March 7, 2012

Mr. Gerardo Rios Chief, Permits Office U.S. EPA Region 9

75 Hawthorne Street San Francisco, CA 94105

Subject: Pio Pico Energy Ce nter PSD Permit Application

Dear Mr. Rios:

As requested by EPA during the February 13, 2012 meeting between representatives of EPA and Pio Pico Energy Center, LLC (Applicant), we are submitting this additional information on behalf of Applicant. EPA al so requested that the Applicant provide the modeling files that support the Class I Incr ement Analysis; those are provided on the

enclosed CD.

It should be noted that the Applicant subm itted the proposed modeling protocol for the Pio Pico Energy Center (Proje ct) to EPA on December 1, 2010, with a request for review and comment, consistent with EPA’s polic y encouraging early consultation on modeling

issues. 1 EPA did not respond to Appl icant’s request. In the ab sence of any questions or concern expressed by EPA regarding the prot ocol, Applicant proceeded with modeling and analysis consistent with the protocol, and has expended consider able time and effort in reliance on EPA’s tacit approval. 2

It should also be noted that Applicant s ubmitted the PSD permit application on April 1, 2011. The original PSD permit application in cluded an air quality analysis, and the applicant sought guidance from EPA on selec tion of nearby sources to include in the cumulative impact modeling. The cumulative impact modeling was submitted on July 6,

2011.

1 Appendix W, Section 10.2.1: “[e]very effort should be made by the Regional Office to meet with all parties involved in a SIP revision or a PSD permit a pplication prior to the start of any work on such a project. During this meeting, a protocol should be established between the preparing and reviewing parties to define the procedures to be followed, the data to be collected, the model to be used, and the analysis of

the source and concentration data.”

2 It should also be noted that during the December 9 meeting, EPA staff indicated that any additional concerns regarding modeling issues would be documented in a letter to Applicant on or before December 16, 2011; however , to date, no such letter has been received. In addition, several calls have been placed to EPA modeling staff to confirm that any remain ing issues have been resolved, but no calls have been returned. Applicant has ther efore proceeded on the assumption th at EPA’s requests for additional information about modeling issues have been fully addressed. 

## sierra research

1801 J Street Sacramento, CA 95811 Tel: (916) 444-6666 Fax: (916) 444-8373

Ann Arbor, MI Tel: (734) 761-6666 Fax: (734) 761-6755Gerardo Rios -2- March 7, 2012

At the February 13, 2012 meeting, EPA staff asked for more information about modeling parameters. Some of these parameters we re first presented to EPA in the July 2011 modeling submittal whereas other parameters we re presented to EPA in April 2011as part of the original PSD permit submittal.

Federal regulations require EP A staff to attempt to determine whether a PSD application is complete within 30 days of submittal. 3 While this is not an enforceable requirement, it represents EPA policy at its highest and most public leve l. EPA’s long delays in responding to submittals, the incremental and sh ifting nature of its requests for additional information (this is EPA’s fourth different request for modeling information), and its failure to provide guidance that would allo w an applicant to provide a complete and satisfactory response, are not consistent with the spirit represented by this regulation.



### SF 6 

### Emissions

Comment: Please estimate SF 6 emissions from circuit breakers at the project.

Response: Sulfur hexafluoride (SF6) will be used as an insulating medium in three 230 kV switchyard breakers and in two generator ci rcuit breakers (GCB). Estimates of the SF6 contained in a 230 kV breaker range from 161 to 208 lbs, depending on the manufacturer.

The GCBs will each contain 24.2 lb of SF6. Th e IEC standard for SF6 leakage is less than 0.5%; the NEMA leakage standard for new circuit breakers is 0.1%. A maximum leakage rate of 0.5% pe r year is assumed.

Type of

Breaker Number of

Breakers lb SF 6 per breaker Maximum Leakage Rate, % Maximum SF 6 Emissions, lb/yr

Switchyard 3 208 0.5% 3.12

Generator 2 24.2 0.5% 0.24

Total 3.36

Based on a global warming potential of 23,900 4 , the maximum SF 6 emissions from facility circuit br eakers will not exceed 40.2 tons/yr of CO 2 e.



