall-stack EGU evalua tion databases and one low-level, non-buoyant

release study. As discussed in Comment IV. be low, the data necessary for modeling the Oak

Ridge and Idaho Falls field study eval uations are not publicly available.

The primary purpose of these evaluations is to determine whether the proposed AERMET

ADJ_U* and AERMOD LOWWIND3 options incl uded in v. 15181 improve or degrade

performance compared to the current regulatory de fault options. These model evaluations result

in the following conclusions:

• For the four AERMOD EGU evaluation studie s analyzed here, the proposed AERMET

ADJ_U* and AERMOD LOWWIND3 options d ecrease model performance, and in one

case, significantly under-predicts measured values.

• For the non-buoyant low-level Prairie Grass tracer release, the proposed AERMET

ADJ_U* and AERMOD LOWWIND3 options slightly improve model performance.

3 

### Of the six proposed AERMET/AERMOD updates, only the plume rise for horizontal and capped stacks and the

AERMOD modeling system inclusion of BLP are explicitly recommended for regulatory default status. These two

options are expressly described as regulatory default provisions in the proposed revision to Appendix W, while the

other four options do not have this description. The proposed rulemaking notice should be revised to make it clear

that USEPA intends to make all the proposed AERMOD upda tes as regulatory default. The proposed rulemaking

notice should then be redistributed for additional public comment.

### Docket#: EPA-HQ-OAR-2015-0310

October 25, 2015

Page - 4



## In December 2012, USEPA released revised versi ons of the AERMOD air dispersion model and

AERMET, the meteorological data processor for AERMOD. These programs are known as

AERMOD v. 12345 and AERMET v. 12345, as they were released on Julian day 345 of 2012.

Version 12345 of AERMOD includes non-default beta options to study the effect of varying

minimum values of the standard deviation of the horizontal wind speed fluctuations ( σ v 

## ) and

wind speed (WS). This version of AERMOD al so includes the non-default beta option for

varying the maximum meander component (FRANmax).

Version 12345 of AERMET includes the non-default be ta option to adjust the calculated friction

velocity under low wind speed and stable cond itions (ADJ_U*). In summary, the non-default

beta options are:

LOWWIND1:

• Minimum σ v 

## can be set from 0.01 to 1.0 m/s

• Minimum WS can be set from 0.01 to 1.0 m/s

• No horizontal meander component is included

LOWWIND2:

• Minimum σ v 

## can be set from 0.01 to 1.0 m/s

• Minimum WS can be set from 0.01 to 1.0 m/s

• FRANmax can be set from 0.5 to 1.0

AERMET ADJ_U*:

• Adjusts the calculated friction velocity under low wind speed and stable conditions

Numerous combinations of these non-defau lt beta options can also be assessed.

In July 2015, USEPA released version 15181 of the AERMOD air dispersion model and

AERMET, the meteorological data processor for AERMOD. 4 

## Version 15181 retains the nondefault beta options LOWWIND1 and LOWWIND2, but also includes non-default beta option

LOWWIND3 to study the effect of varying minimu m values of the standa rd deviation of the

horizontal wind speed fluctuations ( σ v 

## ) and wind speed (WS). The non-default beta option

LOWWIND3 is similar to LOWWIND2, increasing the minimum value of sigma-v from 0.2 to

0.3 m/s, but LOWWIND3 eliminates upwind di spersion, consistent with the LOWWIND1

option.

4 

### Between versions 12345 and 15181, USEPA released AERMOD versions 13350 and 14134, which have the same

non-default beta options as v. 12345.

### Docket#: EPA-HQ-OAR-2015-0310

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Page - 5



## A. USEPA 2003 AERMOD Evaluations

USEPA prepared detailed performance evalua tions prior to the release of AERMOD. 5 

## USEPA’s

evaluations included 17 diffe rent field studies. Using these 17 field studies, USEPA showed that

AERMOD closely predicted monitored impacts ba sed on the robust highest concentration (RHC)

statistical method. The RHC is useful for determining whether modeled predictions are

accurately reflecting the highest concentrations th at will be used for verifying compliance with

regulatory design concentrations (typically NAAQS or PSD increments). USEPA’s evaluations

were performed using AERMOD v. 02222.

The RHC “represents a smoothed estimate of the highest concentrations, based on a tail

exponential fit to the upper end of the concentration distribution.” 6 

## The RHC is calculated as

follows

RHC = χ (n) + ( χ ave - χ (n)) * ln((3n-1)/2),

Where:

• n = min(m

o 

## , m); m

o 

## is the number of values used to characterize the upper end of the

concentration distribution, m is the number of values exceeding a specified threshold

value

• n = 26 for AERMOD evaluations

• χ (n) = n th 

## largest value

• χ ave = average of the n-1 largest values

The RHC is an appropriate analysis of AERMOD ’s utility and applicability. Using unpaired

modeling and monitoring results ensures that th e evaluation assesses the highest concentrations

necessary for verifying complia nce with regulatory design c oncentrations. From Cox and

Tikvart:

Because of the nature of some ambient standards, such as those for SO 2 

## , models

must accurately predict the highest 3-h or 24-h average concentration independent

of exactly when or where they may occur. 7



## In addition, by using unpaired modeling and monitoring results, th e RHC is not overly

influenced by uncertainties in measured wi nd speed, wind direction, stability parameters,

emission rates, and release parameters. Any un certainty in these mode l inputs will result in

5 

### USEPA, AERMOD: Latest Features and Evaluation Results, EPA-453/R-03-003, June 2003. This document is

available at: http://www3.epa.gov/ttn/scram/7 thconf/aermod/aermod_mep.pdf

. 6 

### Id., p. 14.

7 

### Cox, W. and Tikvart, J., A Statistic al Procedure for Determining the Best Performing Air Quality Simulation

Model. Atmospheric Environment, 1990, 24A: 2387-2388.

### Docket#: EPA-HQ-OAR-2015-0310

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Page - 6



## decreased model performance, particularly when trying to replicate measured impacts at a

specific location and time. From th e Guideline on Air Quality Models:

As noted above, poor correlati ons between paired concentr ations at fixed stations

may be due to ‘‘reducible’’ uncertainties in knowledge of the precise plume

location and to unquantified inherent unc ertainties. For example, Pasquill

estimates that, apart from data input e rrors, maximum ground-le vel concentrations

at a given hour for a point source in flat terrain could be in error by 50 percent due

to these uncertainties. Uncertainty of fi ve to 10 degrees in the measured wind

direction, which transports the plume, can result in concentrati on errors of 20 to

70 percent for a particular time and lo cation, depending on stab ility and station

location. Such uncertainties do not indicate that an estimated concentration does

not occur, only that the precise time and locations are in doubt. 8



## Consistent with the Guideline on Air Quality Mode ls, RHCs are an effective and accurate way to

evaluate model performance.

For each field study, an RHC is calculated for both the modeled impacts and the monitoring

results collected during the study. The ratio of the modeled RHC to the monitored RHC

represents how well the model predicte d the field study monitoring results.

Model performance can also be demonstrated using Quantile-Quantile (Q-Q) plots, which are

line graphs of modeled concentrations versus pr edicted impacts. The Q-Q plots are based on the

26 highest modeled and monitored concentrat ions (applicable fo r regulatory design

concentrations), and have the following characteristics:

• Concentrations are unpaired in space and time

• Predicted concentrations represent highest modeled impact across all receptors (monitor

locations) for each data period – same as RANKFILE

• Observed concentrations represent highest imp act across all monitors for each data period

• Model performance decreases as values move away from the middle 1:1 line

B. AERMOD/AERMET v. 15181 Evaluations

The non-default beta AERMET ADJ_U* and AE RMOD LOWWIND3 options included in v.

15181 have the potential to change model performan ce when compared to the current regulatory

default options. Using the AERMOD evalua tion databases provided in USEPA’s SCRAM

website, I evaluated model performance for the following AERMOD versions and options:

8 

### USEPA, Revision to the Guideline on Air Quality Mo dels: Adoption of a Preferred General Purpose (Flat and

Complex Terrain) Dispersion Model and Other Revisions, Appendix W to 40 CFR Part 51, November 9, 2005,

Section 9.1.2.b. This document is available at: http://www3.epa.gov/ttn/scram/guidance_permit.htm#appw

.

### Docket#: EPA-HQ-OAR-2015-0310

October 25, 2015

Page - 7



## • AERMOD v. 02222 (the same version used in USEPA’s 2003 AERMOD evaluation)

• AERMOD v. 12345 (off-the-shelf: no beta options)

• AERMOD v. 15181 (off-the-shelf: no beta options)

• AERMOD v. 15181, beta ADJ_U*

• AERMOD v. 15181, beta ADJ_U*, with LOWWIND3, SVmin = 0.3 m/s;

WSmin = 0.5 m/s; FRANmax = 0.95

The field studies I included in my evaluations are:

• Baldwin (1-hr SO 2 

## ): Rural, flat terra in, 3 stacks, HS = 184.4 m

• Kincaid (1-hr SO 2 

## ): Rural, flat terrain, 1 stack, HS = 187 m

• Lovett (1-hr SO 2 

## ): Rural, complex terrain, 1 stack, HS = 145 m

• Tracy (1-hr SF 6 

## ): Rural, complex terrain, 1 stack, HS = 90.95 m

• Prairie Grass (1-hr SF 6 

## ): Rural, flat terrain, 1 st ack, HS = 0.46 m (no plume rise)

The Baldwin, Kincaid, Lovett, and Tracy fiel d studies are useful for assessing model

performance of tall Electric Generating Unit (EGU) stacks. These field studies are particularly

important since the preliminary evaluations used in developing the non-default beta options all

used non-buoyant low-level releases. The LOWWIND1, LOWWIND2, and LOWWIND3 nondefault beta options were evaluated by USEP A using the Idaho Falls and Oak Ridge field

studies. 9 

## These studies are not likely to be applicable to tall-stack EGU emissions.

The AERMET v. 15181 non-default ADJ_U* beta opti on was developed based on results from

the Prairie Grass and Idaho Falls field studies. 10 

## These studies are non-buoyant, low-level

releases that do not reflect disp ersion for tall EGU stacks. Sin ce the Idaho Falls evaluation study

is not publicly availabl e, I was limited to using the Prairie Grass SF 6 

## field study to evaluate

AERMET ADJ_U* and AERMOD LOWWIND3 non-defa ult beta options performance for lowlevel releases of non-buoyant emissions.

