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 Models: Enhancements to the AERMOD 

Dispersion Modeling System and Incorporation of Approaches to Address Ozone and 

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

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

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

Dispersion Modeling System and Incorporation of Approaches 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 to improperly underpredict air quality 

impacts. Accordingly, the proposed model changes should be abandoned. 

Specifically, while EPA proposes elevating the AERMET ADJ_U* and AERMOD 

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

modeler Camille Sears demonstrates that use of these 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 original validation studies performed for 

AERMOD erratically and in some cases quite significantly reduces modeled impacts, 

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

Indeed, EPA’s proposal is predicated not on these validation 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against severely flawed and outdated datasets that are wholly inappropriate for evaluating model 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 LOWWIND3 options. Similarly, the Cordero 

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

be known and controlled; thus, the relationship between 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 LOWWIND3 model evaluation analyses 

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

support changes to AERMOD’s regulatory default. This 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 folding them into the AERMOD regulatory default. 

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

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

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

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

to highlight issues with the proposal’s treatment of 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 ozone and secondary PM2.5. 

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

be based on levels that protect PSD increments in all 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. Because using MERPs as a screening method in 

PM2.5 and ozone nonattainment areas is likely to interfere 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 unclassified and nonattainment areas should 

be offset by a ratio greater than 1:1. 

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

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

relationships between precursor pollutants and secondary 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 11th Conference on Air Quality Modeling: 

Revision to the Guideline on Air Quality Models: Enhancements to the AERMOD 

Dispersion Modeling System and Incorporation of Approaches To Address Ozone and 

Fine Particulate Matter 

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

Quality Models and the associated 11th 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 significantly underestimating impacts), based on testing 

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

justification for EPA’s proposal to include those changes is based on severely flawed and 

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

performance. Finally, EPA has failed to make 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 modeling system as regulatory defaults. 
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October 25, 2015 

Page - 2 

I. Introduction 

Among USEPA’s proposed revisions to the Guideline 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 research 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 applications. We propose the following updates to the AERMOD 

modeling system to address a number of technical concerns expressed by 

stakeholders: 

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

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

low wind speed conditions. This proposed option 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 conditions. 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 effects of horizontal 

plume meander on the plume centerline concentration. It also eliminates upwind 

dispersion which is incongruous with a straightline, steady-state plume dispersion 

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

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

The notice of proposed rulemaking invites comments on these and other possible 

revisions to the Guideline on Air Quality Models: 

We invite comments on whether we have reasonably addressed the technical 

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

recommend these updates to the regulatory default version of the AERMOD 

modeling system which includes its replacement 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/11thmodconf/EPA-HQ-OAR-2015-0310-0001.pdf. 2

 Id. 
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To begin with, I suggest that the proposed rulemaking notice be modified and redistributed for 

additional public comment.3 Taking this concern one step further, 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 model performance in many situations, and that the bases for 

updating these options to regulatory 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 alternative model 

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

should not be updated to regulatory default status. 

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

Options Generally Degrades Model Performance 

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

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

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

Ridge and Idaho Falls field study evaluations are not publicly available. 

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

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

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

in the following conclusions: 

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

ADJ_U* and AERMOD LOWWIND3 options decrease 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 updates 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 versions 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 (σv) and 

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

varying the maximum meander component (FRANmax). 

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

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

beta options are: 

LOWWIND1: 

• Minimum σv 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 σv 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-default 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 minimum values of the standard deviation of the 

horizontal wind speed fluctuations (σv) 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 dispersion, 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. 
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A. USEPA 2003 AERMOD Evaluations 

USEPA prepared detailed performance evaluations prior to the release of AERMOD.5 USEPA’s 

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

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

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

accurately reflecting the highest concentrations that 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) + (χave - χ(n)) * ln((3n-1)/2), 

Where: 

• n = min(mo, m); mo 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) = nth largest value 

• χave = 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 the evaluation assesses the highest concentrations 

necessary for verifying compliance with regulatory design concentrations. From Cox and 

Tikvart: 

Because of the nature of some ambient standards, such as those for SO2, 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, the RHC is not overly 

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

emission rates, and release parameters. Any uncertainty in these model 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/7thconf/aermod/aermod_mep.pdf. 6 Id., p. 14. 

