### FINAL

Modeling Analysis of Future Emission Scenarios for Ozone Impacts in the Puget Sound Area

prepared by

Brian Lamb, Ying Xie Laboratory for Atmospheric Research Department of Civil & Environmental Engineering Washington State University

Pullman, WA 99164-2910

and

Clint Bowman, Sally Otterson, and Doug Schneider Washington State Department of Ecology

Kathy Himes, John Anderson, Kwame Agyei, and Beth Carper Puget Sound Clean Air Agency

prepared for

Puget Sound Clean Air Agency Seattle, WA

August 2006ii 

### Table of Contents 1 Summary...................................................................................................................... .... 1 2 Introduction.................................................................................................................

..... 4 3 Modeling System Description.......................................................................................... 5 3.1 Model Domain .......................................................................................................... 5 3.2 Episode Selection...................................................................................................... 6 3.3 Meteorology Modeling Methods .............................................................................. 8 3.4 Meteorology Modeling Results................................................................................. 9 3.5 Photochemical Modeling Methods and Boundary Conditions ............................... 18 4 1998 Base Case Photochemical Modeling..................................................................... 19 4.1 Results....................................................................................................................

. 19 4.2 CMAQ Statistics Evaluation................................................................................... 25 5 Sensitivity Analyses....................................................................................................... 32 6 Future Year (2015) Modeling ........................................................................................ 35 6.1 Low-RVP Run Results............................................................................................ 36 6.2 High-RVP Run Results........................................................................................... 50 6.3 Modeled Attainment Test ....................................................................................... 54 Reference ......................................................................................................................

.... 56 Appendix A. Emissions Inventory Documentation .......................................................... 58 Appendix B. Methodology for Local Non-Road Mobile Source Emissions.................... 77 Appendix C. Onroad Mobile Source Emissions Inventory and Data Pre-Processing ..... 79



### List of Tables

Table 1. Selected peak ozone concentrati ons during the July 22-28, 1998 episode (ppm) 8 Table 2. MM5 performance statistics for July 1998 episode............................................ 17 Table 3. Mean Normalized Bias and Error ca lculated by obs ozone greater than 40 ppb. 27 Table 4. Normalized Bias between average observed and predicted 8-hr daily maxima (ppm) using 1x1 and 5x5 grids. ............................................................................... 28 Table 5. Normalized Bias between observed and predicted 8-hr daily maxima (ppm) using all data pairs based on 5x5 and 1x1 grids....................................................... 29 Table 6. Predicted maximum 8-hr ozone (ppm) downwind of Puget Sound and the percentage change from the 1998 base r un by changing VOC and/or NOx emissions as well as removing indivi dual source categories.................................................... 34 Table 7. Anthropogenic VOC and NOx emissions comparison between the 2015 low RVP run and 1998 base run on Tuesday, July 28.................................................... 36 Table 8. Anthropogenic VOC and NOx emi ssions comparison between the 2015 high RVP run and 2015 low RVP run on Tuesday, July 28. ........................................... 36 Table 9. Daily maximum 8-hr ozone (ppm ) for 2015 low RVP run and the relative changes from 1998 base run using 1x1 and 5x5 grids............................................. 41 Table 10. Daily maximum 8-hr ozone (ppm ) for 2015 high RVP run and the relative changes from 2015 low RVP run using 1x1 and 5x5 grids. .................................... 52 Table 11. Modeled attainment te st for the Puget Sound area. .......................................... 55iii 