9 

### USEPA, User’s Guide for the AMS/EPA Regulatory Model – AERMOD, Addendum, Appendix F, Evaluation of

Low Wind Beta Options, June, 2015. This document is available at:

http://www3.epa.gov/ttn/scram/d ispersion_prefrec.htm#aermod

. 10 

### Qian, Wenjun and Akula Venkatram, Performance of Steady-State Dispersion Models Under Low Wind-Speed

Conditions, Boundary Layer Meteorology, 2011, 138:475–491.

### Docket#: EPA-HQ-OAR-2015-0310

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## C. Evaluation Methods and Results

I evaluated the default and non-default beta options of AERMOD and AERMET described

above using five field studie s (Baldwin, Kincaid, Lovett, Tr acy, and Prairie Grass). The

evaluation methods I used and the re sults I obtained are discussed below. 11



## 1. Meteorological data

I used the AERMET v. 02222 meteorological data pr epared by USEPA in my evaluation runs for

AERMOD v. 02222. These data were provided in model-ready form on the USEPA SCRAM

website. 12 

## I created AERMET v. 12345 meteorological data and AERMET v. 12345 non-default

ADJ_U* meteorological data us ing AERMET v. 12345 and the pertinent input data and options.

I also created AERMET v. 15181 meteorologi cal data and AERMET v. 15181 non-default

ADJ_U* meteorological data us ing AERMET v. 15181 and the pertinent input data and options.

The necessary meteorological data for the Prai rie Grass field study we re not provided with

USEPA’s evaluation database. I contacted USEPA, attempting to obtain these missing data. I

did not receive a reply. To complete the Prairi e Grass evaluation, I used 1956 ISH data for North

Platte, Nebraska. I processed these data with AERMET v. 12345 and v. 15181 using the same

procedures as for the other evaluation databases.

2. Data Processing for RHC Calculations

I formatted the observed concentrations and modeled concentrations to facilitate RHC

calculations. For the observed concentrations, I determined the maximum concentration across

all receptors for each data period. For the mode led concentrations, I wrote a FORTRAN utility

program to read AERMOD postfile concentrations and write the output such that all receptor

concentrations for each data period occupy one record. I then extracted the maximum

concentration across all receptors for each data period, as I did for the observed concentrations.

I then isolated and sorted the 26-highest obser ved and modeled concentrations. From these

values, I calculated modeled RHC and observed (monitored) RHC values as discussed above.

11 

### It is important to note that my evaluations, although they used the evaluation field studies provided by USEPA

(and the same equation for calculating RHC), did not always obtain precisely the same Modeled RHC/Monitored

RHC values presented in USEPA’s AERMOD: Latest Features and Evaluation Results, EPA-453/R-03-003, June

2003; minor discrepancies in the results occur. (This document is available at:

http://www3.epa.gov/ttn/scram/7 thconf/aermod/aermod_mep.pdf . ) I discussed these differences with Roger Brode,

USEPA; however, the reason for the differences was not determined. Nonetheless, these discrepancies do not

detract from the main conclusions of my evaluation.

12 

### http://www.epa.gov/ttn/scram/dispersion_prefrec.htm

.

### Docket#: EPA-HQ-OAR-2015-0310

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## A summary of Modeled RHC/Monitored RHC valu es for these modeled scenarios and field

studies is presented in the following table:

Scenario Baldwin

(1

‐ hr SO 2 

### ) Kincaid

(1

‐ hr SO 2 

### ) Lovett

(1

‐ hr SO 2 

### ) Tracy

(1

‐ hr SF 6 

### ) Prairie

Grass

(1

‐ hr SF 6 

### )

v. 02222 1.42 0.84 0.90 1.05 1.19

v. 12345 1.56 0.83 0.78 1.12 1.16

v. 15181 1.55 0.83 0.77 1.12 1.17

v. 15181, ADJ_U* 1.55 0.83 0.91 0.53 1.19

v. 15181, ADJ_U*, LOWWIND3 (0.3, 0.5, 0.95) 1.40 0.72 0.79 0.42 0.95



## For the Baldwin evaluation study, all versions of AERMOD and options tend to over-predict

observed RHC values. Version 15181 AERMET ADJ_U* and AERMOD LOWWIND3 nondefault options tend to improve model performa nce slightly, comparable to AERMOD v. 02222.

Q-Q plots for each modeled scenario are included in Attachment 1.

For the Kincaid evaluation study, all versions of AERMOD and options tend to under-predict

observed RHC values. Version 15181 AERMET ADJ_U* and AERMOD LOWWIND3 nondefault options tend to decrease modeled impacts and model performance, when compared to all

other regulatory default versions. Q-Q plots for each modeled scenario are included in

Attachment 2.

For the Lovett evaluation study, al l versions of AERMOD and options tend to under-predict

observed RHC values. Version 15181 AERMET ADJ_U* and AERMOD LOWWIND3 nondefault options only slightly a ffect modeled impacts and model performance. Using AERMET

ADJ_U* increases modeled impacts compared to the regulatory default AERMOD v. 15181,

while the combination of AERMET ADJ_U* and AERMOD LOWWIND3 does not. Q-Q plots

for each modeled scenario are included in Attachment 3.

For the Tracy evaluation study, Versi on 15181 AERMET ADJ_U* and AERMOD LOWWIND3

non-default options tend to decr ease modeled impacts and model performance, when compared

to all other versions. For the Tracy evaluati on, these options significan tly under-predict modeled

impacts compared to the current regulatory default versions. Version 15181 AERMET ADJ_U*

and AERMOD LOWWIND3 non-default options presen t a considerable problem for verifying

### Docket#: EPA-HQ-OAR-2015-0310

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## compliance with regulatory design concentrations, as the predicted impacts are less than half of

the measured values. Q-Q plots for each m odeled scenario are included in Attachment 4.

For the Prairie Grass evaluation study, current regulatory default versions of AERMET and

AERMOD approach 1:1 model accuracy. Version 15181 AERMET ADJ_U* and AERMOD

LOWWIND3 non-default options tend to decrease modeled impacts and slightly improve model

performance, when compared to other regulatory default versions. Q-Q plots for each modeled

scenario are included in Attachment 5.

It is important to note that fo r four out of five evaluations I performed, Version 15181 AERMET

ADJ_U* and AERMOD LOWWIND3 non-default opti ons tend to decrease modeled impacts

when compared to the regulatory default versions . This should be seen as a red flag, alerting

USEPA that the AERMET ADJ_U* and AERMOD LOWWIND3 non-default options will likely

degrade model performance in many instan ces. From Roger Brode’s January 18, 2011

declaration:

As shown in Tables 2 and 3 of EP A 2003 (AERMOD: Latest Features and

Evaluation Results, EPA-453/R-03-003, J une 2003), modeling and monitored

results for 1-hour averages are in excellent correlation in thes e studies, with the

ratio of predicted to observed performa nce approaching 1:1 in most instances. 13



## In many modeling applications, versi on 15181 AERMET ADJ_U* and AERMOD LOWWIND3

non-default options will likely degrade performa nce compared to current regulatory default

versions.

Furthermore, the above evaluation analyses incl uded a number of site-specific meteorological

data sets that incorporate low wind speed c onditions. For example, the Tracy evaluation

included meteorological data with wind speeds as low as 0.39 meter/second (m/s); the Kincaid

SO 2 

## evaluation included wind speeds as low as 0. 37 m/s; and the Lovett evaluation included

wind speeds as low as 0.30 m/s. 14 

## Concerns expressed by industr y that the regulatory default

version of AERMOD v. 15181 does not accurately asse ss low wind speed conditions neglects the

data used in these actu al AERMOD evaluations.

13 

### See attached file: br ode-decl-2011-01-18.pdf.

14 

### The AERMOD evaluations and modeled meteorological data are at:

http://www.epa.gov/ttn/scram/dispersion_prefrec.htm

.

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## III. EPA’s Purported Justifications for the AERMET ADJ_U* and AERMOD

LOWWIND3 Options are Based on Outdat ed and Flawed Monitoring Studies

AERMOD underwent over a dozen years of develo pment, review, and evaluations before it was

adopted as a Guideline Air Quality model in 2005. During this lengthy development period,

USEPA used 17 different evaluation study database s to determine the “overall good performance

of the AERMOD model.” From Roger Brode’s January 18, 2011 declaration:

As part of the basis for EPA adopti ng the AERMOD model as the preferred

model for near-field applications in the Guideline on Air Quality Models,

Appendix W to 40 CFR Part 51, the perf ormance of the AERMOD model was

extensively evaluated based on a total of 17 field study data bases (AERMOD:

Latest Features and Evaluation Resu lts. EPA-454/R-03003. U.S. Environmental

Protection Agency, Research Triangle Park (2003), portions of which are attached

to this affidavit) ("EPA 2003"). The scope of the model evaluations conducted for

AERMOD far exceeds the scope of ev aluations conducted on any other model

that has been adopted in Appendix W to Part 51. These evaluations demonstrate

the overall good performance of the AE RMOD model based on technically sound

model evaluation procedures, and also illu strate the significant advancement in

the science of dispersion modeling re presented by the AERMOD model as

compared to other models that have been used in the past. 15



## The Idaho Falls and Oak Ridge studies, whic h form the basis for developing the AERMET

ADJ_U* and AERMOD LOWWIND3 non-default opti ons, were never included in the over

dozen years of AERMOD evaluati on and development. These studies were certainly available

during the 1991 through 2005 AERMOD development period, as they were both performed in

1974. It is unclear why these 40-year old studi es were resurrected to develop the AERMET

ADJ_U* and AERMOD LOWWIND3 non-default options . Indeed, it appears likely that

without intense OAQPS lobbying from industry, these studies would still be in mothball status.