7

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

Model. Atmospheric Environment, 1990, 24A: 2387-2388. 
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Page - 6 

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

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

As noted above, poor correlations between paired concentrations at fixed stations 

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

location and to unquantified inherent uncertainties. For example, Pasquill 

estimates that, apart from data input errors, maximum ground-level 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 five to 10 degrees in the measured wind 

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

70 percent for a particular time and location, depending on stability 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 Models, 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 predicted 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 predicted impacts. The Q-Q plots are based on the 

26 highest modeled and monitored concentrations (applicable for 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 impact 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 AERMOD LOWWIND3 options included in v. 

15181 have the potential to change model performance when compared to the current regulatory 

default options. Using the AERMOD evaluation 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 Models: 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 SO2): Rural, flat terrain, 3 stacks, HS = 184.4 m 

• Kincaid (1-hr SO2): Rural, flat terrain, 1 stack, HS = 187 m 

• Lovett (1-hr SO2): Rural, complex terrain, 1 stack, HS = 145 m 

• Tracy (1-hr SF6): Rural, complex terrain, 1 stack, HS = 90.95 m 

• Prairie Grass (1-hr SF6): Rural, flat terrain, 1 stack, HS = 0.46 m (no plume rise) 

The Baldwin, Kincaid, Lovett, and Tracy field 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 USEPA 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 option 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 dispersion for tall EGU stacks. Since the Idaho Falls evaluation study 

is not publicly available, I was limited to using the Prairie Grass SF6 field study to evaluate 

AERMET ADJ_U* and AERMOD LOWWIND3 non-default 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/dispersion_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.
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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 studies (Baldwin, Kincaid, Lovett, Tracy, and Prairie Grass). The 

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

1. Meteorological data 

I used the AERMET v. 02222 meteorological data prepared 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 using AERMET v. 12345 and the pertinent input data and options. 

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

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

The necessary meteorological data for the Prairie Grass field study were 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 Prairie 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 modeled 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 observed 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/7thconf/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. 
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A summary of Modeled RHC/Monitored RHC values for these modeled scenarios and field 

studies is presented in the following table: 

Scenario

Baldwin      

(1‐hr SO2)

Kincaid       

(1‐hr SO2)

Lovett        

(1‐hr SO2)

Tracy         

(1‐hr SF6)

Prairie

Grass        

(1‐hr SF6)

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 performance 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, all versions of AERMOD and options tend to under-predict 

observed RHC values. Version 15181 AERMET ADJ_U* and AERMOD LOWWIND3 nondefault options only slightly affect 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, Version 15181 AERMET ADJ_U* and AERMOD LOWWIND3 

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

to all other versions. For the Tracy evaluation, these options significantly under-predict modeled 

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

and AERMOD LOWWIND3 non-default options present a considerable problem for verifying 
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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 modeled 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 for four out of five evaluations I performed, Version 15181 AERMET 

ADJ_U* and AERMOD LOWWIND3 non-default options 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 instances. From Roger Brode’s January 18, 2011 

declaration: 

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

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

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

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

In many modeling applications, version 15181 AERMET ADJ_U* and AERMOD LOWWIND3 

non-default options will likely degrade performance compared to current regulatory default 

versions. 

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

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

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

SO2 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 industry that the regulatory default 

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

data used in these actual AERMOD evaluations. 

                                                                  13 See attached file: brode-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 Outdated and Flawed Monitoring Studies 

AERMOD underwent over a dozen years of development, 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 databases 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 adopting 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 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-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 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 the AERMOD model as 

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

The Idaho Falls and Oak Ridge studies, which form the basis for developing the AERMET 

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

dozen years of AERMOD evaluation 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 studies 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 evaluation studies are based on very limited and often 

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

Even if all the data collected during these studies 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 changes to the AERMOD modeling system. 

A. Concerns with the 1974 Oak Ridge Diffusion Study 

The Oak Ridge study includes 11 tracer 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: brode-decl-2011-01-18.pdf. 
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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 SF6 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 unrealistically 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 AERMET/AERMOD evaluation purposes. 

These concerns also appear for other release/sampling periods during the Oak Ridge study.18 

During the August 8, 1974 release, occurring 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 concentrations for this period, however, detect SF6 at distances of up 

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

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

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

From NOAA’s Oak Ridge report: 

Ground-level sampled values represent a mean 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 probably collected during a 1-hr period. 

