# Appendix 10.13

Table 10-16

Reference 3: Table 23 on page D-26 of "Appendix D: Supplement BA RT information for Boardman Power Plant BART Report", June 19, 200 9

### 1. Boardman Power Plant BART Report

Appendix D: Supplemental BART Information 

### Page D-2



# DEQ BART Report

for the

Boardman Power Plant

Updated December 19, 2008

Update consists of following: Page 6, link added to ERG Report. Page 19, correction to Table 15. Page 20, addition to Table 16. Page 23, minor change under Cost-Benefit Analysis, and link added to BART Cost Spreadsheet. Page 25, minor edit and link added to BART Modeling Spreadsheet. Page 29, minor clarification made.

### 1. Boardman Power Plant BART Report

Appendix D: Supplemental BART Information 

### Page D-26 baseline, but Mt. Hood is the most frequently impacted Class I area in all other scenarios. The number of days is based on all data, including the days with impacts greater than the 98 th percentile.

5. 14 Class I Wilderness Areas and National Parks within 300 km of the po wer plant were modeled. Other Class I areas farther from the power plant may be impacted, but the impacts would be less than presented in the table.



### Table 22: Visibility Modeling Results (percent improvement)

Parameter NO

x Controls SO 2 Controls

NLNB/

MOFA SNCR SCR SDFGD WFGD

Mt. Hood visibility impacts (98 th
percentile delta dv) 13% 26% 40% 23% 17%

Days/year >1.0 delta dv (most frequently impacted Class I area) 13% 21% 33% 21% 21%

Total visibility impacts (sum of

98 th percentile delta dv for all Class I areas) 15% 27% 40% 34% 33%

Average Class I Area visibility impacts (delta deciview) 11% 27% 40% 29% 33%

Average for all parameters 13% 25% 38% 27% 26%



### The BART guidelines recommend analyzing visibility improvement for the highest impacted Class I area with the assumption that any improvement in the worse impacted area would result in improvement in the lesser impacted areas. However, since the Boardman Plant significantly impacts 14 Class I Areas within 300 kilometers, the Department tried to include other parameters that would assess the significance of the improvements for all Class I areas impacted. Therefore, the Department added the number of Class I areas with impacts greater than 1.0 delta deciview, the total delta deciviews for all Class I areas (98 th 

### percentile), and the average delta deciview for all Class I areas (98 th 

### percentile). As can be seen in Table 21, any one of the parameters is fairly representative of the other parameters perhaps with the exception of WFGD. Given these results, the Department does not believe that adding additional parameters, such as total deciview days, would result in any other conclusions and would probably just add confusion to the analysis (e.g., more days of impacts than are in a year). Using the results of the visibility modeling, the cost effectiveness of the control technologies is recalculated by relating the costs to deciview improvement (Mt. Hood and all Class I areas) as shown in the following 2 tables.

Table 23: Visibility Improvement Cost Effectiveness for Mt. Hood (98 th 

### percentile):

Pollutant Control Technology Control

Effectiveness (lb/mmBtu) Visibility

Improvement (deciview) Cost

effectiveness (million $/dv) Incremental Cost

Effectiveness (million $/dv)

NO

x NLNB/MOFA 0.28 0.58 $6.4

---

NLNB/MOFA &SNCR 0.23 1.20 $5.9 $5.5

SCR 0.07 1.84 12.5 24.9

NLNB/MOFA & SCR 0.07 1.84 $14.65 $30.7

SO 2 SDFGD 0.12 1.04 $35.2

---

WFGD 0.07 0.80 $64.9 1

1 Incremental cost effectiveness cannot be calculated because even though WFGD costs more than SDFGD, WFGD produces less visibility improvement.