### Wind Roses

3 40 CFR 124.3 (c): “The Regional Administrator shall review for completeness every application for an EPA-issued permit. Each application for an EP A-issued permit submitted by a new HWM facility, a new UIC injection well, a major PSD stationary source or major PSD modification, or an NPDES new source or NPDES new discharger should be reviewed for completene ss by the Regional Administrator within 30 days of its receipt . Each application for an EPA-issued permit submitted by an existing HWM facility (both Parts A and B of the application), exis ting injection well or existing NPDES source or sludgeonly facility should be reviewed for completeness within 60 days of receipt. Upon completing the review, the Regional Administrator shall notify the applicant in writing whether the application is complete. If the application is incomplete, the Regional Administrator shall list the information necessary to make the application complete.” (emphasis added).

4 40 CFR 98 Subpart A, Table A-1.Gerardo Rios -3- March 7, 2012

Comment: Please provide new wind rose gr aphics based on the definition of “calm” periods used in the modeling.

Response: In order to reduce the amount of time wh ere wind speed is treated as “calm,” the San Diego Air Pollution Cont rol District (SDAPCD or Di strict) defines wind speeds below 0.45 m/sec as “calm” (standard definition is 0.5 m/sec). Charts were provided in the PSD application that graphically summa rize quarterly and annua l distributions of wind speed and direction (“wind roses”). Th e wind roses that were provided in the application used the standard cutoff for cal m periods. EPA requested that Applicant provide wind roses that use the same cuto ff used by the Applicant, following District guidance, in the modeling; the re quested wind roses are attached.



### Summary of 1-hour NO 2 

### NAAQS Compliance Calculation

Comment: Please show the calculations and relevant data used to demonstrate compliance with the 1-hour NO 2 NAAQS.

Response: The requested calculations are presented in Table 1.

Table 1

One-hr NO 2 NAAQS Compliance Calculation (Receptor 8164 a )

Year 2004 2005 2006 2007 2008 5-year Average

Date March 7 Oct. 19 Dec. 14 March 23 Oct. 14

Hour 21 3 18 23 19

Cumulative Impact (μg/m3) 175.5 170.5 193.9 172.9 184.4 179.4

Regional Background (μg/m3) 125.94 78.95 107.14 101.50 110.90

Project Impact (μg/m3) 37.36 62.05 67.03 20.01 36.55

a Receptor with the high est 5-year average 98 th percentile of the daily maximum 1-hour cumulative impact (monitor data plus modeled impact from project and non-project nearby sources).



### Isopleth Maps of 5-Year Averages of 98 th 

### Percentile Concentrations

Comment: Please provide isopleths m aps showing the 5-year averages of 98 th percentile concentrations for both PM 2.5 and NO 2

.

Response: The requested isopleths maps ar e provided as Figures 1 and 2.Gerardo Rios -4- March 7, 2012Figure 1. Five-Year Averages of 98thPercentile Maximum Daily One-hour NO2Concentrations(Measured Background plus Modeled Project and Nearby Sources)Gerardo Rios -5- March 7, 2012Figure 2. Five-Year Averages of 98thPercentile Maximum 24-hour PM2.5Concentrations(Measured Background plus Modeled Project and Nearby Sources)Gerardo Rios -6- March 7, 2012



### Justification of Use of PVMRM

Comment: On December 1, 2011, at EPA’s request, the applicant submitted additional justification for the use of the PVMRM Tier 3 non-regulatory default option. EPA specifically requested more information on prongs 1 and 4 of the 5-prong demonstration under Appendix W Section 3.2.2. Now EPA requests more information on prongs 2, 3, and 5.

Section 3.2.2.e of Subpart 51, Appendix W states:

e. Finally, for condition (3) in paragraph (b) of this subsection, an alternative refined model may be used provided that:

i. The model has received a scientific peer review;

ii. The model can be demonstrated to be applicable to the problem on a theoretical basis; iii. The data bases which are necessary to perform the analysis are available and adequate; iv. Appropriate performance evaluations of the model have shown that the model is not biased toward underestimates; and v. A protocol on methods and procedures to be followed has been established.