Therefore, ground-level sample bag concentrations 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 regarding 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 meteorological 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. 
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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 several hundred meters above the ground 

surface.21 This level of vertical mixing is not indicative 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 releases 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 above the ground. This is substantially less than 

the standard 10 meter anemometer 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 SF6 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 stable, 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. Furthermore, periods of stagnation 

were observed during the Idaho Falls tests: 

                                                                  21 Id., Appendix A. 

22 NOAA Technical Memorandum ERL ARL-52, Diffusion 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/scram/guidance/met/mmgrma.pdf. 24 NOAA Technical Memorandum ERL ARL-52, Diffusion Under Low Wind Speed, Inversion Conditions, 

December 1974, pp. 20-21. 

25 Id., p. 13. 
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It may be noted from tables 8 and 9 that 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 PM10 Study 

It appears that USEPA has relied on the Cordero Rojo Mine PM10 analysis for their 

recommended updates to the AERMOD modeling system. Although not included 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 11th Conference on Air Quality Modeling.28 USEPA 

also includes a portion of the Cordero Rojo Mine PM10 analysis in the available AERMOD test 

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

About 75% of Cordero Rojo Mine PM10 emissions come from roadways, but it is unclear 

whether the roads are paved, 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 roads, must be accurately measured. Otherwise the 

calculated emissions will be unreliable for model 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 Cordero Rojo Mine roadway PM10 emissions, since the 

emission rate calculations and associated inputs 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, 11th 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/11thmodconf/presentations/1-5_Proposed_Updates_AERMOD_System.pdf 29 Id. 

30 http://www3.epa.gov/ttn/chief/ap42/ch13/final/c13s0202.pdf. 31 http://www3.epa.gov/ttn/chief/ap42/ch13/final/c13s0201.pdf. 
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In essence, the reviewing public does not know how the Cordero Rojo Mine PM10 emission rates 

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

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

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

associated with roadway and other fugitive PM10 emissions should exclude the Cordero Rojo 

Mine PM10 study for AERMET ADJ_U* or AERMOD LOWWIND3 evaluation purposes. If 

USEPA intends to use the Cordero Rojo Mine PM10 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 PM10 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 volume sources, not area sources.33 This alone would likely improve 

model performance. Furthermore, wind erosion PM10 emissions are not included in the test case 

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

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

Cordero Rojo Mine PM10 study. 

For the above reasons, USEPA should not rely on the Cordero Rojo Mine PM10 study for 

AERMET ADJ_U* or AERMOD LOWWIND3 evaluation and development purposes. 

IV. The AERMET ADJ_U* and AERMOD LOWWIND3 Model Evaluation Analyses 

are not Available for Public Review 

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

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

The reports that describe the studies are publicly available. The modeling evaluations 

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

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

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

This lack of access to the Idaho falls and Oak Ridge 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/aermet_ustar_15181_aermod_15181_lowwind3.zip. 33 http://www3.epa.gov/scram001/reports/Haul_Road_Workgroup-Final_Report_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/dispersion_prefrec.htm#aermod. 
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Moreover, the notice of proposed rulemaking misinforms the public stating that the essential 

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

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

From the notice of proposed rulemaking: 

Model performance evaluation and peer scientific review references for the 

updated AERMOD modeling system are cited, 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 cases 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 documentation, 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 actual 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, and associated data used for the Idaho Falls and 

Oak Ridge evaluation studies, the proposed 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 everything 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 rulemaking public comment period to provide for 

additional input once these essential materials 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/11thmodconf/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/dispersion_prefrec.htm#aermod. 
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USEPA should also make any evaluation studies that they consider, or rely upon, publicly 

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

analysis performed by USEPA. This is particularly 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 Guideline on Air Quality Models 

Current regulatory guidance allows using version 15181 AERMET ADJ_U* and AERMOD 

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

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

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

inputs to verify whether the alternate model performs better than the current regulatory default 

version. 