The Idaho Falls and Oak Ridge model evaluati on studies are based on very limited and often

flawed data collection. These model evaluati on studies each comprise only 11 hours of data.

Even if all the data collected during these stud ies could reliably be used to evaluate AERMET

and AERMOD (see below for why they are not), this is a remarkably small data set for USEPA

to be recommending such significant ch anges to the AERMOD modeling system.

A. Concerns with the 1974 Oak Ridge Diffusion Study

The Oak Ridge study includes 11 tr acer gas releases from July and August, 1974. Throughout

this study, the wind speeds measured during the tracer gas releases appear suspect. For example,

15 

### See attached file: br ode-decl-2011-01-18.pdf.

### Docket#: EPA-HQ-OAR-2015-0310

October 25, 2015

Page - 12



## the August 7, 1974 release, occurring from 0715 through 0815 EDT, reports an average wind

speed of 0.26 meter/second. 16 

## This translates to a travel distance of 936 meters/hour. The

sampled concentrations for this period, however, detect SF 6 

## at distances of up to 2,663 meters

away from the release point. 17 

## Samplers beyond hourly plume travel distances should not be

measuring detectable tracer gas concentrations. This implies one of two conditions: either the

measured wind speed for this hour is unrealisticall y low, or the sampled concentrations reflect a

sampling period greater than one-hour. Both of these conditions raise questions about whether

these data should be used for AE RMET/AERMOD evaluation purposes.

These concerns also appear for other rele ase/sampling periods dur ing the Oak Ridge study. 18



## During the August 8, 1974 release, occurri ng from 0645 through 0745 EDT, the reported

hourly-average wind speed is 0.23 meter/second. 19 

## This translates to a travel distance of 828

meters/hour. The sampled concentrati ons for this period, however, detect SF 6 

## at distances of up

to 3,100 meters away from the release point, whic h infers a 3 hours and 45 minutes travel time.

It should be noted that the Oa k Ridge study ground samples were collected from sampling times

of from one to three hours, with most of the tracer gas likely collected during th e first hour.

From NOAA’s Oak Ridge report:

Ground-level sampled values represent a m ean integrated concentration resulting

from sampling times of 1 to 3 hr (tracer releases were 60 min). Most of the tracer

present in a given sample bag was pr obably collected during a 1-hr period.

Therefore, ground-level sample bag concen trations have been normalized through

multiplication by the time of sample collection and division by the length of tracer

release. 20



## Unfortunately, the Oak Ridge report does not list the sampling time for each tracer gas release.

This adds further uncertainty re garding the use of the Oak Ridge sampled values for converting

the currently non-default beta AERMET ADJ_U* and AERMOD LOWWIND3 options to

regulatory default status. Adding even more uncertainty, sampling times over one hour would

require multiple hours of meteorological data, with changing wind conditions for each hour. For

example, a two-hour sample cannot accurately be used to evaluate a model that has only one

hour of meteorologi cal data input.

16 

### NOAA Technical Memorandum ERL ARL-61, Diffusion Under Low Wind Speed Conditions near Oak Ridge,

Tennessee, August 1976, p.13. Available at: http://www.arl.noaa.gov/documents/reports/ARL-61.pdf

. 17 

### Id., p. 52.

18 

### Id., Appendix A.

19 

### Id., p.13.

20 

### Id., p.18.

### Docket#: EPA-HQ-OAR-2015-0310

October 25, 2015

Page - 13



## The Oak Ridge study includes other concerns as well. The sample release periods occur during

summer daylight hours, which are not conducive to assessing night-time conditions where the

atmosphere is likely to be much more stable. This is made evident by the aerial samples taken

during this study which detected tracer gases at levels severa l hundred meters above the ground

surface. 21 

## This level of vertical mixing is not in dicative of stable, low wind speed boundary

layer conditions and should not be used for AERMET ADJ_U* or AERMOD LOWWIND3

development purposes.

B. Concerns with the 1974 Idaho Falls Diffusion Study

The Idaho Falls study includes 11 tracer gas re leases from February through May, 1974. Of

these 11 tracer gas release, only four were for wind speeds less than one meter/second. 22 

## This is

an extremely small sample size for evaluating low wind speed, stable conditions. Moreover,

these wind speeds are measured at four meters ab ove the ground. This is substantially less than

the standard 10 meter anemomet er height recommended by USEPA, and when scaled to 10

meters these measured wind speeds would likely increase. 23



## Several of the Idaho Falls sample release periods occurred during daylight hours, which are less

conducive to assessing night-time conditions where the atmosphere is much more likely to be

more stable. This is expressed by the tilted oil fog plumes observed during this study, depicting

mixing at levels considerably above the ground surface. 24



## From NOAA’s Idaho Falls report:

These results imply that the SF 6 

## and the oil fog plumes were coincident and

partially elevated. The tower samples taken on the 200-m arc during tests 10

through 14 also confirmed that the plumes were elevated. 25



## This level of vertical mixing is not typically seen during st able, low wind speed boundary layer

situations and should not be used for AERMET ADJ_U* or AERMOD LOWWIND3

evaluations for these types of meteorological conditions. Furthe rmore, periods of stagnation

were observed during the Idaho Falls tests:

21 

### Id., Appendix A.

22 

### NOAA Technical Memorandum ERL ARL-52, Diffus ion Under Low Wind Speed, Inversion Conditions,

December 1974, p. 6. Available at: http://www.arl.noaa.gov/documents/reports/ARL-52.pdf

. 23 

### USEPA, Meteorological Monitoring Guidance for Regulatory Modeling Applications , EPA-454/R-99-015,

February 2000, p. 3-4. This document is available at:

http://www3.epa.gov/ttn/scr am/guidance/met/mmgrma.pdf

. 24 

### NOAA Technical Memorandum ERL ARL-52, Diffus ion Under Low Wind Speed, Inversion Conditions,

December 1974, pp. 20-21.

25 

### Id., p. 13.

### Docket#: EPA-HQ-OAR-2015-0310

October 25, 2015

Page - 14



## It may be noted from tables 8 and 9 th at during tests 1, 8, and 11 higher centerline

concentrations were measured at 400 than at 200 m. Stagnation or "puddling"

may have been responsible. 26



## These stagnant conditions can lead to elevated ground-level concentrations remaining in the

vicinity of the emission source for longer than one-hour (the duration of the sampling during the

Idaho Falls study). 27 

## Stagnation and sloshing effects can lead to elevated pollutant

concentrations for several hours, even though plume models, such as AERMOD, erase any

carryover concentrations from one hour to the next. This can lead to model under-predictions

during stagnant and sloshing conditions. In such conditions, a puff model would likely be more

applicable. USEPA’s recommendation that AERMET ADJ_U* and AERMOD LOWWIND3 be

adopted as regulatory default options fails to consider these situations.

C. Concerns with the 1993 Cordero Rojo Mine PM 10 

## Study

It appears that USEPA has reli ed on the Cordero Rojo Mine PM 10 

## analysis for their

recommended updates to the AERMOD modeling sy stem. Although not in cluded in Appendix F

of the AERMOD User’s Guide, USEPA includes this study as part of their AERMET/AERMOD

beta option evaluations presented at the 11 th 

## Conference on Air Quality Modeling. 28 

## USEPA

also includes a portion of the Cordero Rojo Mine PM 10 

## analysis in the available AERMOD test

cases. There are, however, significant conc erns with USEPA’s use of this analysis.

About 75% of Cordero Rojo Mine PM 10 

## emissions come from roadways, but it is unclear

whether the roads are pa ved, unpaved, or some combination of the two. 29 

## Whatever the roadway

types, it is often difficult to get an accurate emission inventory for haul roads. Silt loading for

paved roads, or silt percentage for unpaved road s, must be accurately measured. Otherwise the

calculated emissions will be unreliable for mode l evaluation purposes. Furthermore, the AP-42

emission factors for unpaved roads were updated in November, 2006. 30 

## The AP-42 emission

factors for paved roads were last updated in January, 2011. 31 

## It is not evident which emission

factors were used to calculate the 1993 Corder o Rojo Mine roadway PM 10 

## emissions, since the

emission rate calculations and associated i nputs are not part of USEPA’s test case.

26 

### Id., p. 28.

27 

### Id., p. 12.

28 

### Proposed Updates to AERMOD Modeling System, Roger Brode, OAQPS, 11 th 

### Conference on Air Quality

Modeling, Research Triangle Park, NC, August 12, 2015, pp. 12-16. This document is available at:

http://www3.epa.gov/ttn/scram/11t hmodconf/presentations/1-5_Proposed_Updates_AERMOD_System.pdf

29 

### Id.

30 

### http://www3.epa.gov/ttn/chief/ap 42/ch13/final/c13s0202.pdf

. 31 

### http://www3.epa.gov/ttn/chief/ap 42/ch13/final/c13s0201.pdf

.

### Docket#: EPA-HQ-OAR-2015-0310

October 25, 2015

Page - 15



## In essence, the reviewing public does not know how the Cordero Rojo Mine PM 10 

## emission rates

used by USEPA were calculated, or if these emissi on calculations are accurate. This is not a

normal or acceptable situation for a model eval uation study, where emissions are determined

using continuous emission monitoring or known tracer gas release rate s. The level of uncertainty

associated with roadway and other fugitive PM 10 

## emissions should exclude the Cordero Rojo

Mine PM 10 

## study for AERMET ADJ_U* or AERMOD LO WWIND3 evaluation purposes. If

USEPA intends to use the Cordero Rojo Mine PM 10 

## study for this purpose, then all model inputs,

emission rate calculations, and model performance analyses should be made publicly available.

An additional time period for public review and comment would then be warranted.