Applicant provided information supporting the us e of PVMRM in its in itial PSD application submitted in April 2011. At EPA’s request, A pplicant provided supplemental information on Prongs 1 and 4 in its December 1, 2011 submittal. Additional information is provided below with regard to the other three prongs.

Prong 2. The model can be demonstrated to be a pplicable to the probl em on a theoretical

basis.

AERMOD without PVMRM (or OLM) is a disp ersion model. It predicts ground-level

concentrations based on distribut ion of emissions from the s ources being modeled. However, the NOx emitted at the stack is a mixture of (primarily) NO and NO 2 . If conversion of stack

NO to NO 2 in the atmosphere is not taken into account, the model will significantly underpredict NO 2 impacts. Conversely, if the unrealis tic assumption is made that 100% of stack NO is converted to ground-level NO 2 at all receptors, regard less of transport time, transport distance, or ambient ozone concentr ations, the dispersion model will significantly overpredict NO 2 impacts. Conservative assumptions are initially used in compliance screening methodologies in order to assure compliance. As the compliance evaluation methodology becomes more sophisticated, the greater accuracy co ntinues to assure compliance while allowing more realistic (le ss conservative) assumptions. EPA addresses this process, in the context of the 1-hour NO 2 standard, through increasingly sophisticated (and less conservative) tiers of analysis.Gerardo Rios -7- March 7, 2012

The regulatory default modeling methodologies for the 1-hour NO 2 standard are screening methodologies. 5

Tier 1: Assume full conversion of NO to NO 2 based on application of an appropriate refined modeling technique under Section 4.2.2 of Appendix W to

estimate ambient NOx concentrations.

Tier 2: Multiply Tier 1 result by empirically derived NO 2 /NOx ratio, with 0.75 as

the annual national default ratio.

Tier 3: Detailed screenin g methods may be considered on a case-by-case basis, with the Ozone Limiting Method (OLM) identified as a detailed screening technique for point sources. PVMRM is also considered by EPA to be in this category at this time. 6

Under EPA’s interim guidance for eval uating compliance with the 1-hour NO 2 NAAQS, the Significant Impact Level (SIL)-equivalent is so low that almost any moderate-to-large

combustion source will have di fficulty passing a screening asse ssment. It is therefore necessary to use more sophisticated analytical tools, as provided for in EPA guidance. One such tool identified by EPA, and selected fo r use with the Project, is the PVMRM option

within AERMOD.

AERMOD-PVMRM has been tested specifica lly for its ability to compute unbiased results. One of the key studies was conduc ted by a senior modeler currently in OAQPS at EPA, and conducted on large natural gas-fired power plants. 7 The study concluded the following: “Based on all of the data available, the AERMOD-PVMRM algorithm is judged to provide unbiased estimates of the NO 2 /NOx ratio based on criteria that are comparable to, or more rigorous than, evalua tions performed for other dispersion models that are judged to be refined.”

In its March 1, 2011 guidance, EPA states:

“We believe that these additional model eval uation results lend further credence to the use of these Tier 3 options in AERMOD for estimating hourly NO2 concentrations, and we recommend that their use should be generally accepted provided some

reasonable demonstration can be made of the appropriateness of the key inputs for these options, the in-stack NO2/NOx ratio and the background ozone concentrations.” 8

For these reasons, use of the AERMOD-PVMRM algor ithm is applicable to a refined analysis of compliance with the 1-hour average NO 2 impacts from the Project on a theoretical basis.

5 EPA, Appendix W Section 5.2.4. See also Memorandum from Wood to Regional Air Division Directors

, General Guidance for Implementing the 1-hour NO2 Standard in Prevention of Significant Deterioration

Permits , June 28, 2010, p.14.

6 Memorandum from Wood to Regional Air Division Directors, Applicability of Appendix W Modeling Guidance for the 1-hour NO2 NAAQS , June 28, 2010, p.2.

7 Brode, Roger W. Final Report, Evaluation of Bias in AERMOD-PVMRM , MACTEC report on Alaska Department of Environmental Conservation Contract No. 18-9010-12, June 2005.