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

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

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

options to regulatory default status is based 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, alternative model demonstration per Section 3.2.2 of 

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

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

be made fully available to the reviewing public. This information should include all 

meteorological data, model inputs, test data, and all 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 and their consultants may have developed a distorting relation with 

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

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

Regional/State/Local modelers workshops. 
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This collaboration between industry and OAQPS misses 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 allows, 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 11th Conference on Air Quality Modeling, both OAQPS and industry acknowledge this 

mutual collaboration, without mentioning that other stakeholders, with even smaller financial 

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

Bridges at the 11th Conference on Air Quality Modeling: 

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

2 collaboration already from the 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 the 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 proposal 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 things 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 11th 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 11th Conference on Air Quality Modeling, Wednesday, August 12, 2015, p. 12. Available at: 

http://www3.epa.gov/ttn/scram/11thmodconf/presentations/2015_Eleventh_Modeling_Conference-Transcripts_08-

12-2015.pdf. 
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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 conference, "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 some 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 providing 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 may 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 technical reasons, I do not believe that the proposed non-default beta 

AERMET ADJ_U* and AERMOD LOWWIND3 options warrant being upgraded 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 the next presentation, two State modelers came up 

to me and expressed their gratitude that someone 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. Based 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 tend to decrease modeled impacts virtually across-the-board. 

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

to be excluded from the model development 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 exchanges transparent to the general public. 

                                                                  39 Id., p. 242. 

40 Id., p. 245. 
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VII. Concluding Remarks 

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

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

pressure has developed into a worrisome collaborative model development process between 

industry and USEPA. The reviewing public, however, has been excluded from this two-party 

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

evaluations are not even available for the public to review. 

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

that the AERMOD modeling system be modified to include regulatory default AERMET 

ADJ_U* and AERMOD LOWWIND3 options. These studies 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 options often lead to substantial model 

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

AERMOD results to be less than 50% of the monitored 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 case-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 these comments on the Proposed Revision to the 

Guideline on Air Quality Models and the associated 11th Conference on Air Quality Modeling. 

Sincerely, 

 Camille Sears 
Attachment 1: 

Quantile-Quantile Plots for Baldwin EGU Evalua
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 02222

02468

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3

)

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Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

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Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

0

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Lovett 3-hour SO2 (Mod/Obs RHC = 1.25 (n=11), 1.13 

0

200400600800

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Kincaid 3‐hour SO2 (Mod/Obs RHC = 0.76 (n=11), 0.73 

0

200400600800

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Kincaid 3-hour SO2 (Mod/Obs RHC = 0.76 (n=11), 0.73 

0

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Baldwin 3-hour SO2 (Mod/Obs RHC = 1.39 (n=11), 1.28 

0

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Baldwin 1-hour SO2 (Mod/Obs RHC = 1.42 (n=11), 1.42 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 12345

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02468

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02468

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Lovett 3-hour SO2 (Mod/Obs RHC = 1.09 (n=11), 1.06 

0

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0

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Baldwin 3-hour SO2 (Mod/Obs RHC = 1.75 (n=11), 1.44 

0

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Baldwin 1-hour SO2 (Mod/Obs RHC = 1.50 (n=11), 1.56 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 15181

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02468

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Kincaid 3-hour SO2 (Mod/Obs RHC = 0.80 (n=11), 0.71 

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0

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Baldwin 1-hour SO2 (Mod/Obs RHC = 1.49 (n=11), 1.55 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 15181, Beta ADJ_U*

02468

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)

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02468

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0

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0

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)

Observed Concentration (μg/m3)

Kincaid 3-hour SO2 (Mod/Obs RHC = 0.80 (n=11), 0.71 

0

500

10001500200025003000

0 500 1000 1500 2000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Baldwin 3-hour SO2 (Mod/Obs RHC = 1.97 (n=11), 1.59 

0

500

1000150020002500300035004000

0 500 1000 1500 2000 2500 3000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Baldwin 1-hour SO2 (Mod/Obs RHC = 1.66 (n=11), 1.71 

0

500

1000150020002500300035004000

0 500 1000 1500 2000 2500 3000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Baldwin 1-hour SO2 (Mod/Obs RHC = 1.49 (n=11), 1.55 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 15181, Beta ADJ_U*, LOWWIND3 (0.3 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.74 (n=11), 0.84 

0

50

100150200250

0 50 100 150 2

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 3-hour SO2 (Mod/Obs RHC = 1.07 (n=11), 0.97 

0

200400600800

100012001400

0 200 400 600 800 1000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Kincaid 3-hour SO2 (Mod/Obs RHC = 0.80 (n=11), 0.71 

