Testimony of Dr. Gi-Dong Kim

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STATE OF NEW MEXICO

BEFORE THE ENVIRONMENTAL IMPROVEMENT BOARD

IN THE MATTER OF PROPOSED REVISIONS

TO THE STATE IMPLEMENTATION PLAN

FOR THE SAN JUAN GENERATING STATION

BEST AVAILABLE RETROFIT TECHNOLOGY

DETERMINATION 

No. EIB 13- 02(R)

_____________________________________________________________________________

NMED EXHIBIT 12 – WRITTEN TESTMONY OF 

Dr. GI-DONG KIM

______________________________________________________________________________

Statement of Witness Qualifications

Gi-Dong Kim is a Modeling Specialist in the Modeling Unit of the Air Quality Bureau’s 

Planning and Policy Section, where he has worked for eleven years. Dr. Kim holds a Ph.D. and 

a Master of Science Degree in Environmental Engineering from the University of Tennessee. 

His responsibilities with the Bureau include the review and performance of computer-based 

dispersion modeling in support of permitting, enforcement and regulation development actions.

Dr. Kim has performed approximately 154 modeling analyses for the Bureau. He was 

responsible for reviewing the dispersion modeling analysis performed by PNM throughout the 

BART determination process. His resume is attached as exhibit 12a.

Testimony

Introduction

1 The major steps involved in performing this visibility modeling analysis were to:

2 o Determine natural visibility conditions as a baseline visibility condition;

3 o Evaluate the emission rates of the pollutants that deteriorate visibility;

4 o Run CALMET meteorology model with 2001 year of meteorological data;

5 o Run CALPUFF model with the emission rates and the output of CALMET model;

6 o Run CALSUM post-processor;

7 o Run CALPOST post-processor to perform visibility calculations; and

8 o Repeat all the procedures mentioned above with 2002 and 2003 years of 

9 meteorological data.

10

11 Natural visibility (or background) conditions are used as a reference for determination of 

12 the impact of the facility sources. More specifically, CALPOST, which is a post-processor of the 
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1 CALPUFF modeling system, calculates delta deciviews values that are the difference between 

2 deciviews including the impact of the facility sources and the natural visibility condition and 

3 deciviews of the natural visibility condition alone. From among these daily values for each year 

modeled, the value of the 98th percentile (approximately equivalent to the 8th 4 highest value (or 

5 day)) is used for comparing the effects of each scenario in improving visibility.

6 The governing requirements for this analysis are found in 40 C.F.R. Part 51 Appendix Y, 

7 EPA BART Guidelines, and the Western Regional Air Partnership (WRAP) BART modeling 

8 guidelines developed by a collaborative effort of tribal governments, state governments and 

9 various federal agencies to comply with EPA’s regional haze regulations.

10 My role for this project was to make sure that PNM’s visibility modeling analysis was 

11 consistent with the BART Modeling Guidelines and then evaluate the visibility improvement of 

12 each NOx control technology, using PNM’s visibility modeling results. 

13 PNM’s Modeling Submissions

14 In 2007, PNM provided NMED with all the modeling results performed on a year-to-year 

15 basis for the facility-wide impact for each control scenario. PNM modeled visibility impacts of 

16 nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matter (PM), which are the visibility 

17 impairing pollutants emitted by the facility. PNM submitted updated modeling analyses 

18 performed with revised emission rates and different ammonia background concentrations several 

19 times between 2007 and 2012, using the same 2007 modeling approach. PNM submitted the 

20 most updated modeling analysis in 2013.

21 The 2013 modeling was performed with the revised emission rates for Sulfur Dioxide 

22 (SO2), Sulfuric Acid (H2SO4), and Total Particulate Matter (TPM) and available NOx control 

23 technologies including selective catalytic reduction (SCR), selective non-catalytic reduction 
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1 (SNCR), and also considering unit retirements as an alternative operating scenario that had not 

2 been discussed previously. More specifically, the 2013 modeling analysis incorporated the new 

3 SO2 emission rate of 0.15 lb/MMBtu, total particulate matter (TPM) emission rate of 0.034 

4 lb/MMBtu, and H2SO4 emission rates of 0.00026 lb/MMBtu and 0.000046 lb/MMBtu for SCR 

5 and non-SCR, respectively, from the current NSR permit dated August 31, 2012. These new 

6 emission limits have been incorporated into the Baseline modeling scenario and each NOx 

7 control scenario, including EPA’s Federal Implementation Plan (FIP) scenario representing four 

8 SCRs, and NMED’s 2011 State Implementation Plan (SIP) scenario representing four SNCRs. 

9 For the State Alternative representing SNCR on Units 1 and 4, and shutdown of Units 2 and 3, 

10 the modeling is based on these same emission rates for TPM and H2SO4 , while for SO2 a rate of 

11 0.10 lb/MMBtu is used, as specified in the Term Sheet. The PNM modeling analysis was 

12 performed on a year-by-year basis for the facility-wide impact for the four scenarios. 

13 The most significant difference from prior modeling is that the 2013 modeling analysis 

14 considered two unit retirements, with SNCR for the other two units, as an alternative operating 

15 scenario.