Also, the Cordero Rojo Mine PM 10 

## test case models roadway sources with area sources. 32 

## The

Haul Road Workgroup Final Report Submission to EPA-OAQPS recommends that these sources

should be modeled with volum e sources, not area sources. 33 

## This alone would likely improve

model performance. Furthermore, wind erosion PM 10 

## emissions are not incl uded in the test case

modeling, even though some hourly-average wind speeds are greater th an 15 meters/second.

These wind speeds would cause substantial fugitive dust emissions not accounted for in the

Cordero Rojo Mine PM 10 

## study.

For the above reasons, USEPA should not rely on the Cordero Rojo Mine PM 10 

## study for

AERMET ADJ_U* or AERMOD LOWWIND3 eval uation and development purposes.

IV. The AERMET ADJ_U* and AERMOD LO WWIND3 Model Evaluation Analyses

are not Available for Public Review

The primary model evaluation analyses us ed to support the AERMET ADJ_U* and AERMOD

LOWWIND3 non-default options are the Idaho Fa lls, Idaho and Oak Ridge, Tennessee studies. 34



## The reports that describe the studies are publ icly available. The modeling evaluations

themselves, including input data , model results, and analysis of the model results, are not

publicly available. To my knowledge, the m odeling evaluations and associated data are

currently limited to OAQPS, the API (the Ameri can Petroleum Institute), and API’s consultants.

This lack of access to the Idaho falls and Oak Ri dge model evaluations and associated data has

severely limited my ability to review and comment on the proposed AERMOD updates and rule

making. I believe others have been similarly affected.

32 

### Available at: http://www.epa.gov/ttn/scram/models/aermod/aer met_ustar_15181_aermod _15181_lowwind3.zip

. 33 

### http://www3.epa.gov/scram001/reports/Haul_Road_ Workgroup-Final_Repo rt_Package-20120302.pdf

. 34 

### USEPA, User’s Guide for the AMS/EPA Regulatory Model – AERMOD, Addendum, Appendix F, Evaluation of

Low Wind Beta Options, June, 2015. This document is available at:

http://www3.epa.gov/ttn/scram/d ispersion_prefrec.htm#aermod

.

### Docket#: EPA-HQ-OAR-2015-0310

October 25, 2015

Page - 16



## Moreover, the notice of proposed rulemaking misi nforms the public stating that the essential

model evaluation test cases are pub licly available for review and comment. Indeed, the two most

essential evaluation test cases, Idaho Falls a nd Oak Ridge, are not on EPA’s SCRAM Web site.

From the notice of proposed rulemaking:

Model performance evaluation and peer sc ientific review references for the

updated AERMOD modeling system are cite d, as appropriate. An updated user’s

guide and model formulation documents for version 15181 have been placed in

the docket. We have updated the summary description of the AERMOD modeling

system to appendix A of the Guideline to reflect these proposed updates. The

essential codes, preprocessors, and test ca ses have been updated and posted to the

EPA’s SCRAM Web site, http://www.epa.gov/ttn/scram



## . 35



## And from EPA’s SCRAM Web site :



## 9/14/15 The presentations, transcripts, and audio recordings from the 11th

Conference on Air Quality Modeling are now available. Additionally, all of the

model code, technical support documenta tion, draft guidance, IWAQM reports,

supporting docket material, etc. related to the proposed Guideline on Air Quality

Models rulemaking are accessible through the 11th Conference on Air Quality

Modeling informational website

. 36



## Again, missing from this docket material are the ac tual model evaluations for the Idaho Falls and

Oak Ridge studies, which form the basis for developing the AERMET ADJ_U* and AERMOD

LOWWIND3 non-default options. The 17 original model evaluation databases used to develop

AERMOD are available on EPA’s SCRAM Web site , but the Idaho Falls and Oak Ridge studies

are not. Without all the model inputs, outputs, a nd associated data used for the Idaho Falls and

Oak Ridge evaluation studies, the propos ed AERMET ADJ_U* and AERMOD LOWWIND3

options remain unsupported and impossible for complete public review and comment.

USEPA should make the Idaho Falls and Oak Ridge evaluation studies publicly accessible on

EPA’s SCRAM Web site, complete with everyt hing needed to replicate Appendix F of the

AERMOD v. 15181 User’s Guide Addendum. 37 

## Currently, the Idaho Falls and Oak Ridge

evaluation studies are “black boxes,” opaque to virtually all aspects of public review and

analysis. USEPA should extend the proposed ru lemaking public comment period to provide for

additional input once these essential ma terials are made publicly accessible.

35 

### Federal Register / Vol. 80, No. 145 / Wednesday, July 29, 2015 / Proposed Rules, p. 45345. This document is

available at: http://www3.epa.gov/ttn/scram/11thmodc onf/EPA-HQ-OAR-2015-0310-0001.pdf

. 36 

### http://www3.epa.gov/scram001/

. 37 

### USEPA, User’s Guide for the AMS/EPA Regulatory Model – AERMOD, Addendum, Appendix F, Evaluation of

Low Wind Beta Options, June, 2015. This document is available at:

http://www3.epa.gov/ttn/scram/d ispersion_prefrec.htm#aermod

.

### Docket#: EPA-HQ-OAR-2015-0310

October 25, 2015

Page - 17



## USEPA should also make any evaluation studie s that they consider, or rely upon, publicly

accessible on EPA’s SCRAM Web site, complete w ith everything needed to replicate the entire

analysis performed by USEPA. This is par ticularly important given the ongoing publiclyobscure interactions between USEPA and industry.

V. The Proposed AERMET ADJ_U* and AERMOD LOWWIND3 Options Should Not

Be Elevated to Regulatory Defaults, and should at Most Remain as Alternate

Models per Section 3.2 of the Gu ideline on Air Quality Models

Current regulatory guidance allows us ing version 15181 AERMET ADJ_U* and AERMOD

LOWWIND3 non-default options to be used in permitting analyses, providing there is an

appropriate alternative model de monstration per Section 3.2.2 of the current Guideline on Air

Quality Models. This involves collection of s ite-specific meteorological data and other model

inputs to verify whether the alte rnate model performs better than the current regu latory default

version.

It important to note, again, that the AERMET ADJ_U* and AERM OD LOWWIND3 revisions

will in key instances greatly reduce modeled im pacts and model performance compared to the

current regulatory default vers ions. The proposed update of these currently beta non-default

options to regulatory default status is ba sed on very limited, and often flawed, data.

Due to model performance concerns outlined in Section II above, version 15181 AERMET

ADJ_U* and AERMOD LOWWIND3 non-default options should at most be applied on a caseby-case basis, and then only after a detailed, a lternative model demonstration per Section 3.2.2 of

the current Guideline on Air Quality Models. In addition, any alternativ e model demonstrations

that USEPA proposes to use for approving revi sions to the AERMOD modeling system should

be made fully available to the reviewing publ ic. This informati on should include all

meteorological data, model inputs, test data, and a ll other data needed to replicate any alternative

model demonstrations.

Based on existing model evaluation performance, and the lack of evaluations performed using

current technology, version 15181 AERMET ADJ _U* and AERMOD LOWWIND3 non-default

options should not be converted to regulatory default status.

VI. Call for an OAQPS Ombudsman

I am concerned that industry a nd their consultants may have deve loped a distorting relation with

OAQPS in areas of model development. This is evident in the “collabor ation” between industry

and OAQPS that is touted at recent confer ences on air quality modeling and at the

Regional/State/Local modelers workshops.

### Docket#: EPA-HQ-OAR-2015-0310

October 25, 2015

Page - 18



## This collaboration between industry and OAQPS misse s an obvious group that is virtually absent

from technical model development activities – the US citizens that are exposed to the air

pollutants emitted by industry. When budget al lows, these citizens are represented by

environmental groups, but funding is never anywhere close to the amount spent by industry in

model review, development, and revision efforts.

At the 11 th 

## Conference on Air Quality Modeling, both OAQPS and industry acknowledge this

mutual collaboration, without mentioning that ot her stakeholders, with even smaller financial

resources than OAQPS, are missing from the di scussion. From a presentation by Mr. George

Bridges at the 11 th 

## Conference on Air Quality Modeling:

1 And in this case, you know, there's been a lot of

2 collaboration already from th e beginning. I think it's--I

3 was talking to Jeff Masters just before I came up here, and

4 you know, we talked about in the old days how there used to

5 be a lot of collaboration on th e science, and I think we're

6 trying to get back to that. And where we are with this

7 proposal, there has been a fair amount of collaboration

8 leading up to this--this propos al between stakeholders and

9 the EPA here so that we can actually try to put the best

10 science forward in our guideline models.

11 There's a lot of people that have been involved.

12 I know Pete Pagano at Iron and Steel, Cathe Kalisz at API--

13 those folks have all worked with us on various, you know,

14 field studies or data sets and th ings that we've been able to

15 use as we've gone through and tried to upgrade the model and

16 improve the Guideline . And so I want to thank you guys for

17 that contribution.

18 I mean we're all at a place today, I think private

19 sector and public sector, where resources aren't what they

20 used to be, and so where we can work together and leverage, I

21 think we can develop a better product. 38



## And from a presentation by Mr. Rob Kaufmann, Koch Industries, at the 11 th 

## Conference on Air

Quality Modeling:

17 For the record I want to note that I am not

18 related to Andy Kaufman, so I'm not planning to sing or lip

38 

### Transcript from the 11 th 

### Conference on Air Quality Modeling, Wednesda y, August 12, 2015, p. 12. Available at:

http://www3.epa.gov/ttn/scram/11thmodc onf/presentations/2015_Eleventh_Mod eling_Conference-Transcripts_08-

12-2015.pdf

.

### Docket#: EPA-HQ-OAR-2015-0310

October 25, 2015

Page - 19



## 19 synch the words to the Mighty Mouse theme. However, I think

20 it might be appropriate, if you are familiar with the Mighty

21 Mouse them (sic), with a couple of subtle changes, it could have

22 been the theme song for this c onference, "Here we come to

23 save the day. EPA's Appendix W fixes are on the way."