0

500

10001500200025003000

0 500 1000 1500 2000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Baldwin 3-hour SO2 (Mod/Obs RHC = 1.97 (n=11), 1.59 

0

500

1000150020002500300035004000

0 500 1000 1500 2000 2500 3000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Baldwin 1-hour SO2 (Mod/Obs RHC = 1.66 (n=11), 1.71 

0

500

1000150020002500300035004000

0 500 1000 1500 2000 2500 3000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Baldwin 1-hour SO2 (Mod/Obs RHC = 1.37 (n=11), 1.40 
Attachment 2: 

Quantile-Quantile Plots for Kincaid EGU Evalua
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 02222

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

0

50

100150200250

0 50 100 150 2

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 3-hour SO2 (Mod/Obs RHC = 1.25 (n=11), 1.13 

0

200400600800

100012001400

0 200 400 600 800 1000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Kincaid 3‐hour SO2 (Mod/Obs RHC = 0.76 (n=11), 0.73 

0

200400600800

100012001400

0 200 400 600 800 1000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Kincaid 3-hour SO2 (Mod/Obs RHC = 0.76 (n=11), 0.73 

0

500

10001500200025003000

0 500 1000 1500 2000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Kincaid 1-hour SO2 (Mod/Obs RHC = 0.75 (n=11), 0.84 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 12345

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.89 (n=11), 1.12 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.89 (n=11), 1.12 

0

50

100150200250

0 50 100 150 2

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 3-hour SO2 (Mod/Obs RHC = 1.09 (n=11), 1.06 

0

200400600800

100012001400

0 200 400 600 800 1000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Kincaid 3-hour SO2 (Mod/Obs RHC = 0.77 (n=11), 0.74 

0

500

10001500200025003000

0 500 1000 1500 2000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Kincaid 1-hour SO2 (Mod/Obs RHC = 0.75 (n=11), 0.83 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 15181

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.74 (n=11), 0.84 

0

50

100150200250

0 50 100 150 2

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 3-hour SO2 (Mod/Obs RHC = 1.07 (n=11), 0.97 

0

200400600800

100012001400

0 200 400 600 800 1000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Kincaid 3-hour SO2 (Mod/Obs RHC = 0.80 (n=11), 0.71 

0

500

10001500200025003000

0 500 1000 1500 2000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Kincaid 1-hour SO2 (Mod/Obs RHC = 0.68 (n=11), 0.87 

0

500

10001500200025003000

0 500 1000 1500 2000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Kincaid 1-hour SO2 (Mod/Obs RHC = 0.74 (n=11), 0.83 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 15181, Beta ADJ_U*

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.74 (n=11), 0.84 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.66 (n=11), 0.90 

0

50

100150200250

0 50 100 150 2

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 3-hour SO2 (Mod/Obs RHC = 1.09 (n=11), 0.97 

0

200400600800

100012001400

0 200 400 600 800 1000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Kincaid 3-hour SO2 (Mod/Obs RHC = 0.73 (n=11), 0.70 

0

500

10001500200025003000

0 500 1000 1500 2000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Kincaid 1-hour SO2 (Mod/Obs RHC = 0.67 (n=11), 0.76 

0

500

10001500200025003000

0 500 1000 1500 2000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Kincaid 1-hour SO2 (Mod/Obs RHC = 0.76 (n=11), 0.83 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 15181, Beta ADJ_U*, LOWWIND3 (0.3 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.74 (n=11), 0.84 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.66 (n=11), 0.90 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.64 (n=11), 0.74 

0

50

100150200250

0 50 100 150 2

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 3-hour SO2 (Mod/Obs RHC = 1.14 (n=11), 1.13 

0

200400600800

100012001400

0 200 400 600 800 1000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Kincaid 3-hour SO2 (Mod/Obs RHC = 0.77 (n=11), 0.74 

0

500

10001500200025003000

0 500 1000 1500 2000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Kincaid 1-hour SO2 (Mod/Obs RHC = 0.75 (n=11), 0.83 

0

500

10001500200025003000

0 500 1000 1500 2000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Kincaid 1-hour SO2 (Mod/Obs RHC = 0.68 (n=11), 0.72 
Attachment 3: 

Quantile-Quantile Plots for Lovett EGU Evalua
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 02222

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

0

50

100150200250

0 50 100 150 2

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 3-hour SO2 (Mod/Obs RHC = 1.25 (n=11), 1.13 