8 Memorandum from Fox to Regional Air Division Directors, Additional Clarification Regarding Application of Appendix W Modeling Guidance for the 1-hour NO2 NAAQS , March 1, 2011, p. 6.Gerardo Rios -8- March 7, 2012

Prong 3. The data bases which are necessary to perform the analysis are available and adequate.

The additional data needed to perform the PV MRM analysis (i.e., ambient ozone data) were collected from the Chula Vista monitoring station, which is the same station that provided the

ambient NO 2 data.

The justification for use of background air quality data from the Chula Vista monitoring station was presented in the November 2010 mode ling protocol at pp. 6-7. As discussed in the monitoring protocol, the Chula Vista monito ring station was selected by the San Diego APCD as the monitoring location most re presentative of the project site.

The ozone data used in the PVMRM analysis were provided by the District. The hourly ozone data was 6.33% incomplete before data substitution. 9 The data substitution procedure described in the December 1, 2011 submittal was used to fill any gaps.

In-stack NO 2 /NO

x ratios for the project turbines are based on source test data from similar units, provided by the District (and described in the January 5, 2012 submittal). In-stack

NO 2 /NO

x ratios for nearby sources were based on Di strict source test da ta, provided by the District (and also described in the January 5, 2012 submittal).

Prong 5. A protocol on methods and procedures to be followed has been established.

Applicant submitted the proposed modeling protocol for the Pio Pico Energy Center (Project) to EPA on December 1, 2010, with a request for review and comment, consistent with EPA’s policy encouraging early consultation on m odeling issues. EPA did not respond to Applicant’s request. In the absence of any que stions or concern expressed by EPA regarding the protocol, Applicant proceeded with modeling and analysis consistent with the protocol, and has expended considerable time and effort in reliance on EPA’s tacit approval.

In March 2011, EPA published additi onal modeling guidance for 1-hour NO 2 compliance

demonstrations. 10 The modeling protocol submitted in December 2010 is consistent with that guidance. The AQIA submitted as part of the PSD application is also consistent with that guidance.

EPA did provide some guidance in May 2011 re garding selection of nearby sources for cumulative impact analysis for this project. EPA did not at that time express any concern regarding use of PVMRM for this Project or seek additional justification for such use.

Conclusion

9 2004, 529 missing hours out of all 8784 hours / 6.02%; 2005, 549 missing hours out of all 8760 hours / 6.27%; 2006, 483 missing hours out of all 8760 hours / 5.51%; 2007, 634 missing hours out of all 8760 hours / 7.34%; 2008, 571 missing hours out of all 8784 hours / 6.50%;

10 Memorandum from Fox to Regional Air Division Directors, Additional Clarification Regarding Application of Appendix W Modeling Guidance for the 1-hour NO2 NAAQS , March 1, 2011.Gerardo Rios -9- March 7, 2012

Based upon the information provided above and in previous submittals to EPA, Applicant

believes that the use of PVMRM for the dem onstration of compliance with the 1-hour NO

x NAAQS meets the criteria sp ecified in Section 3.2.2.e of Subpart 51, Appendix W.



### Surface Roughness

Please provide a more detailed (by sector) co mparison of the surface roughness at the project site with the surface roughness at the site providing the meteorological data.

Otay Mesa Meteorological Station

Response: The surface roughness values used in th e PPEC PSD permit application were developed by the SDAPCD, and represent th e surface roughness surrounding the site where meteorological data were collected. The Di strict followed EPA’s “AERMOD Implementation Guide” (2008 version) in using EPA’s AERS URFACE processor with the National Land

Cover Data 1992 archive to determine surf ace characteristics for AERMET (Class II Modeling Protocol pp.2-9 to 2-14).

AERSURFACE uses a Land Use data base fr om 1992, and does not take buildings into

account. The District reviewed aer ial photos for the area, which s how that the vicinity of the Otay Mesa meteorological towe r is surrounded by a light industr ial and residential area that

includes northern Mexico and the U.S border ar ea. Using this information, the District adjusted the surface roughness f actor from the value of approximately 0.2 meters calculated by AERSURFACE to 0.7 meters to more accura tely represent the current terrain and structures surrounding the Otay Mesa meteorological monitori ng station location (see Figure 3). The District’s adjustment is supported by AERSURFACE guidance 11 and the scientific

literature. 12 EPA’s AERSURFACE guidance assigns an even higher surface roughness of 0.8 m to commercial/industrial/transp ortation areas (Class 23) not locat ed at an airport. Stull’s scientific textbook on boundary layer meteorology, which is a scientific reference for Table

A-3 in EPA’s AERSURFACE User’s Gu ide, gives a surface roughness range of approximately 0.7 m to 1.3 m for “Cen ters of large to wns and cities.”

