Appendix 10.13 

Table 10-16 

Reference 3: 

Table 23 on page D-26 of "Appendix D: Supplement BART information 

for Boardman Power Plant BART Report", June 19, 2009 

 
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 98th percentile. 

5. 14 Class I Wilderness Areas and National Parks within 300 km of the power 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 

NOx Controls SO2 Controls 

NLNB/ 

MOFA SNCR SCR SDFGD WFGD 

Mt. Hood visibility impacts (98th

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 

98th 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 (98th percentile), 

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

Pollutant Control Technology 

Control 

Effectiveness 

(lb/mmBtu) 

Visibility 

Improvement 

(deciview) 

Cost 

effectiveness 

(million $/dv) 

Incremental Cost 

Effectiveness 

(million $/dv) 

NOx 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 

SO2 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.