0

100200300400500600

0 100 200 300 400 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 1-hour SO2 (Mod/Obs RHC = 0.89 (n=11), 0.90 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 12345

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.89 (n=11), 1.12 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.89 (n=11), 1.12 

0

50

100150200250

0 50 100 150 2

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 3-hour SO2 (Mod/Obs RHC = 1.09 (n=11), 1.06 

0

100200300400500600

0 100 200 300 400 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 1-hour SO2 (Mod/Obs RHC = 0.69 (n=11), 0.78 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 15181

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.74 (n=11), 0.84 

0

50

100150200250

0 50 100 150 2

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 3-hour SO2 (Mod/Obs RHC = 1.07 (n=11), 0.97 

0

100200300400500600

0 100 200 300 400 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 1-hour SO2 (Mod/Obs RHC = 0.79 (n=11), 0.77 

0

100200300400500600

0 100 200 300 400 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 1-hour SO2 (Mod/Obs RHC = 0.68 (n=11), 0.77 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 15181, Beta ADJ_U*

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.74 (n=11), 0.84 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.66 (n=11), 0.90 

0

50

100150200250

0 50 100 150 2

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 3-hour SO2 (Mod/Obs RHC = 1.09 (n=11), 0.97 

0

100200300400500600

0 100 200 300 400 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 1-hour SO2 (Mod/Obs RHC = 0.66 (n=11), 0.69 

0

100200300400500600

0 100 200 300 400 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 1-hour SO2 (Mod/Obs RHC = 0.91 (n=11), 0.91 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 15181, Beta ADJ_U*, LOWWIND3 (0.3 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.74 (n=11), 0.84 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.66 (n=11), 0.90 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.64 (n=11), 0.74 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.52 (n=11), 0.53 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.51 (n=11), 0.61 

0

50

100150200250

0 50 100 150 2

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 3-hour SO2 (Mod/Obs RHC = 1.07 (n=11), 1.04 

0

100200300400500600

0 100 200 300 400 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 1-hour SO2 (Mod/Obs RHC = 0.79 (n=11), 0.78 

0

100200300400500600

0 100 200 300 400 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 1-hour SO2 (Mod/Obs RHC = 0.80 (n=11), 0.79 
Attachment 4: 

Quantile-Quantile Plots for Tracy EGU Evalua
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 02222

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 12345

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.89 (n=11), 1.12 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.89 (n=11), 1.12 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.89 (n=11), 1.12 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 15181

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.74 (n=11), 0.84 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.87 (n=11), 1.12 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 15181, Beta ADJ_U*

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.74 (n=11), 0.84 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.66 (n=11), 0.90 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.49 (n=11), 0.53 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 15181, Beta ADJ_U*, LOWWIND3 (0.3 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.74 (n=11), 0.84 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.66 (n=11), 0.90 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.64 (n=11), 0.74 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.39 (n=11), 0.42 
Attachment 5: 

Quantile-Quantile Plots for Prairie Grass Tracer Ev
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 02222

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

0

50

100150200250

0 50 100 150 2

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 3-hour SO2 (Mod/Obs RHC = 1.25 (n=11), 1.13 

0

100200300400500600

0 100 200 300 400 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 1-hour SO2 (Mod/Obs RHC = 0.89 (n=11), 0.90 

0

200000400000600000800000

100000012000001400000

0 200000 400000 600000 800000 1000000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Prairie Grass 1-hour SO2 (Mod/Obs RHC = 1.33 (n=11), 1.19 

0

200000400000600000800000

100000012000001400000

0 200000 400000 600000 800000 1000000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Prairie Grass 1-hour SO2 (Mod/Obs RHC = 1.33 (n=11), 1.19 

0

200000400000600000800000

100000012000001400000

0 200000 400000 600000 800000 1000000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Prairie Grass 1-hour SF6 (Mod/Obs RHC = 1.33 (n=11), 1.19 

0

200000400000600000800000

100000012000001400000

0 200000 400000 600000 800000 1000000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Prairie Grass 1-hour SF6 (Mod/Obs RHC = 1.33 (n=11), 1.19 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 12345

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.89 (n=11), 1.12 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.89 (n=11), 1.12 

