# YAKAMA NATION Department of Natural Resources

Environmental Management Program

How-Laak Hush Wit
Sacred Breath of the Columbia River Gorge

An Inter

­ Tribal Project in collaboration with the Confederated Tribes of the Umatilla Indian Reservation, the Nez Perce Tribe, and the Confederated Tribes of Warm Springs to Examine the Effects of Atmospheric Deposition on Natural and Cultural Resources and to Work Toward Development of an Air Quality Policy in the Columbia River Gorge



### Presents 

# Analysis of Air Quality Data in the Columbia River Gorge during Temporary Shutdowns at the PGE Boardman Plant

A Report to the Yakama Nation by Dr. Dan Jaffe

Yakama Nation , Post Office Box 151, 401 Fort Road, Toppenish, WA 98948 (509) 865

‐ 5121 

### February 27, 2008

Dept. of Natural Resources, Deputy Director, Phil Rigdon, prigdon@yakama.com , 509

‐ 865

‐ 5121

Environmental Program Manager, Aja DeCoteau, aja@yakama.com , 509

‐ 865

‐ 5121, ext 6038 Sacred Breath Project Manager, Rebecca Hawk Elwood, relwood@yakama.com , 509

‐ 865

‐ 5121, ext 60791

Analysis of Air Quality Data in the Colu mbia River Gorge during Temporary Shutdowns at the PGE Boardman Plant

Report to the Yakama Nation by Dr. Dan Jaffe Northwest Air Quality NW_airquality@hotmail.com February 27, 2008

Executive Summary In an earlier report, I analyzed 12 years of IMPROVE aerosol data from the site located at Wishram, Washington, along with airmass back-trajectories to indicat e source region. The data were used to identify the 50 worst air qua lity days in the Columbia River Gorge (CRG) between 1993-2004. A key result from that work wa s that the days with th e worst air quality in the CRG were associated with transport and sour ces on the east end of the Gorge, including the PGE coal fired power plant at Boardman, Oregon. I have extende d this analysis to consider months when the PGE Boardman plant was te mporarily shutdown and therefore had zero emissions. In this way I can more precisely qua ntify the contribution from the Boardman plant

to haze in the CRG. From this analysis, I find that the PGE Bo ardman plant has a signi ficant influence on air quality in the CRG. The contribution to fine particle mass (haze) at Wishram from the Boardman plant is 0.90 ug/m 3 for the entire year, 3.94 ug/m 3 if only the month of November is considered and 7.40 ug/m 3 if only November days when th e airflow is coming “down-gorge” (from east to west) is considered. In all 3 case s the results are statistically significant at a 95%

confidence interval or better. These results i ndicate a much larger influence from the PGE Boardman plant on air quality in the CRG, comp ared to the February 8, 2008 Science Summary Report of the Columbia River Gorge Air Qualit y Study (abbreviated here as CRG-AQS). The most likely cause for this discrepa ncy is that our analysis is base d on data collected at Wishram, whereas the CRG-AQS base thei r conclusions on a computer simulation, and this simulation shows a significant discrepa ncy with the actual data. These results have important policy implicati ons regarding pollution controls on the PGE Boardman plant: 1) The PGE Boardman plant has a significant in fluence on air quality and haze in the CRG. Days with the worst air quality in th e CRG occur in the fall and are associated2

with “down-gorge” atmospheric transport (east to west). For these days, the average contribution from the PGE Boardman plant is 7.40 ug/m 3 . This makes it one of the largest contributors to air quality degr adation in the CRG during these days. 2) As emissions from the PGE Boardman plant are reduced, I would expect an approximately linear reduction in its contribu tion to haze in the CRG. In other words,

a 50% reduction in all emissions from the PGE Boardman plant would bring its contribution to fine particle haze in the CRG to 3.70 ug/m 3 , which is still a very significant contribution.