24 Audience member: Sing it. 39



## Mr. Kaufmann further adds:

4 We are pleased to see that some of the--that based

5 on our preliminary reviews so me of those changes have

6 resulted in significant improvements, but we believe that

7 there is a continued need for collaboration between industry

8 and EPA as we go forward with some of those model fixes, and

9 AWMA presentations noted that as well. So we concur with

10 that finding. And we will be pr oviding some more in depth

11 comments for the record once we've had time to fully dive

12 into the Appendix W Federal Register notice. 40



## While collaboration between industry and OAQPS ma y provide worthwhile results at times, this

process can be taken too far. For example, if industry and USEPA persistently recommend

model revisions that tend to reduce modeled impacts, then a systematic model under-prediction

bias will result. Based on t echnical reasons, I do not believe th at the proposed non-default beta

AERMET ADJ_U* and AERMOD LOWWIND3 options warrant being upgr aded to regulatory

default status (see comments above).

In April 2013, I gave a presentation at the RSL Modelers Workshop in Dallas, Texas.

Immediately after my presentation, and before th e next presentation, two State modelers came up

to me and expressed their gratitude that some one other than industry was providing information

to OAQPS. I believe they were concerned with the intense lobbying that industry provides in

matters of air dispersion modeling. Base d on the number of recent AERMET and AERMOD

revisions proposed by industry, I believe this to be a serious problem. And not surprisingly,

industry’s proposed revisions te nd to decrease modeled impact s virtually across-the-board.

The ultimate stakeholders, US citizens made to breathe air pollutants emitted by industry, seem

to be excluded from the model developmen t collaboration between OAQPS and industry. I

suggest that USEPA appoint an Ombudsman to oversee the exchanges between OAQPS and

industry, and to help make these excha nges transparent to the general public.

39 

### Id., p. 242.

40 

### Id., p. 245.

### Docket#: EPA-HQ-OAR-2015-0310

October 25, 2015

Page - 20



## VII. Concluding Remarks

Industry has been intensely lobbying USEPA to modify the AERMOD mode ling system to “fix”

alleged over-prediction problems dur ing low wind speed, stable conditions. This persistent

pressure has developed into a worrisome colla borative model development process between

industry and USEPA. The review ing public, however, has been ex cluded from this two-party

model development process. For example, as noted above, the Oak Ridge and Idaho Falls model

evaluations are not even availa ble for the public to review.

Nonetheless, based largely on the Oak Ridge a nd Idaho Falls studies, USEPA is recommending

that the AERMOD modeling system be modifi ed to include regulatory default AERMET

ADJ_U* and AERMOD LOWWIND3 options. These st udies are based on very limited, flawed,

and outdated data, and should never have been used by USEPA for assessing AERMOD

performance.

The AERMET ADJ_U* and AERMOD LOWWIND3 opti ons often lead to substantial model

under-predictions. For the Tracy pow er plant evaluation shown above, these options cause

AERMOD results to be less than 50% of the m onitored values . This is a serious concern not

addressed by USEPA.

Version 15181 AERMET ADJ_U* and AERMOD LOWWIND3 non-default options should not

be adopted as regulatory default in the AERMOD modeling system. These options, if retained at

all, should at best only be applied on a cas e-by-case basis, and only after a detailed, publicreviewed alternative model demonstration per Section 3.2.2 of the current Guideline on Air

Quality Models.

Thank you for the opportunity to submit thes e comments on the Proposed Revision to the

Guideline on Air Quality Models and the associated 11 th 

## Conference on Air Quality Modeling.

Sincerely,

Camille SearsAttachment 1:Quantile-Quantile Plots for Baldwin EGU EvaluationEvaluationof26highestModeledandMonitoredConcentrations:AERMODv.022220246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.25 (n=11), 1.13 (n=26))02004006008001000120014000 200 400 600 800 1000 1200 1400

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid3‐hourSO2(Mod/ObsRHC=0.76(n=11),0.73(n=26))02004006008001000120014000 200 400 600 800 1000 1200 1400

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid 3-hour SO2(Mod/Obs RHC = 0.76 (n=11), 0.73 (n=26))0500100015002000250030000 500 1000 1500 2000 2500 3000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Baldwin 3-hour SO2(Mod/Obs RHC = 1.39 (n=11), 1.28 (n=26))050010001500200025003000350040000 500 1000 1500 2000 2500 3000 3500 4000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Baldwin 1-hour SO2(Mod/Obs RHC = 1.42 (n=11), 1.42 (n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.123450246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/Obs RHC=0.89(n=11),1.12(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.89(n=11),1.12(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.09 (n=11), 1.06 (n=26))02004006008001000120014000 200 400 600 800 1000 1200 1400

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid 3-hour SO2(Mod/Obs RHC = 0.77 (n=11), 0.74 (n=26))0500100015002000250030000 500 1000 1500 2000 2500 3000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Baldwin 3-hour SO2(Mod/Obs RHC = 1.75 (n=11), 1.44 (n=26))050010001500200025003000350040000 500 1000 1500 2000 2500 3000 3500 4000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Baldwin 1-hour SO2(Mod/Obs RHC = 1.50 (n=11), 1.56 (n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.151810246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.74(n=11),0.84(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.07 (n=11), 0.97 (n=26))02004006008001000120014000 200 400 600 800 1000 1200 1400

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid 3-hour SO2(Mod/Obs RHC = 0.80 (n=11), 0.71 (n=26))0500100015002000250030000 500 1000 1500 2000 2500 3000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Baldwin 3-hour SO2(Mod/Obs RHC = 1.97 (n=11), 1.59 (n=26))050010001500200025003000350040000 500 1000 1500 2000 2500 3000 3500 4000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Baldwin 1-hour SO2(Mod/Obs RHC = 1.66 (n=11), 1.71 (n=26))050010001500200025003000350040000 500 1000 1500 2000 2500 3000 3500 4000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Baldwin 1-hour SO2(Mod/Obs RHC = 1.49 (n=11), 1.55 (n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.15181,BetaADJ_U*0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.74(n=11),0.84(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.07 (n=11), 0.97 (n=26))02004006008001000120014000 200 400 600 800 1000 1200 1400

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid 3-hour SO2(Mod/Obs RHC = 0.80 (n=11), 0.71 (n=26))0500100015002000250030000 500 1000 1500 2000 2500 3000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Baldwin 3-hour SO2(Mod/Obs RHC = 1.97 (n=11), 1.59 (n=26))050010001500200025003000350040000 500 1000 1500 2000 2500 3000 3500 4000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Baldwin 1-hour SO2(Mod/Obs RHC = 1.66 (n=11), 1.71 (n=26))050010001500200025003000350040000 500 1000 1500 2000 2500 3000 3500 4000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Baldwin 1-hour SO2(Mod/Obs RHC = 1.49 (n=11), 1.55 (n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.15181,BetaADJ_U*,LOWWIND3(0.30.50.95)0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.74(n=11),0.84(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.07 (n=11), 0.97 (n=26))02004006008001000120014000 200 400 600 800 1000 1200 1400

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid 3-hour SO2(Mod/Obs RHC = 0.80 (n=11), 0.71 (n=26))0500100015002000250030000 500 1000 1500 2000 2500 3000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Baldwin 3-hour SO2(Mod/Obs RHC = 1.97 (n=11), 1.59 (n=26))050010001500200025003000350040000 500 1000 1500 2000 2500 3000 3500 4000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Baldwin 1-hour SO2(Mod/Obs RHC = 1.66 (n=11), 1.71 (n=26))050010001500200025003000350040000 500 1000 1500 2000 2500 3000 3500 4000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Baldwin 1-hour SO2(Mod/Obs RHC = 1.37 (n=11), 1.40 (n=26))Attachment 2:Quantile-Quantile Plots for Kincaid EGU EvaluationEvaluationof26highestModeledandMonitoredConcentrations:AERMODv.022220246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.25 (n=11), 1.13 (n=26))02004006008001000120014000 200 400 600 800 1000 1200 1400

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid3‐hourSO2(Mod/ObsRHC=0.76(n=11),0.73(n=26))02004006008001000120014000 200 400 600 800 1000 1200 1400

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid 3-hour SO2(Mod/Obs RHC = 0.76 (n=11), 0.73 (n=26))0500100015002000250030000 500 1000 1500 2000 2500 3000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid 1-hour SO2(Mod/Obs RHC = 0.75 (n=11), 0.84 (n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.123450246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/Obs RHC=0.89(n=11),1.12(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.89(n=11),1.12(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.09 (n=11), 1.06 (n=26))02004006008001000120014000 200 400 600 800 1000 1200 1400

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid 3-hour SO2(Mod/Obs RHC = 0.77 (n=11), 0.74 (n=26))0500100015002000250030000 500 1000 1500 2000 2500 3000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid 1-hour SO2(Mod/Obs RHC = 0.75 (n=11), 0.83 (n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.151810246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.74(n=11),0.84(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.07 (n=11), 0.97 (n=26))02004006008001000120014000 200 400 600 800 1000 1200 1400

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid 3-hour SO2(Mod/Obs RHC = 0.80 (n=11), 0.71 (n=26))0500100015002000250030000 500 1000 1500 2000 2500 3000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid 1-hour SO2(Mod/Obs RHC = 0.68 (n=11), 0.87 (n=26))0500100015002000250030000 500 1000 1500 2000 2500 3000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid 1-hour SO2(Mod/Obs RHC = 0.74 (n=11), 0.83 (n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.15181,BetaADJ_U*0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.74(n=11),0.84(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.66(n=11),0.90(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.09 (n=11), 0.97 (n=26))02004006008001000120014000 200 400 600 800 1000 1200 1400

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid 3-hour SO2(Mod/Obs RHC = 0.73 (n=11), 0.70 (n=26))0500100015002000250030000 500 1000 1500 2000 2500 3000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid 1-hour SO2(Mod/Obs RHC = 0.67 (n=11), 0.76 (n=26))0500100015002000250030000 500 1000 1500 2000 2500 3000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid 1-hour SO2(Mod/Obs RHC = 0.76 (n=11), 0.83 (n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.15181,BetaADJ_U*,LOWWIND3(0.30.50.95)0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.74(n=11),0.84(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.66(n=11),0.90(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.64(n=11),0.74(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.14 (n=11), 1.13 (n=26))02004006008001000120014000 200 400 600 800 1000 1200 1400