0

50

100150200250

0 50 100 150 2

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 3-hour SO2 (Mod/Obs RHC = 1.09 (n=11), 1.06 

0

100200300400500600

0 100 200 300 400 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 1-hour SO2 (Mod/Obs RHC = 0.69 (n=11), 0.78 

0

200000400000600000800000

100000012000001400000

0 200000 400000 600000 800000 1000000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Prairie Grass 1-hour SO2 (Mod/Obs RHC = 1.39 (n=11), 1.16 

0

200000400000600000800000

100000012000001400000

0 200000 400000 600000 800000 1000000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Prairie Grass 1-hour SO2 (Mod/Obs RHC = 1.39 (n=11), 1.16 

0

200000400000600000800000

100000012000001400000

0 200000 400000 600000 800000 1000000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Prairie Grass 1-hour SF6 (Mod/Obs RHC = 1.39 (n=11), 1.16 

0

200000400000600000800000

100000012000001400000

0 200000 400000 600000 800000 1000000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Prairie Grass 1-hour SF6 (Mod/Obs RHC = 1.39 (n=11), 1.16 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 15181

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.74 (n=11), 0.84 

0

50

100150200250

0 50 100 150 2

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 3-hour SO2 (Mod/Obs RHC = 1.07 (n=11), 0.97 

0

100200300400500600

0 100 200 300 400 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 1-hour SO2 (Mod/Obs RHC = 0.79 (n=11), 0.77 

0

200000400000600000800000

100000012000001400000

0 200000 400000 600000 800000 1000000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Prairie Grass 1-hour SF6 (Mod/Obs RHC = 0.62 (n=11), 0.68 

0

200000400000600000800000

100000012000001400000

0 200000 400000 600000 800000 1000000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Prairie Grass 1-hour SF6 (Mod/Obs RHC = 1.36 (n=11), 1.17 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 15181, Beta ADJ_U*

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

0

50

100150200250

0 50 100 150 2

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 3-hour SO2 (Mod/Obs RHC = 1.25 (n=11), 1.13 

0

100200300400500600

0 100 200 300 400 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 1-hour SO2 (Mod/Obs RHC = 0.89 (n=11), 0.90 

0

200000400000600000800000

100000012000001400000

0 200000 400000 600000 800000 1000000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Prairie Grass 1-hour SO2 (Mod/Obs RHC = 1.33 (n=11), 1.19 

0

200000400000600000800000

100000012000001400000

0 200000 400000 600000 800000 1000000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Prairie Grass 1-hour SO2 (Mod/Obs RHC = 1.33 (n=11), 1.19 

0

200000400000600000800000

100000012000001400000

0 200000 400000 600000 800000 1000000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Prairie Grass 1-hour SF6 (Mod/Obs RHC = 1.33 (n=11), 1.19 

0

200000400000600000800000

100000012000001400000

0 200000 400000 600000 800000 1000000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Prairie Grass 1-hour SF6 (Mod/Obs RHC = 1.33 (n=11), 1.19 
Evaluation of 26 highest Modeled and Monitored Co

AERMOD v. 15181, Beta ADJ_U*, LOWWIND3 (0.3 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.91 (n=11), 1.05 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.74 (n=11), 0.84 

02468

1012141618

0 2 4 6 8 10 12 14 

Predicted Concentration(μg/m

3

)

Observed Concentration (μg/m3)

Tracy 1‐Hour SF6 (Mod/Obs RHC = 0.66 (n=11), 0.90 

0

50

100150200250

0 50 100 150 2

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 3-hour SO2 (Mod/Obs RHC = 1.09 (n=11), 0.97 

0

100200300400500600

0 100 200 300 400 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Lovett 1-hour SO2 (Mod/Obs RHC = 0.66 (n=11), 0.69 

0

200000400000600000800000

100000012000001400000

0 200000 400000 600000 800000 1000000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Prairie Grass 1-hour SF6 (Mod/Obs RHC = 1.01 (n=11), 0.95 

0

200000400000600000800000

100000012000001400000

0 200000 400000 600000 800000 1000000 

Predicted Concentration (μg/m

3

)

Observed Concentration (μg/m3)

Prairie Grass 1-hour SF6 (Mod/Obs RHC = 1.00 (n=11), 0.95 
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 ofthe Office of Air and Radiation's Oftice ofAir 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 ofsuch 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 ofthe American Meteorological Society EPA Regulatory Model

("AERMOD") throughout its history. I currently serve as co-chair ofthe AMS/EPA

Regulatory Model Improvement Committee (AERMIC) consisting of atmospheric scientists

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

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

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

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

of Part 51 ofthe Code of Federal Regulations.