Introduction In my earlier analysis, I found th at the worst air quality days were associated with sources in the eastern Columbia River Gorge (CRG) and were much more frequent in the fall, especially the month of November, when 18 of the 50 worst da ys occurred (Jaffe 2006). At the time that work was conducted, IMPROVE data was availa ble only through 2004. To extend this work, I have incorporated all data through 2006, focusing on a period in late 2005 when the PGE Boardman power plant was shutdown due to a probl em with a main turbine unit. Since the plant was almost fully shutdown in November 2005, this presents an ideal oppor tunity to examine the influence of the PGE Boardman plant during the month when we expect the greatest influence from the plant on air quality in the CRG.

Method Data for this work come from several sour ces. Aerosol data from the Wishram site for 1993-2006 is from the IMPROVE program website ( http://vista.cira.colostate.edu/improve/ ). Backward airmass trajectories were calculated using the on-line HYSPLIT model ( http://www.arl.noa a.gov/ready.html ). I used identical procedures and meteorological data as in my earlier report (Jaffe 2006). Emission data fo r the PGE Boardman plant were provided by Mark Fisher from the Oregon Department of Environmental Quality (ODEQ). The monthly

NOx and SO 2 emissions were determined from conti nuous emission monitor measurements and reported annually to ODEQ on Form F1106 for the years 1992-2006.3

Results Figure 1 shows the monthly NOx emission for the PGE Boardman plant going back to 1992. In most months, NOx emissions are close to 1000 tons. Over the 15 year timeframe, there have been several periods with extended shutdow ns that resulted in significantly lower NOx emissions. Simply put, if no coal is burned th en no NOx is generated. These shutdowns have occurred for a variety of reasons, both scheduled and unscheduled. Of the 163 months for which I have data, 33 months have emi ssions of less than 100 tons/month. I assume that if NOx emissions are essentially zero, then all other emissions will be near zero. It is important to note th at I am using the PGE Boardman NOx emissions as a surrogate for all plant emissions.



### Boardman NOx emissions



### 0 200 400 600 800 1000 1200 1400 1600 1800Jan-93Jan-94Jan-95Jan-96Jan-97Jan-98Jan-99Jan-00Jan-01Jan-02Jan-03Jan-04Jan-05Jan-06Jan-07NOx emissions (tons/month

Figure 1. NOx emissions (tons/month) for the PGE Boardman plant.

I examined all of the Wishram IMPROVE data to see if I could id entify an improvement in air quality in those months when the PGE pl ant had zero or insignifi cant emissions. Figure 24

shows the monthly average fine mass concentra tion (PM2.5) measured at Wishram vs the PGE

Boardman NOx emissions.



## 0 2 4 6 8 10 12 14 16 18

0 500 1000 1500 2000 PGE Boardman NOx emissions (tons/month)Monthly mean (ug/m3)

Figure 2. Monthly mean fine mass concentra tion (PM2.5) measured at Wishram vs PGE Boardman NOx emissions (tons/month).

Figure 2 does not show an obvious relationshi p, but this is not surp rising given the large seasonal and daily variations in meteorology and transport. On the other hand if we examine Figure 2 closely we see that only when the emissi ons are high, do we get high PM concentrations

in the CRG. If I segregate the data to compare months with low emissions vs months with high (or normal) emissions, I find there is a significan t difference. Comparing months that have emissions of less than 100 tons, I fi nd that the difference is 0.90 ug/m 3 . Using a t-test to compare months with emissions above and below 100 tons, th is difference is statisti cally significant with a confidence of 95% or greater . Choosing a slightly different segregation criteria (e.g. 200 tons/month or 300 tons/month) has no impact on this conclusion. Table 1 shows details on the

data above and below 100 tons/month.5

Months with emissions >100 tons Months with emissions <100 tons Number 122 30 Average fine mass (ug/m 3 ) 6.06 5.16

S.D. 2.10 1.50 Table 1. Comparison of fine mass data (PM 2.5) for months with greater than, and less than, 100 tons. The difference (0.90 ug/m 3 ) is statistically significant at a confidence of 95% or better. Based on our earlier findings (Jaffe 2006) I exp ect to identify an even larger influence on air quality from the PGE Boardman plant during the fall, especial ly November. Of the months with very low NOx emissions, one period occurr ed in November 2005, when the plant suffered an extended shutdown (November 2005-June 2006) due to a turbine failure . I will therefore

evaluate the November 2005 data in more detail . Figure 2 shows a time series of just the November data for all years.