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid 3-hour SO2(Mod/Obs RHC = 0.77 (n=11), 0.74 (n=26))0500100015002000250030000 500 1000 1500 2000 2500 3000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid 1-hour SO2(Mod/Obs RHC = 0.75 (n=11), 0.83 (n=26))0500100015002000250030000 500 1000 1500 2000 2500 3000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Kincaid 1-hour SO2(Mod/Obs RHC = 0.68 (n=11), 0.72 (n=26))Attachment 3:Quantile-Quantile Plots for Lovett EGU EvaluationEvaluationof26highestModeledandMonitoredConcentrations:AERMODv.022220246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.25 (n=11), 1.13 (n=26))01002003004005006000 100 200 300 400 500 600

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 1-hour SO2(Mod/Obs RHC = 0.89 (n=11), 0.90 (n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.123450246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/Obs RHC=0.89(n=11),1.12(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.89(n=11),1.12(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.09 (n=11), 1.06 (n=26))01002003004005006000 100 200 300 400 500 600

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 1-hour SO2(Mod/Obs RHC = 0.69 (n=11), 0.78 (n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.151810246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.74(n=11),0.84(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.07 (n=11), 0.97 (n=26))01002003004005006000 100 200 300 400 500 600

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 1-hour SO2(Mod/Obs RHC = 0.79 (n=11), 0.77 (n=26))01002003004005006000 100 200 300 400 500 600

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 1-hour SO2(Mod/Obs RHC = 0.68 (n=11), 0.77 (n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.15181,BetaADJ_U*0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.74(n=11),0.84(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.66(n=11),0.90(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.09 (n=11), 0.97 (n=26))01002003004005006000 100 200 300 400 500 600

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 1-hour SO2(Mod/Obs RHC = 0.66 (n=11), 0.69 (n=26))01002003004005006000 100 200 300 400 500 600

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 1-hour SO2(Mod/Obs RHC = 0.91 (n=11), 0.91 (n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.15181,BetaADJ_U*,LOWWIND3(0.30.50.95)0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.74(n=11),0.84(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.66(n=11),0.90(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.64(n=11),0.74(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.52(n=11),0.53(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.51(n=11),0.61(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.07 (n=11), 1.04 (n=26))01002003004005006000 100 200 300 400 500 600

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 1-hour SO2(Mod/Obs RHC = 0.79 (n=11), 0.78 (n=26))01002003004005006000 100 200 300 400 500 600

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 1-hour SO2(Mod/Obs RHC = 0.80 (n=11), 0.79 (n=26))Attachment 4:Quantile-Quantile Plots for Tracy EGU EvaluationEvaluationof26highestModeledandMonitoredConcentrations:AERMODv.022220246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.123450246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/Obs RHC=0.89(n=11),1.12(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.89(n=11),1.12(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.89(n=11),1.12(n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.151810246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.74(n=11),0.84(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.87(n=11),1.12(n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.15181,BetaADJ_U*0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.74(n=11),0.84(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.66(n=11),0.90(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.49(n=11),0.53(n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.15181,BetaADJ_U*,LOWWIND3(0.30.50.95)0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.74(n=11),0.84(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.66(n=11),0.90(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.64(n=11),0.74(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.39(n=11),0.42(n=26))Attachment 5:Quantile-Quantile Plots for Prairie Grass Tracer EvaluationEvaluationof26highestModeledandMonitoredConcentrations:AERMODv.022220246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.25 (n=11), 1.13 (n=26))01002003004005006000 100 200 300 400 500 600

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 1-hour SO2(Mod/Obs RHC = 0.89 (n=11), 0.90 (n=26))02000004000006000008000001000000120000014000000 200000 400000 600000 800000 1000000 1200000 1400000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Prairie Grass 1-hour SO2(Mod/Obs RHC = 1.33 (n=11), 1.19 (n=26))02000004000006000008000001000000120000014000000 200000 400000 600000 800000 1000000 1200000 1400000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Prairie Grass 1-hour SO2(Mod/Obs RHC = 1.33 (n=11), 1.19 (n=26))02000004000006000008000001000000120000014000000 200000 400000 600000 800000 1000000 1200000 1400000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Prairie Grass 1-hour SF6(Mod/Obs RHC = 1.33 (n=11), 1.19 (n=26))02000004000006000008000001000000120000014000000 200000 400000 600000 800000 1000000 1200000 1400000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Prairie Grass 1-hour SF6(Mod/Obs RHC = 1.33 (n=11), 1.19 (n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.123450246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/Obs RHC=0.89(n=11),1.12(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.89(n=11),1.12(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.09 (n=11), 1.06 (n=26))01002003004005006000 100 200 300 400 500 600

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 1-hour SO2(Mod/Obs RHC = 0.69 (n=11), 0.78 (n=26))02000004000006000008000001000000120000014000000 200000 400000 600000 800000 1000000 1200000 1400000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Prairie Grass 1-hour SO2(Mod/Obs RHC = 1.39 (n=11), 1.16 (n=26))02000004000006000008000001000000120000014000000 200000 400000 600000 800000 1000000 1200000 1400000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Prairie Grass 1-hour SO2(Mod/Obs RHC = 1.39 (n=11), 1.16 (n=26))02000004000006000008000001000000120000014000000 200000 400000 600000 800000 1000000 1200000 1400000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Prairie Grass 1-hour SF6(Mod/Obs RHC = 1.39 (n=11), 1.16 (n=26))02000004000006000008000001000000120000014000000 200000 400000 600000 800000 1000000 1200000 1400000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Prairie Grass 1-hour SF6(Mod/Obs RHC = 1.39 (n=11), 1.16 (n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.151810246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.74(n=11),0.84(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.07 (n=11), 0.97 (n=26))01002003004005006000 100 200 300 400 500 600

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 1-hour SO2(Mod/Obs RHC = 0.79 (n=11), 0.77 (n=26))02000004000006000008000001000000120000014000000 200000 400000 600000 800000 1000000 1200000 1400000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Prairie Grass 1-hour SF6(Mod/Obs RHC = 0.62 (n=11), 0.68 (n=26))02000004000006000008000001000000120000014000000 200000 400000 600000 800000 1000000 1200000 1400000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Prairie Grass 1-hour SF6(Mod/Obs RHC = 1.36 (n=11), 1.17 (n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.15181,BetaADJ_U*0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.25 (n=11), 1.13 (n=26))01002003004005006000 100 200 300 400 500 600

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 1-hour SO2(Mod/Obs RHC = 0.89 (n=11), 0.90 (n=26))02000004000006000008000001000000120000014000000 200000 400000 600000 800000 1000000 1200000 1400000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Prairie Grass 1-hour SO2(Mod/Obs RHC = 1.33 (n=11), 1.19 (n=26))02000004000006000008000001000000120000014000000 200000 400000 600000 800000 1000000 1200000 1400000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Prairie Grass 1-hour SO2(Mod/Obs RHC = 1.33 (n=11), 1.19 (n=26))02000004000006000008000001000000120000014000000 200000 400000 600000 800000 1000000 1200000 1400000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Prairie Grass 1-hour SF6(Mod/Obs RHC = 1.33 (n=11), 1.19 (n=26))02000004000006000008000001000000120000014000000 200000 400000 600000 800000 1000000 1200000 1400000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Prairie Grass 1-hour SF6(Mod/Obs RHC = 1.33 (n=11), 1.19 (n=26))Evaluationof26highestModeledandMonitoredConcentrations:AERMODv.15181,BetaADJ_U*,LOWWIND3(0.30.50.95)0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.91(n=11),1.05(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.74(n=11),0.84(n=26))0246810121416180 2 4 6 8 10 12 14 16 18

Predicted Concentration ( μ g/m

3 )ObservedConcentration(μg/m3)Tracy1‐HourSF6(Mod/ObsRHC=0.66(n=11),0.90(n=26))0501001502002500 50 100 150 200 250

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 3-hour SO2(Mod/Obs RHC = 1.09 (n=11), 0.97 (n=26))01002003004005006000 100 200 300 400 500 600

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Lovett 1-hour SO2(Mod/Obs RHC = 0.66 (n=11), 0.69 (n=26))02000004000006000008000001000000120000014000000 200000 400000 600000 800000 1000000 1200000 1400000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Prairie Grass 1-hour SF6(Mod/Obs RHC = 1.01 (n=11), 0.95 (n=26))02000004000006000008000001000000120000014000000 200000 400000 600000 800000 1000000 1200000 1400000

Predicted Concentration ( μ g/m

3 )Observed Concentration (μg/m3)Prairie Grass 1-hour SF6(Mod/Obs RHC = 1.00 (n=11), 0.95 (n=26))

# Attachment 6:

Declaration of Roger W. Brode

### Case: 10-1252 Document: 1288492 Filed: 01/18/2011 Page: 1



## ORAL ARGUMENT NOT YET SCHEDULED

UNITED STATES COURT OF APPEALS

FOR THE DISTRICT OF COLUMBIA CIRCUIT

UNITED STATES ENVIRONMENTAL

PROTECTION AGENCY, NATIONAL ENVIRONMENTAL

DEVELOPMENT ASSOCIATION'S

CLEAN AIR PROJECT,



## Docket No. 10-1252

(and consolidated cases) 

## Petitioner,

Respondent. 