SDAPCD performed a qualitative review of the surface roughness values used by the Applicant (per instructions by the District) to ensure that they were reasonably representative of the project site. It di d this by examining the 10 km by 10 km domain centered on the project site, and determined that the data in the two tables are r easonably represen tative of the

domain as a whole. This conclusion is not surprising, because both locations are located within that 10 km by 10 km domain.

11 EPA. AERSURFACE User’s Guide, Table A-3 – Seasonal Values of Su rface Roughness (m) for the NLCD92 21-Land Cover Classification System, page A-5, January 2008.

12 Stull, R.B., An Introduction to Boundary Layer Meteorology, Figure 9.6 -Aerodynamic roughness lengths for typical terrain types, page 380, 1988.Gerardo Rios -10- March 7, 2012

Figure 3. Otay Mesa Meteorological Station and Surrounding Area

EPA has requested additional detail charac terizing the surface roughness surrounding the project site by sector. A pplicant has characterized th e surface roughness as follows:

 Twelve 30-degree land use sectors

 Annual results by sector

 1 kilometer radius

 Seasons defined as follows

o October-February: Late autumn after frost and harvest

o March-April: Transitional spring (par tial green coverage, short annuals)

o May-September: Midsumme r with lush vegetationGerardo Rios -11- March 7, 2012

The results for the PPEC project site from AE RSURFACE, along with the District’s revised values for the location of the meteorologica l monitoring station, are shown in Table 2.

Table 2 Comparison of Surface Roug hness at PPEC Stack Location with Otay Mesa Meteorological Station (meters)

Sector PPEC Otay Mesa Met Station (adjusted by SDAPCD)

1 0.2 0.7

2 0.2 0.7

3 0.2 0.7

4 0.2 0.7

5 0.2 0.7

6 0.2 0.7

7 0.1 0.7

8 0.2 0.7

9 0.2 0.7

10 0.1 0.7

11 0.2 0.7

12 0.2 0.7

In contrast to the area surr ounding the Otay Mesa meteorolog ical station, the area surrounding the project site is not surrounde d by buildings. However, ther e are significant structures adjacent to the project site, including the co mbustion gas turbines, heat recovery steam generators, dry cooling tower and administrati on building for the Otay Mesa Energy Center located directly east of the Projec t at distances be tween 0.1 and 0.6 km.

The information provided in the Applicant’s De cember 1, 2011 letter demonstrates that the albedo and Bowen ratio values for the projec t and meteorological st ation sites are very

similar.

The importance of surface roughness in the AERMOD dispersion model is to properly interpret the meteorological observations at the monitoring site. The surface roughness around the meteorologic al station is used

, 13 along with monitored wind speeds, directions, and temperatures by AERMOD’s meteorological processor, AE RMET, “in determining the magnitude of mechanical turbulence a nd the stability of the boundary layer” 14 being monitored. It is only after the monitored mete orological data have b een properly interpreted that the data can be used by AERMOD to co rrectly simulate air dispersion from various project sites within reasonable proximity to the meteorological monitoring stations.

13 EPA. AERSURFACE User’s Guide , page 6, January 2008.

14 EPA. AERSURFACE User’s Guide , page 1, January 2008.Gerardo Rios -12- March 7, 2012

Surface roughness is therefore used by AERMOD to properly interpret airflow measurements at the monitoring site and apply th em to the dispersion calculations.

Assessment of the representativeness of the Otay Mesa meteorological data

EPA guidance describes the factors that should be taken into account in assessing siting of meteorological monitors. 15 Although this guidance is principa lly useful in determining where to site a new monitor, it is also helpful in assessing the adequacy of existing data.