November Fine mass concentations at Wishram with PGE Boardman monthly NOx emissions.



### 0 2 4 6 8 10 12 14 16 1819931994199519961997199819992000200120022003200420052006Fine mass (ug/m3)



### 0 200 400 600 800 1000 1200PGE Emissions (tons/month

Figure 3. Average fine mass (PM2.5) concentr ations for November 1993-2006 at Wishram (red triangles) and PGE Boardman NOx emission (b lue squares). In November 2005 the PGE Boardman plant was essentially shut down due to a turbine problem so NOx emissions were near zero for that month.6

From an air quality point of view, the cleanest November on record was in 2005, with an average fine mass concentration of 4.61 ug/m 3 . I compared the mean concentration for November 2005 with all other November data. A bar graph co mparison is shown in Figure 4 and a summary of

the results is shown in Table 2.

Fine mass concentration for November IMPROVE data



### 0 2 4 6 8 10 12PM2.5 (ug/m3)

All Nov except 2005 Nov 2005

Figure 4: Comparison of IMPROVE data from Wishram for November 2005 data (red bar on right) with all other Novembers data (blue bar on left). The error bars show the 95% confidence interval for each dataset and these do not overlap . A t-test confirms that the difference (3.94 ug/m 3 ) is statistically significant with a confidence of greater than 95%. More detailed data are shown in Table 2.

Table 2. Comparison of November 2005 IMPROVE observations with all other November observations. A t-test confirms that the difference (3.94 ug/m 3 ) is statistically significant with a confidence of greater than 95%. All November data except 2005 November 2005 Number of IMPROVE observations 108 10

Mean fine mass

concentration (ug/m 3 ) 8.55 4.61

S.D. 6.76 3.747

So far I have analyzed the impacts of the PGE Boardman emissions on all data and on only November data. Again, referring to our pr evious work, I found that the influence from

sources in the Eastern end of th e CRG was strongest when airma ss trajectories indicated strong transport from the east end of the Gorge. So I have further segregat ed our analysis by only looking at the data when back traj ectories indicated this transport. As I reported earlier (Jaffe 2006), these days are associated with the highest fine mass concentrations in the CRG. Example trajectories can be found in our earlier report. Figure 5 compares the fine mass concentr ation from November 2005 with all other November data, but in both cases, only days wi th airmass transport from the eastern CRG are included. While this significantl y reduces the number of data points, it points out the strong

influence from the PGE Boardman emissions. Fo r dates with transport from the eastern CRG, the average fine mass concentra tion in November 2005 is 5.94 ug/m 3 , compared to 13.34 ug/m 3

for all other November data w ith similar transport patterns.8

Fine mass concentration for November IMPROVE data with Trajectories from East Gorge

0 2 4 6 8 10 12 14 16 18



## All years exc 2005 (n=23) 2005 (n=5)PM2.5 ug/m3

Figure 5. Fine mass (PM2.5) concentration fo r all November IMPROVE data (1993-2006) for days with trajectories arriving at Wishram from the eastern e nd of the CRG. In November 2005 the PGE Boardman plant was essentially shut do wn due to a turbine problem. As a result, concentrations at Wishram were significantly re duced. Each bar shows the mean and the error bars show the 95% confidence inte rval around each mean. A t-test confirms that the difference (7.40 ug/m 3 ) is statistically significant at a confiden ce of greater than 95% . Table 3 shows the detailed data.

All November data except 2005 with East Gorge trajectories November 2005 data with East Gorge trajectories Count 23 5 Mean fine mass concentration (ug/m 3 ) 13.34 5.94

S.D. 8.33 1.51 Table 3. Comparison of November 2005 IMPROVE observations with all other November observations, that exhibit airmass transport from the eastern CRG. A t-test confirms that the difference (7.40 ug/m 3 ) is statistically significant with a confidence of greater than 95%.