# v. )



## )

)



## )

)

)



## }

)



## }

)

)



## )

)



# ---------------)

DECLARATION OF ROGER W. BRODE

1. My name is Roger W. Brode. I hold B.S. and M.S. degrees in Atmospheric Sciences and I

am currently assigned as a physical scientist in the Air Quality Modeling Group within the

Air Quality Assessment Division of the Office of Air and Radiation's Oftice of Air Quality



### Planning and Standards at the 

### U.S. Environmental Protection Agency 

### ("EPA"), where 

### my

1

### Case: 10-1252 Document: 1288492 Filed: 01/18/2011 Page: 2

responsibilities include the**development~** 

### evaluation and application of air quality dispersion

models and the development of guidance associated with application of such models in

support of EPA regulations governing the Prevention of Significant Deterioration

('"PSD"tpermitting program. I have been involved in the development,**evaluation~** 

### testing,

and documentation of the American Meteorological Society EPA Regulatory Model

("AERMOD") throughout its history. I currently serve as co-chair of the AMS/EP A

Regulatory Model Improvement Committee (AERMIC) consisting of atmospheric scientists

and dispersion model experts overseeing the further technical developnlent of the model, and

as co-chair of the AERMOD Implementation Workgroup consisting of EPA Regional Office

and State dispersion modelers whose charge has been to indentify and assess potential issues

with implementation of the AERMOD model as EPA's preferred model under Appendix W

of Part 

### 51 of the Code of Federal Regulations.

2. The revised primary national ambient air quality standard for oxides of sulfur ('''802

NAAQS") requires that the three year average of the annual 99 th 

### percentile of the daily

maximum I-hour average concentrations of 802 be less than or equal to 75 parts per billion.

In addition, owners and operators of a new major stationary source or a major source

undergoing a major modification located in areas not designated "'nonattainment" for the S02

NAAQS must obtain a PSD pertnit, and to do so must demonstrate (among other things) that

the emissions increases from the new or modified sotITce will not cause or contribute to a

violation of the revised S02 NAAQS. Existing air quality models, including AEM10D, are

readily capable of accurately predicting whether the revised primary S02 NAAQS is attained

and whether individual sources cause or contribute to a violation of the S02 NAAQS.

Specifically, dispersion models that are used to demonstrate compliance with the 802 (and

2

## Case: 10-1252 Document: 1288492 Filed: 01/18/2011 Page: 3

other) NAAQS, including under PSD pemlitting programs, use sequential hourly

meteorological data as the basis for estimating ambient concentration levels. These data are

combined with other inputs (chiefly source emission information, background emissions, and

receptor information) to predict transport and dispersion of emitted pollutant plumes. Since

the key varying inputs to these models are input on an hourly basis, all applications of these

models under the guidance in Appendix W (40 CFR Part 51) are predicated upon the nlodels'

ability to predict hourly ambient concentrations. These models thus generate one-hour air

quality distributions from which the three year average of the annual 99 th 

### percentile of daily

maximum I-hour average concentration of 

## 802 can be readily calculated or otherwise

reasonably approximated.

3. As part of the basis for EPA adopting the AERMOD model as the preferred model for nearfield applications in the Guideline on Air Quality Models, AppendixW to 40 CFR Part 51,

the performance of the AERMOD model was extensively evaluated based on a total of 17

field study data bases (AERMOD: Latest Features and Evaluation Results. EPA-454/R-03-

003. U.S. Environmental Protection Agency, Research Triangle Park (2003), portions of

which are attached to this affidavit) ("EP A 2003"). The scope of the model evaluations

conducted for AERMOD far exceeds the scope of evaluations conducted on any other model

that has been adopted in Appendix W to Part 51. These evaluations demonstrate the overall

good performance of the AERMOD model based on technically sound model evaluation

procedures, and also illustrate the significant advancement in the science of dispersion

modeling represented by theAERMOD model as compared to other models that have been

used in the past. In particular, adoption of the AERMOD model has significantly reduced the

3

## Case: 10-1252 Document: 1288492 Filed: 01/18/2011 Page: 4

potential for overestimation of ambient impacts from elevated sources in complex terrain

compared to other nl0dels.

4. Some of the field studies used to evaluate AERMOD model perfonnance involved ambient

sampling of 

## S02 for a period of one year or more at several (typically about 10) monitors

sited around operating power plants. Other field studies involved sanlpling of controlled

releases of non-reactive tracers, typically SF6, generally over a shorter duration than the

operational studies, but with more robust sampling to facilitate more detailed diagnosis of

model performance. Although the long-term field studies associated with operating power

plants included assessments of 3-hour, 24-hour and even annual average impacts from the

model, evaluation results for 1-hour averages were routinely included for all of the field

studies. As sho\\<ll in Tables 2 and 3 of EPA 2003, modeling and monitored results for 1-

hour averages are in excellent correlation in these studies, with the ratio of predicted to

observed perfoffilance approaching 1: 1 in most instances. Thus, in my opinion, the

perfonnance of the AERMOD model for estimating l-hour ambient concentrations**iswell~**



### documented and the form of the new I-hour 

## 802 standard raises no questions or concerns

regarding the appropriateness of AERMOD.

5. Th.e 

## S02 NAAQS Coalition states that the revised 

## S02 NAAQS is a**~'probabilistic"** 

### standard

and asserts that this makes modeling nlore problematic, especially as compared to the

previous "detenninistic" standard. (Coalition p. 5.) The teffilS "probabilistic" and

"'detenninistic" do not have an ordinarily understood meaning in this context, but 

### it appears

that the assertion is that predictive models like AERMOD are not suitable for a standard

which includes a percentile-based form (where the relevant comparison is to a percentile of

air quality from an air quality distribution), as opposed to an expected exceedance form

4

## Case: 10-1252 Document: 1288492 Filed: 01/18/2011 Page: 5

(whereby a standard may exceeded on a given number of days and compliance is assessed

based on air quality on the designated day once the allowed exceedance days are removed

from the distribution). I know ofuo reason that AERMOD and other similar types of models

is suitable for one type of form and not the other. As just stated in paragraph 2, the models

readily generate air quality distributions from which either percentiles (for the revised S02**NAAQS~** the 99 th 

### percentile) or exceeding days can be determined. In fact, the percentile

form of the i-hour 

## S02 NAAQS is a more "stable" metric than a standard based on the 1 st_



### highest or 2 nd 

### -highest concentrations, since the potential impact of "'outliers H 

### in the

distribution is mitigated, especially when the multi-year average aspect of the 802 NAAQ8

is accounted for.

6. Both the 802 NAAQS Coalition and their affiant Mr. Paine raise a number of points

regarding the issue of whether allowable or actual source emissions should be modeled,

stating that use of allowable emissions overstates sources' impacts. See, e.g. Paine Decl. at

,

11-14. This issue is independent of the predictive accuracy of AERMOD or other models.

7. EPA's rules and guidance provide significant flexibility in the choice of which models to use

in determining if sources cause of contribute to NAAQS violations for purposes ofPSD

permitting. EPA's rules specify that "where an air quality model specified in Appendix W of

this part

... is inappropriate, the model may be modified or another model substituted 

## f' with

""Titten approval from EPA. 40 C.F.R. §5L 166 (1)(2). The rules therefore allow**flexibility~**



### subject to appropriate requirements, for alternative modeling techniques to be applied on a

case-by-case basis subject to approval by appropriate reviewing authority.

8. The declaration of Michael E. Long voices concerns regarding the use of the AERMOD

dispersion model to support implementation of the l·hour 802 standard, and asserts that

5

## Case: 10-1252 Document: 1288492 Filed: 01/18/2011 Page: 6

"'AERMOD significantly over predicts the actual one-hour ambient concentrations in our

area when the available information is used in the model as directed by 

## EPA." Long Decl. at

, 8. This assertion is based on a comparison of model-predicted ambient concentrations to

ambient 

## 802 concentrations reported for 2008 at local EPA monitoring stations in the

vicinity of the ArcelorMittal facilities being modeled. Mr. Long reports that the "AERMOD

model predicted one-hour concentrations that were higher than the monitored values 90% of

the time and the predicted values were as much as 373,131 times higher than the actual

monitored values." Id. Lacking any additional details regarding the model-to-monitor

comparisons cited 

## by Mr. Long, the response here is necessarily limited to a general

discussion of issues involved in such comparisons. A number of factors can affect the

comparison of a modeled concentration with a monitored concentration, including the

accuracy of the emission rate and other source characteristics input to the model, the

representativeness of the meteorological data input to the model, and the influence of local

geographical features and land use characteristics on the transport and dispersion of the

plume. Another key factor that affects comparisons of modeled vs. monitored

concentrations, paired in time and space, is the potential error or uncertainty in the 

### \\lnd

direction input to the model for that hour since the wind direction will determine the

transport direction of the plume, Slight errors in the transport wind direction may account tor

significant differences in modeled vs. monitored concentrations for a specific hour,

especially for elevated plumes under stable atmospheric conditions where the lateral spread

of the plume can be very limited for relatively long transport distances, and errors of a few

degrees in wind direction can be the difTerence in the plume directly impacting the monitor

for a particular hour or the plume missing the monitor completely. In such**cases~** 

### a factor of

6

### Case: 10-1252 Document: 1288492 Filed: 01/18/2011 Page: 7

373,131 difference between modeled and monitored concentrations could easily be

attributable to error or uncertainty in the wind direction. Note that wind directions reported

from routine meteorological monitoring stations located at airports, the most common source

of meteorological data used in air quality 

### modeling applications, are reported to the nearest

10 

### degrees. In**addition~** 

### the comparison may reflect issues related to use of allowable versus

actual emissions, which is irrelevant for purposes of determining whether the AERl\rl0D

model itself is biased.

9. The declaration of Robert J. 

### Paine addresses practical issues in applying the AERMOD

model that allegedly arise due to the fonn of the I-hour S02 standard, as well as concerns

regarding the conservatism of the assumptions on source emissions based on Appendix W

guidance in relation to the I-hour S02 standard. Responses to these issues are summarized

below, numbered according to Mr. Paine's declaration, with some responses applying to

multiple comments:

(a)



### Paine Decl.

, 9.: The AERMOD model "does not yet provide results that anow 

### permit

applicants to follow EPA's guidance tor 

### deternlining whether they comply with the I**~hour** 

### 802

NAAQS because of the unique statistical foml of that**NAAQS.'~**



### Paine Ded. 

# 1 10.: "The form of the I-hour 802 NAAQ8 requires the applicable guideline

dispersion model to compute the highest I-hour concentration for each day at each modeled

receptor point, and to keep track of this daily I-hour maximum concentration statistic for each of

the 365 days for each year modeled independently at each location modeled.