The factors recommended for consid eration in monitor siting are:

 Proximity

 Height of Measurement

 Boundary Layer Profile Considerations

 Surface Characteristics

The goal of the analysis is to determine whet her the proposed monitoring data is adequately representative of th e area of interest. 16 Because the preferred regulatory dispersion model (AERMOD) is a steady state model, one set of meteorological conditions must be selected to represent the entire modeling dom ain. The determination of wh ether data are adequate for this purpose is a qualitative evaluation that requires the ap plication of experience and judgment.

Factor 1: Proximity.

“In general, the representativeness of the me teorological data used in an air quality modeling analysis is dependent on the proxim ity of the meteorological monitoring site

to the “area-of-interest”. 17

“In some instances, even though meteorological data are acquired at the location of the pollutant source, they may not correctly characterize the important atmospheric dispersion conditions; e.g., dispersion conditi ons affecting sources located on the coast are strongly affected by off-shore air/sea boundary conditions - data collected at the source would not always reflect these conditions.” 18

The Otay Mesa meteorological station is very cl ose to the project site—3.2 km. Both the site and the station are approximately the same di stance from the Pacific Ocean, and there are no significant nearby bodies of water to affect dispersion conditi ons at either site. There are no intervening terrain features be tween the project site and th e meteorological station. The principal factor for assessi ng representativeness of the me teorological data, proximity, therefore weighs strongly in favor of the Otay Mesa data.

Factor 2: Height of Measurement.

15 EPA, Meteorological Monitoring Guidance for Regulatory Modeling Applications . p. 3-1

16 This is not the same as the project site. See the discussion of Factor 1 (Proximity) below.

17 EPA, Meteorological Monitoring Guidance for Regulatory Modeling Applications . p. 3-3

18 EPA, Meteorological Monitoring Guidance for Regulatory Modeling Applications . p. 3-3Gerardo Rios -13- March 7, 2012

“Representativeness is a func tion of the height of the measurement. For example, one can expect more site-to-site variability in measurements taken close to the surface compared to measurements taken aloft. As a consequence, upper-air measurements are generally representative of much larg er spatial domains then are surface

measurements.” 19

Measurements at the Otay Mesa meteorological st ation are made at a height of 10 m, which is the standard measurement height for a permanen t NWS station of the type routinely accepted by EPA. This factor supports the use of the Otay Mesa data.

Factor 3: Boundary Layer Profile Considerations.

“Where appropriate, data representativene ss should be viewed in terms of the appropriateness of the data for constructing realistic boundary layer profiles and three dimensional meteorological fi elds. Factors that should be considered in selecting a monitoring site in complex terrain include: th e aspect ratio and slope of the terrain, the ratios of terrain height to stack height and plume height, the distance of the source from the terrain feature, and the effects of terrain features on meteorological conditions, especially wind speed and wind direction.” 20

Factor 3A: Aspect ratio and slope of the terrain.

The aspect ratio and slope of the terrain surr ounding the meteorological station is flat, sloping slightly upwards to the east. The aspect ratio an d slope of terrain to the south and west of the project site is the same as for the meteorologi cal station. However, th e terrain rises to the north and east of the project site. As a result , wind speed and direction data taken at the project site would be, to some extent, affected by the terrain. Meteorological data taken at the site would be expected to be less representative of the area of interest than data collected at the Otay Mesa station, which is less affected by the terrain. This factor supports the use of the Otay Mesa data.

Factor 3B: Ratios of terrain height to stack height and plume height

The terrain to the east of the pr oject site rises to more than a hundred meters above the stack within two kilometers. However, the plume he ight is above the terrain height, so this subfactor is not a significan t factor in assessing mete orological monitor location.

Factor 3C: Distance of the source from the terrain feature

The nearest terrain feature that could affect me teorological conditions is the elevated terrain

of the San Ysidro Mountains to the east of both the project site and the meteorological station. The project site is approximately 1.5 km from the nearest ridge. The meteorological station is approximately 4.5 km from the same ridge, wh ich is the closest te rrain feature to the meteorological station. This factor suppor ts the use of the Otay Mesa data.