Taken together, these three analyses present a consistent picture of the influence from the PGE Boardman plant on air quality in the CRG. Us ing the analysis for all months, I find that the9

PGE Boardman influence is, on average, 0.90 ug/m 3 . Comparing only November data, I find that the influence is significantly larger at 3.94 ug/m 3 . Comparing only November data for days with likely transport from the eastern CRG, I find the influence is greatest at 7.40 ug/m 3 . Table 4

summarizes the results and also derives the % contribution at Wish ram due to the PGE

Boardman emissions.

Table 4. Summary of fine mass co ncentrations at Wishram (ug/m 3 ) with and without PGE Boardman influence. With Boardman influence

(ug/m 3 ) Without Boardman influence

(ug/m 3 ) Difference (ug/m 3 ) Fine mass due to PGE Boardman (%) All months 1 6.06 5.16 0.90 14.9 All November data 2 8.55 4.61 3.94 46.1

All November data w/ trajectories from East Gorge 3 13.34 5.94 7.40 55.5

1 This row compares months where PGE Boardman emissions are less than 100 tons vs all other months. 2 This row compares the November 2005 data (plant shutdown) with all other November data. 3 This row compares November 2005 data with east gor ge trajectories with all other November data with east gorge trajectories.

Comparison with other studies A number of agencies have been involved in a significant air quality study in the CRG between 2000-2007. Only data from one year was considered (2004) and the primary conclusions were derived from analysis of a re gional air quality model. The Columbia River Gorge Air Quality Study, Science Summary Re port (abbreviated here as CRG-AQS) was released on February 8 th , 2008. I will compare our results to those in this report. As part of the observations and modeling that was conducted for the CRG-AQS report, attention focused on one air pollution episode , which occurred between November 8-14, 2004. The CRG-AQS report, as well as our earlier work (Jaffe et al., 2006) found that emissions in the east end of the CRG were largely responsible for this episode. Thus it is reasonable to compare the results from this November episode with our results for November when trajectories are coming from the eastern CRG. By reference to the bottom line of Table 4 (above), we see that the PGE Boardman plant is responsible for approximately 55% of the fine mass during these episodes. In contrast the CRG -AQS report states that Electri c Generation Units (EGUs) east of10

the CRG (essentially only the PGE Boardman plan t) were responsible for 32% of the fine mass at Wishram (CRG-AQS, pg 87), a significan t discrepancy with our results. In addition, our analysis indicates that for the November episodes with down-gorge transport, the PGE Boardman contribution is 7.40 ug/m 3 at Wishram, whereas in the CRG-AQS report they conclude that EGUs are responsible for only 3.17 ug/m 3 (2.64 ug/m 3 + 0.53 ug/m 3

, Table 5-7, pg 85). Thus the CRG-AQS study a ppears to significantl y under-estimate the influence from the PGE Boardman plant. A likely s ource of this discrepanc y is that our analysis was based on analysis of the IMPROVE data co llected at the Wishram site, whereas the CRGAQS conclusions were based largely on a computer simulation of air quality in the CRG. In Figure 5-1 of the CRG-AQS (pg 74) there is one graph comparing the computer simulation with

observations from several sites in the CRG. For th e November cases (right side of Figure 5-1) the model is performing very poorly. For example, for the worst air quality days the observed scattering coefficients is up to 250 Mm -1 , however the "predicted" (i.e. modeled) value is low by about a factor of 3. In short, this simulati on does a poor job of repr oducing the observational data and appears to significantly underestimate the contribution to haze from sources in the CRG for the worst air quality days of the year. The report goes on to estimate the influence from EGUs in the east of the CRG in 2018. It does this by assuming a “presumptive BART" c ontrol level for the Boardman power plant.