7

### Case: 10-1252 Document: 1288492 Filed: 01/18/2011 Page: 8

Paine Decl.

, 11.: "In the case for which a cumulative modeling analysis is required, this same

procedure must be applied to the combined contributions of the individual source being

permitted, nearby sources and regional background."

Response: As stated in paragraph 2 above, all of these metrics are readily obtainable from model

outputs. Although the existing version of AERMOD does not contain an algorithm from which

these metrics emerge automatically as model outputs, this does not change the result that all of

these metrics are obtainable. In fact,we are aware that Mr. Paine, along with other private sector

parties, developed post-processing tools to compute the I-hour S02 design value based on the

form of the revised S02 NAAQS utilizing model output options available at the time.

(b)



### Paine Decl

, 11.: "Furthermore, EPA in most cases requires a conservatively high regional

background concentration to 

## be added for all hours modeled, rather than the actual values

measured during each hour of the modeling simulation."

Response: EPA issued guidance on a range of issues related to the new I-hour 802 standard on

August 23 2010, including a recommendation that the overall highest I-hour monitored 802

concentration from 

### a representative monitor could be used to account for the monitored

background component in a cumulative impact assessment '"without further justification." We

recognize that use of the overall highest I-hour monitored value may entail a degree of

conservatism that could prevent a source from demonstrating compliance \\lith NAAQS;

however, that conservatism forms the basis for allowing the approach to be used without further

justification. The August 23 memorandum further stated that "Additional refinements to this

'tirst**tier~** 

### approach based 011 

### some level of temporal pairing of modeled and monitored values

8

### Case: 10-1252 Document: 1288492 Filed: 01/18/2011 Page: 9

may be considered on a case-by-case basis, subject to approval by the reviewing authority, with

adequate justification and documentation." However, we also note that Appendix 

### W explicitly

makes ·'no**attempt'~** 

### to "comprehensively define" the criteria involved in detennining which

nearby sources to inc1udein an analysis "owing to both the Wliqueness of each modeling

situation and the large number of variables involved in identifying nearby sources." See

Appendix W section 8.2.3.b.

(c)



### Paine Dec!.' 

## 12.: "Following EPA's regulatory requirements for PSD modeling, the tnodeled

predictions of hourly concentrations of a probabilistic standard such as the 99 th 

### percentile daily

maximum hourly S02 concentrations produced 

## by a single source tor which a pennit is sought

can be nluch higher than concentrations that actually occur in the ambient**air.~'**



### Response: As noted in paragraph 6 above, the issue of allowable versus actual emissions is

independent to the question of the accuracy of AERMOD or other models. Also, as stated in

paragraph 5 above, there is no reason that AERMOD (or other similar models) is not equally

accurate in predicting percentile air quality distributions or expected exceedances on a given day.

The underlying data which are input to the model generate air quality distributions which are

equally suitable for either type of form.

(d)



### Paine Dec!.

, 12. : "Modeling of peak 

## 802 emissions as if they occur continuously is a

distortion of reality and will overestimate the ambient air concentrations. This is especially true

for I-hour averages, since the variation of emissions for such a short averaging period is

9

### Case: 10-1252 Document: 1288492 Filed: 01/18/2011 Page: 10

potentially much higher than that for the other S02 NAAQS averaging periods. This makes the

assumption of constant peak emissions a critical issue for this new standard."

Response: The purpose of dispersion modeling in the context of the PSD permitting program is

to demonstrate that the proposed new or modified emissions will not cause or contribute to

violations of the standard if the penl1it is granted. This is inherently a predictive exercise since 

## it

entails an assessment of proposed future emissions. EPA's guidance for conducting such

analyses is dictated 

## by and consistent with that purpose. Mr. Paine's statement that I-hour

averages are more variable than longer averaging periods again does not relate to potential model

bias and in any case makes a sweeping generalization for situations that diner case-by-case. The

statement that peak S02 emissions should not be modeled is a restatement of the dispute as to

use of allowable or actual**emissions~** 

### and does not relate to the issue of model bias.

(e)



### Paine Decl.**~.** 

## 13.: "The model overprediction tendency is even more likely to be a problem in a

cwnulative impact analysis because numerous sources (i.e., the source being pennitted and

potentially thousands of other nearby sources) are all modeled at peak etnissions at all times and

added to a regional background level of S02

... leading to unrealistic predictions that the I-hour

S02 NAAQS will be exceeded."

Response: As noted, the issue of allowable versus actual emissions is independent of the issue of

models' predictive accuracy. However, EPA's August 

## 23,2010 clarification memo regarding

the applicability of Appendix W guidance for the I-hour S02 NAAQS cautioned "against the

literal and uncritical application of very prescriptive procedures for identifying which

background sources should be included in the modeled emission inventory for 

### NAAQS



### 10

## Case: 10-1252 Document: 1288492 Filed: 01/18/2011 Page: 11

compliance demonstrations, including those described in Chapter C, Section IV.C.l of the draft

New Source Review Workshop Manual (EPA, 1990), noting [again] that Appendix Vol



### emphasizes the importance of professional judgment in this process." One motivation for that

caution was a concern that application of such procedures could lead to an overly conservative

result by including too many background sources in the cumulative impact assessment. As noted

elsewhere, Section 8.2.3.b of Appendix W suggests that '"the number of such sources is expected

to 

## be small except in unusual situations."

(f)

Paine Decl. 13.: '"Moreover, since the nearby sources will be modeled individually (but their

emissions are already accounted for in the regional monitoring), there will inevitably be doublecounting of the background impacts between the components of the "nearby**sources'~** 

### and the

"'regional background", especially for the common situation of the state requiring a single peak

regional background value to be used for all modeled hours."

Response: As noted in several responses above, there are many application-specitic factors that

need to be considered in determining how to conduct an adequate assessment of cumulative

impacts, accounting tor contributions from nearby background sources explicitly in the model as

well as a monitored contribution, while avoiding or minimizing the potential for double-counting

of modeled and monitored impacts.



### (g)

Paine Decl.

, 14.: 

### "The distribution of total peak daily emissions over the three-year period of

2000-2002 

### [from major 802 

### sources in central North Dakota] was found to overpredict the

second-highest monitored 24-hour c,oncentrations by roughly a factor of 2 because the emissions

11

### Case: 10-1252 Document: 1288492 Filed: 01/18/2011 Page: 12

on average are lower than peak 

### values assumed in the modeling. For the probabilistic I-hour

standard

... and for 

### closer receptors, the overprediction ratio would likely be even higher than

for a 24-hour average, causing extensive areas of fictitious modeled NAAQS violations.

n



### Response: The first statement in this comment merely confirms what was indicated in an earlier

response, namely that modeled impacts based on maximum allowable emissions should 110t be

expected to accurately predict ambient monitored concentrations in most cases, since monitored

concentrations can only reflect impacts from actual emissions. Overprediction by a factor of 2

does not suggest a significant degree of conservatism given that tnodeled emissions reflected

peak emissions. No rationale is offered to support the assertion that the overprediction ratio

would likely be even higher for the I-hour standard, and we see no reason to expect that

necessarily to be the case.

(h)



### Paine Decl. 

## 14.: "Based on my experience with modeling the I-hour NAAQS for nitrogen

dioxide

- a NAAQS that is similar in form to the I-hour S02 NAAQS

- this overprediction ratio

could approach a factor of lOin areas with numerous sources modeled together."

Response: Although the form of the I-hour N02 standard is very similar to the 

## tornl of the 1

M



### hour 802 standard, the role of NO x chemistry in modeling ambient N02 impacts associated with

NOx emissions makes it difficult to draw comparisons between the two standards in terms of the

potential for the model to overestimate ambient impacts as compared to monitored

concentrations. The comment does not indicate what assumptions were Inade in the N02

modeling analyses regarding the conversion of NO emissions to ambient N02. An overly

conservative assumption in relation to that conversion could introduce a significant bias in the



### 12

### Case: 10-1252 Document: 1288492 Filed: 01/18/2011 Page: 13

modeled concentrations relative to monitored concentrations ofN02 that would have no

relevance to modeling I-hour 802 impacts.

(i)

Paine Decl.' 15. : '"If a cumulative modeling assessment shows violations of the NAAQS, then

the PSD permit applicant can still obtain a permit for its source by showing that the proposed

source does not contribute significantly to the modeled violation. EP A, however, has not yet

defined a procedure for determining whether a proposed source that conducts a cumulative

modeling analysis and finds nlodeled violations due to other sources is by itself causing or

contributing to these predicted (and possibly false) I-hour S02 NAAQS violations. This "safety

valve" thus does not yet exist for applicants trying to demonstrate that their proposed 802-

emitting sources will not cause or contribute to any modeled violations of the I-hour 802

NAAQS."

Response: Recognizing the importance of the significant contribution test within the PSD

permitting program, EPA recommended an interim Significant Inlpact Level (SIL) in its August

23 guidance memorandum regarding the I-hour S02 NAAQS. This interim SIL provides the

"safety valve" that may allow a pennit applicant to obtain a permit in cases where the cumulative

impact assessment shows modeled violations of the l-hour S02 NAAQS, 

### if it can be

demonstrated that the proposed emission increases do not contribute significantly to those

modeled violations, paired in time and space. Although the form of the I-hour S02 standard

may complicate the '"bookkeeping" needed to make such a demonstration, the principle of the

significant contribution test based on the SIL has not changed under the I-hour S02 NAAQS.



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### Case: 10-1252 Document: 1288492 Filed: 01/18/2011 Page: 14

Pursuant to 

### 28 U.S.c. § 

### 1746, and under penalty of perjury! I declare the foregoing js



## true and 

## correct to the best of my knowledge.

Date Roger W. Brode



### 14