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

NAAQS") requires that the three year average ofthe annual 99th percentile ofthe 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 ofthe 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 ofthe 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 ofthese

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 ofthe annual 99th percentile ofdaily

maximum I-hour average concentration of 802 can be readily calculated or otherwise

reasonably approximated.

3. As part ofthe 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 ofthe 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) ("EPA 2003"). The scope ofthe 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 ofthe 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 ofthe 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 ofthe field studies used to evaluate AERMOD model perfonnance involved ambient

sampling of S02 for a period ofone 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 ofthe 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 ofthe AERMOD model for estimating l-hour ambient concentrations iswell~

documented and the form ofthe 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 ofdays 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 ofmodels

is suitable for one type ofform 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 99th percentile) or exceeding days can be determined. In fact, the percentile

form ofthe i-hour S02 NAAQS is a more "stable" metric than a standard based on the 1st_

highest or 2

nd-highest concentrations, since the potential impact of "'outliersH

in the

distribution is mitigated, especially when the multi-year average aspect ofthe 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 ofthe 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 ifsources 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 substitutedf' 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 ofthe AERMOD

dispersion model to support implementation ofthe 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 ofmodel-predicted ambient concentrations to

ambient 802 concentrations reported for 2008 at local EPA monitoring stations in the

vicinity ofthe 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 ofissues involved in such comparisons. A number offactors can affect the

comparison of a modeled concentration with a monitored concentration, including the

accuracy ofthe emission rate and other source characteristics input to the model, the

representativeness ofthe meteorological data input to the model, and the influence oflocal

geographical features and land use characteristics on the transport and dispersion ofthe

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 ofthe plume, Slight errorsin 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

ofthe 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

ofmeteorological 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 ofdetermining whether the AERl\rl0D

model itself is biased.

9. The declaration ofRobert J. Paine addresses practical issues in applying the AERMOD

model that allegedly arise due to the fonn ofthe I-hour S02 standard, as well as concerns

regarding the conservatism ofthe 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 ofthe unique statistical foml ofthat NAAQS.'~

Paine Ded. 110.: "The form ofthe 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 ofthis 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 ofthe individual source being

permitted, nearby sources and regional background."

Response: As stated in paragraph 2 above, all ofthese metrics are readily obtainable from model

outputs. Although the existing version ofAERMOD 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 ofthe 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 ofthe modeling simulation."

Response: EPA issued guidance on a range ofissues 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 ofthe 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 oftemporal pairing ofmodeled 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 99th 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 ofthe accuracy ofAERMOD 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 ofform.

(d)

Paine Dec!. , 12. : "Modeling of peak 802 emissions as ifthey occur continuously is a

distortion ofreality 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 ofdispersion modeling in the context ofthe PSD permitting program is

to demonstrate that the proposed new or modified emissions will not cause or contribute to

violations ofthe standard ifthe 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 ofthe dispute as to

use of allowable or actual emissions~ and does not relate to the issue ofmodel 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 ofthe 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 ofthe 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 ofsuch 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 ofAppendix W suggests that '"the number ofsuch 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 ofthe background impacts between the components ofthe "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

ofmodeled and monitored impacts.

(g)

Paine Decl. , 14.: "The distribution oftotal 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 of2 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 offictitious 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 ofthe I-hour N02 standard is very similar to the tornl ofthe 1M

hour 802 standard, the role ofNOx chemistry in modeling ambient N02 impacts associated with

NOx emissions makes it difficult to draw comparisons between the two standards in terms ofthe

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 ofNO 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 ofthe 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. EPA, 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 ofthe I-hour 802

NAAQS."

Response: Recognizing the importance ofthe 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 ofthe l-hour S02 NAAQS, ifit can be

demonstrated that the proposed emission increases do not contribute significantly to those

modeled violations, paired in time and space. Although the form ofthe I-hour S02 standard

may complicate the '"bookkeeping" needed to make such a demonstration, the principle ofthe

significant contribution test based on the SIL has not changed under the I-hour S02 NAAQS.

13
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

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