This limit is assumed to be 0.23 lbs of NOx per million BTU or 1323 lbs NOx per hour (CRGAQS, pg 92). This translates into annual NOx emissions of 5790 tons/year. Given that the

current emissions are 800-1000 tons/month durin g normal operations (see Figure 1), the 5790 tons/year limit is approximately a 50% reduction in NOx emissions from current levels. The "presumptive BART" control also results in a 70% reduction in SOx emissions (CRG-AQS, pg 92). For the November episode considered, a nd using these assumed emissions in 2018, the resulting influence from PGE Boardman is 0.95 ug/m 3 (CRG-AQS, Table 7-9, pg 108). Our results suggest a much larger level of influence. Referring to the bottom line of Table 4 (above), for the November episode, I estimate the cu rrent influence from PGE Boardman at 7.40 ug/m 3

. Even if all emissions were reduced by 70% in 2018 em issions, then the influence would still be approximately 2.22 ug/m 3 , much larger than the value reported in the CRG-AQS report.11

In addition, the CRG-AQS compares the 2018 base case with 5 alternate emission scenarios. Case 1 represents a total zero out of the PGE Boardman emissions. From this analysis the authors state: “Since major SO2 and NOx emission reductions at the Boardman Power Plant are already reflected in the 2018 inventory (i.e., presumptive BART controls), practically zero sensitivity to Case 1 is seen.” (CRG-AQS, pg 97).

This statement is inconsistent with their ow n results and certainly inconsistent with the results of our study. It is very puzzling how the study could conclude that by reducing

Boardman emissions to 50-70% of current levels brings the influence at Wishram to 0.95 ug/m 3

, and then to claim that zeroing out of these emi ssions will have no further influence. Our results

indicate that the current PGE Boardman influence during these November episodes is 7.40 ug/m 3 and I would expect a linear relationship between this influence and the degree of emission control, assuming all pollutant s were reduced a similar %. In summary, our analysis of times when the PGE Boardman plant was temporarily shutdown indicate a much larger influence on air quality in the CRG, compared to the CRG-AQS study. I believe the primary cause for this discre pancy is that the CRG-AQS result is based on a computer simulation, which does a poor job of reproducing the actual data for the worst air quality days of the year.12

References Pitchford M.L, Green M.C. Morris R., Emery C ., Sakata R., Swab C., and Mairose P.T. Columbia River Gorge Air Qu ality Study, Final Science Su mmary Report. February 8 th

, 2008. Jaffe D.A. Who is polluting the Columbia River Gorge? Final report to the Yakama Nation. Northwest Air Quality, Inc., Seattle, Wash., December 31, 2006.

Appendix: Summary of data used. More detailed data can be obtained from the IMPROVE website or from the author. ND indicates no data.

Month Boardman NOx emissions

(tons) Average fine mass at Wishram

(ug/m3) June-93 104.5 6.18 July-93 426.2 4.53 August-93 1141 7.33 September-93 1082.4 8.01

October-93 1103 9.34 November-93 1076.7 13.77 December-93 1042.6 10.45

January-94 973.9 8.36 February-94 1046.38 6.89

March-94 1116.08 5.13 April-94 1106.32 5.05 May-94 195.99 6.79 June-94 492.68 4.57 July-94 1169.02 7.82 August-94 1542.76 7.35 September-94 1159.65 7.31

October-94 767.9 6.46 November-94 801.2 4.48 December-94 994.5 ND

January-95 949.4 ND February-95 0 ND

March-95 0 ND April-95 0 7.17 May-95 12.8 6.20 June-95 0 5.65 July-95 206.5 6.96 August-95 913.4 5.19 September-95 785.1 8.74

October-95 600.8 5.23 November-95 372.8 6.13 December-95 0 6.35

January-96 0 4.11 February-96 0 7.90

March-96 0 5.18 April-96 0 2.89 May-96 0 4.16 June-96 0 5.00 July-96 195 6.84 August-96 687 7.64 September-96 702 5.08

October-96 915 5.73 November-96 952 6.64 December-96 889 4.70

January-97 184 6.39 February-97 0 4.18

March-97 0 4.52 April-97 0 5.55 May-97 0 6.16 June-97 0 3.95 July-97 153 6.32 August-97 586 6.72 September-97 519 7.3913