19 EPA, Meteorological Monitoring Guidance for Regulatory Modeling Applications . p. 3-3

20 EPA, Meteorological Monitoring Guidance for Regulatory Modeling Applications . p. 3-3Gerardo Rios -14- March 7, 2012

Factor 3D: Effects of terrain features on meteorological conditions, especially wind speed

and wind direction.

The most important consideration in siting a wind direction sensor in complex terrain is that

the measured direction should not be biased in a particular direction that is not experienced by the pollutant plume.

The same large-scale topographic features located to the east and north that influence the meteorological data monitoring station also infl uence the proposed proj ect site in the same manner. This factor supports the use of the Otay Mesa data.

Factor 4: Surface Characteristics

“In steady-state modeling applications, one typically focuses on the meteorological conditions at the release height of the source or sources, or the plume height in the case of buoyant sources. Representativene ss for steady-state modeling applications must necessarily be assessed in concer t with the steady-state assumption that meteorological conditions are constant within the space-time domain of the application; as typically a pplied, measurements for a si ngle location, somewhere near the source, are assumed to apply, without change, at all points in the modeling domain. Consistency would call for site selection criteria consistent with the steady state assumption; i.e., to the extent possible, sites should perhaps be selected such that factors which cause spatial variations in meteorological conditions , are invariant over the spatial domain of the application, what ever that might be. Such factors would include surface characteristics such as gr ound cover, surface roughness, the presence or absence of water bodies, etc. Similarl y, the representativeness of existing thirdparty data bases should be judged, in part, by comparing the surface characteristics in the vicinity of the meteorol ogical monitoring site with th e surface characteristics that generally describe the analysis domain.” 21

The presence of buildings surrounding the Otay Mesa station would affect the data (the increased surface roughness of the urban setting would be expected to reduce windspeeds). AERMOD takes this effect into account by ad justing for surface roughness at the monitoring site. When the analysis domain is viewed as a whole, this factor suppo rts the use of the Otay

Mesa data.

Conclusion

The single most important factor in determini ng representativeness, pr oximity to the source, strongly supports a determination that the Otay Mesa meteorological data are representative. Other factors that support this determination are the similar dist ances to major terrain features, including the nearby mountains and the ocean.

In conclusion, the Applicant belie ves that the use of five year s of meteorological data from the Otay Mesa station is fully consistent with EPA guidance, is fully consistent with EPA past practice, and is adequately repr esentative of the Project area.

21 EPA, Meteorological Monitoring Guidance for Regulatory Modeling Applications . p. 3-2Gerardo Rios -15- March 7, 2012

If you have any questions regarding this in formation, please contact the Applicant’s representative David Jenkins at (317) 431-1004, or Gary Rubens tein or me at (916) 444-6666.

Sincerely,

Steve Hill

cc: John McKinsey, Stoel Rives LLP David Jenkins, Apex Power Group Steve Moore, San Diego Ai r Pollution Control District

Attachments

Enclosure: CDOtay Mesa – Annual, 2004Otay Mesa – 1 st Quarter, 2004Otay Mesa – 2 nd Quarter, 2004Otay Mesa – 3 rd Quarter, 2004Otay Mesa – 4 th Quarter, 2004Otay Mesa – Annual, 2005Otay Mesa – 1 st Quarter, 2005Otay Mesa – 2 nd Quarter, 2005Otay Mesa – 3 rd Quarter, 2005Otay Mesa – 4 th Quarter, 2005Otay Mesa – Annual, 2006Otay Mesa – 1 st Quarter, 2006Otay Mesa – 2 nd Quarter, 2006Otay Mesa – 3 rd Quarter, 2006Otay Mesa – 4 th Quarter, 2006Otay Mesa – Annual, 2007Otay Mesa – 1 st Quarter, 2007Otay Mesa – 2 nd Quarter, 2007Otay Mesa – 3 rd Quarter, 2007Otay Mesa – 4 th Quarter, 2007Otay Mesa – Annual, 2008Otay Mesa – 1 st Quarter, 2008Otay Mesa – 2 nd Quarter, 2008Otay Mesa – 3 rd Quarter, 2008Otay Mesa – 4 th Quarter, 2008