16 In terms of methodology, the major difference between the 2007 and 2013 modeling 

17 analyses is that in 2007, PNM used a non-regulatory version of the CALPUFF modeling system 

18 (Version 6.1). In 2013, PNM used a regulatory version of the CALPUFF modeling system,

19 Version 5.8. Another difference is that in 2007, PNM used a National Park Service methodology 

20 to calculate inorganic condensable particulate matter, H2SO4. In 2013, PNM used the inorganic 

21 condensable particulate matter emission rates based on the facility’s total particulate matter 

22 emission limit as established in the NSR permit effective August 31, 2012, which is more 

23 realistic. An additional difference is that the 2013 modeling analysis was performed with a more 
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1 representative ammonia background concentration and an improved method to calculate 

2 visibility impacts. The 2013 modeling approach is more conservative than the 2007 modeling 

3 approach.

4 Evaluation of PNM’s Modeling

5 I determined that the PM modeling was performed according to the WRAP BART 

6 modeling guidelines and EPA Guidelines. The PNM BART modeling was performed using an 

7 EPA-approved version of the CALPUFF model to account for the chemical transformation of 

8 NOx and SO2, an EPA-compiled meteorological data set, and modified EPA’s modeling input 

9 files to include the source specific parameters for PNM specific modeling scenarios. The 

10 modeled CALPUFF domain, receptors, ozone, and the meteorological data used in the analysis 

11 all came directly from EPA modeling. 

12 As previously described, PNM modeled the following scenarios:

13

14 Table 1. Target Emissions for Modeling Scenarios

Scenario Operation and Target Emissions

Baseline Units 1-4 operating only with existing air pollution control technology and 

new permitted SO2 and Total Particulate Matter (TPM) emission rates of 

0.15 lb/MMBtu and 0.034 lb/MMBtu, respectively

Sulfuric Acid (H2SO4) emission rate of 0.000046 lb/MMBtu for non-SCR

EPA FIP Units 1-4 operating with existing air pollution control technology and with 

new SCR installation; NOx at 0.05 lb/MMBtu and new permitted SO2 and 

TPM emission rates of 0.15 lb/MMBtu and 0.034 lb/MMBtu, respectively

Sulfuric Acid (H2SO4) emission rate of 0.00026 lb/MMBtu for SCR

2011 NMEDSIP Units 1-4 operating with existing air pollution control technology and with 

new SNCR installation; NOx at 0.23 lb/MMBtu and new permitted SO2

and TPM emission rates of 0.15 lb/MMBtu and 0.034 lb/MMBtu, 

respectively

Sulfuric Acid (H2SO4) emission rate of 0.000046 lb/MMBtu for non-SCR

State Alternative Units 2 & 3 retired, Units 1 & 4 operating with existing air pollution 

control technology and with new SNCR installation; NOx at 0.23 

lb/MMBtu, new permitted TPM emission rate of 0.034 lb/MMBtu, and a 
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reduced SO2 emission rate of 0.10 lb/MMBtu

Sulfuric Acid (H2SO4) emission rate of 0.000046 lb/MMBtu for non-SCR

1

2 After determining that PNM’s modeling conformed to applicable requirements and 

3 guidelines, I explored the PNM modeling results to estimate the visibility improvement in three

ways recommended by EPA: 98th 4 percentile deciview (dv) at each Class I area for the four 

5 scenarios, average and maximum days exceeding the threshold value of 0.5 dv, and deciview 

6 improvement at each Class I area with additional NOx control technologies on the existing air 

7 pollution control technology.

Figure 1 shows the 98th 8 percentile deciview values of each NOx control scenario at the 16 

9 Class I areas for comparing the effects of each scenario in improving visibility. Deciviews are

10 the measure of visibility. More specifically, the higher the deciview value is, the worse the 

11 visibility condition is. As shown in Figure 1, the EPA FIP and State Alternative outperform the 

12 2011 NMED SIP from the comparison of the three years from 2001 through 2003. Mesa Verde 

National Park shows the highest 98th 13 percentile among the 16 Class I areas which is the nearest 

14 Class I area to SJGS, located approximately 40 km north of the facility. Grand Canyon National 

Park shows the lowest 98th 15 percentile which is located in the most distant area, located 

16 approximately 300 km west of the SJGS. 

When the frequency of the occurrence of the peak 98th 17 percentile is compared to each 

18 BART technology scenario, the figure illustrates that the State Alternative and the EPA FIP more 

significantly reduce the peak 98th 19 percentiles than the NMED SIP scenario does. Figure 2 shows

the cumulative 98th 20 percentile value of the sixteen Class I area for each scenario for the 

21 comparison of a general visibility improvement.

22 Figure 3 shows the days exceeding a 0.5 dv threshold at each Class I area for each BART 

23 technology scenario for each year of the three years. The figure indicates that the State 
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1 Alternative and the EPA FIP significantly improve visibility as compared to the Baseline and 

2 2011 NMED SIP scenarios. The State Alternative closely matches the EPA FIP scenario’s days 

3 comparison. 

4 Figure 4 shows the visibility improvement with additional NOx control technologies on 

5 top of the base case. The results indicate that the State Alternative and the EPA FIP are better 

6 than visibility impacts projected for the 2011 NMED SIP scenario. The State Alternative is 

7 comparable to the EPA FIP in improving visibility. 
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Figure 1. 98th Percentile on the 16 Class I areas
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Figure 2. Visibility Impact Results
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Figure 3. Average and Maximum Days Compared to 0.5 dv
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Figure 4. Visibility Improvement of Each Scenario