October-97 329 6.63 November-97 651 8.39 December-97 826 6.83

January-98 719 5.59 February-98 304 3.81

March-98 708 4.83 April-98 752 7.38 May-98 62 4.14 June-98 0 4.74 July-98 678 8.00 August-98 784 6.97 September-98 861 7.47

October-98 909 9.49 November-98 942 6.64 December-98 1012 4.01

January-99 870 4.39 February-99 583 4.01

March-99 547.9 5.81 April-99 731.8 6.16 May-99 383.6 3.87 June-99 349.8 3.42 July-99 823.8 6.55 August-99 891.3 7.69 September-99 904.5 7.81

October-99 1005.6 6.38 November-99 949.7 7.04 December-99 907.8 3.28

January-00 799.9 3.72 February-00 912.2 4.59

March-00 898.8 4.11 April-00 770.3 4.53 May-00 266.3 3.51 June-00 0 ND July-00 255.6 ND August-00 871.6 ND September-00 731 ND

October-00 969 ND November-00 947.7 ND December-00 782.9 ND January-01 853.7 9.40 February-01 898.2 7.26

March-01 1009.2 4.18 April-01 957.1 4.63 May-01 560.9 4.99 June-01 604.1 4.43 July-01 1048.7 6.73 August-01 993.1 7.25 September-01 950.7 4.98

October-01 1023.1 4.44 November-01 851.5 7.79 December-01 1019.3 5.54

January-02 951.1 4.43 February-02 849.4 3.76

March-02 499 3.30 April-02 465.9 3.80 May-02 726.8 3.25 June-02 0 4.72 July-02 286.1 7.81 August-02 779.5 6.52 September-02 873.8 5.82

October-02 976.9 7.23 November-02 1005.5 16.58 December-02 988.3 6.20

January-03 899 5.31 February-03 883.8 4.09

March-03 953.7 2.02 April-03 926.5 2.01 May-03 853.2 5.34 June-03 51.4 6.43 July-03 925.8 4.41 August-03 926.2 5.90 September-03 958.5 7.13

October-03 954.2 5.98 November-03 878.3 7.36 December-03 870.4 4.48

January-04 975.9 3.47 February-04 802.4 6.15

March-04 918.3 3.8914

April-04 823.7 4.92 May-04 0 3.71 June-04 0 4.18 July-04 41.4 8.83 August-04 750.1 5.21 September-04 864.9 5.43

October-04 944 5.33 November-04 835 10.35 December-04 857.1 4.42

January-05 1061 5.71 February-05 913.2 6.68

March-05 844.1 3.68 April-05 962.4 3.04 May-05 72.5 3.49 June-05 830.3 5.65 July-05 868.8 8.36 August-05 973.7 10.61 September-05 1020.8 5.59 October-05 756.4 5.29 November-05 10.3 4.61 December-05 0 6.80

January-06 0 2.48 February-06 405 3.86

March-06 0 3.61 April-06 0 4.70 May-06 97.5 7.03 June-06 13.2 6.33 July-06 1016.6 7.07 August-06 1033.7 7.89 September-06 821.8 5.80

October-06 882 5.47 November-06 970.6 6.65 December-06 1087.5 6.07

IMPROVE sample dates in November with airma ss trajectories suggesting transport from the east end of the CRG.

19-Nov-2006 14-Nov-2003 1-Nov-1995 1-Nov-2006 25-Nov-2002 24-Nov-1993 24-Nov-2005 4-Nov-2002 28-Nov-2006 21-Nov-2005 1-Nov-2002 7-Nov-2002 18-Nov-2005 18-Nov-2001 24-Nov-2001 15-Nov-2005 12-Nov-2001 9-Nov-2001 9-Nov-2005 17-Nov-2000 29-Nov-2000 14-Nov-2004 14-Nov-2000 20-Nov-2000 1-Nov-2004 7-Nov-1998 15-Nov-1997 8-Nov-2004 12-Nov-1997 10-Nov-1993