### List of Figures

Figure 1. 12 and 4 km MM5 domain (D03 and D04)......................................................... 6 Figure 2. Surface weather map on Monday, July 27, 1998. ............................................... 7 Figure 3. Surface temperature pattern from MM5 for 16:00 PDT on July 27, 1998........ 10 Figure 4. Surface wind pattern from MM5 for 16:00 PDT on July 27, 1998................... 10 Figure 5. Surface temperatures and wind sp eed predicted by MM5 compared with observations at the Sea-Tac airport....................................................................... 11 Figure 6. Surface temperatures and wind sp eed predicted by MM5 compared with observations at Portland........................................................................................ 11 Figure 7. Surface wind direction predicted by MM5 compared with observations at the Sea-Tac airport...................................................................................................... 12 Figure 8. Surface wind direction predicted by MM5 compared with observations at Portland................................................................................................................. 12 Figure 9a-c. Predicted PBL heights by MM5 on July 26-28, 1998 (shown as GMT)...... 14 Figure 10a-f. Observed and predicted vertical profiles of temperature, wind speed, and wind direction at Quillayute, WA and Salem, OR................................................ 15 Figure 11. Surface stations used in MM5 observational nudging. ................................... 17 Figure 12. Ozone monitoring sites within the modeling domain...................................... 19 Figure 13. Observed and simulated 1-hr surface ozone concentrations for July 1998 episode. ................................................................................................................. 21 Figure 14. Scatter plot of the average obser ved and predicted 8-hr daily maxima for 5x5 and 1x1 grids for July 1998 episode ..................................................................... 31 Figure 15. Quantile-Quantile plot of observed and predicted 8- hr daily maxima using all data pairs for 5x5 and 1x1 grids for July 1998 episode. ....................................... 31 Figure 16a-c. Sensitivity curves of maximum 8-hr ozone downwind of Puget Sound to changes in anthropogenic NOx and VOC emissions on July 26-28, 1998........... 33 Figure 17a-c. Simulated ozone contour at 17 PDT on July 26 for 1998, 2015, and ozone difference contour between 1998 and 2015 (2015-1998)..................................... 38 Figure 18a-c. Simulated ozone contour at 16 PDT on July 27 for 1998, 2015, and ozone difference contour between 1998 and 2015 (2015-1998)..................................... 39 Figure 19a-c. Simulated ozone contour at 17 PDT on July 28 for 1998, 2015, and ozone difference contour between 1998 and 2015 (2015-1998)..................................... 40 Figure 20. Simulated 1-hr surface ozone concentrations for 2015 low RVP run compared to the observed the predicted ozone le vels during the July 1998 episode. ........... 43 Figure 21a-b. VOC/NOx ratios (moleC/mole) in surface layer emissions from 1998 base run and 2015 low RVP run at 16 PDT on July 27. ............................................... 48 Figure 22a-b. O3/NOx contour plot from the 1998 base r un and 2015 low RVP run at 16 PDT on July 27. .................................................................................................... 49 Figure 23a-c. Ozone differen ce plot between the 2015 high RVP and low RVP run on July 26-28, 2015. (2015 high RVP – 2015 low RVP) ......................................... 51iv 

### Appendix A Table of Contents
Introduction................................................................................................................... .....60 Emissions Inventory Approach..........................................................................................61 Base Year Point Sources Inventory ..................................................................................65 Base Year Stationary Area Sources Inventory...................................................................65 Base Year Non-road Mobile Sources Inventory................................................................66 Non-road Sources included in NONROAD model................................................66 Non-road sources not included in the NONROAD model ....................................66 Spatial and temporal allocation..............................................................................67 Base Year On-Road Mobile Sources Inventory.................................................................67
Base Year Biogenic Inventory...........................................................................................68 Future Year (2015) Emissions Projections ........................................................................68 Point Sources .........................................................................................................68 Stationary Area Sources.........................................................................................69 Non-Road Mobile Sources.....................................................................................69 On-Road Mobile Sources.......................................................................................70 Biogenic Emissions................................................................................................71 Emissions Su mmaries .......................................................................................................71 Emissions Inventory Limitations .......................................................................................73 Comparisons between Seattle and Portland Area Emissions.................................74 VOC/NO X Ratios ...................................................................................................75



### Appendix A Tables Table 1. Washington and Oregon Counties in Modeling domain ....................................64 Table 2. Local Non-Road Emissions Estimates Substitutions, Tons/Year.......................67 Table 3. Puget Sound VOC Emissions, Base and Future Cases.......................................72 Table 4. Puget Sound NO X Emissions, Base and Future Cases........................................73 Table 5. Ratio of Seattle Area/Portland Area Emissions..................................................74 Table 6. Comparison of Portland, Seattle NEI 1999, and Seattle Local Estimates ..........75 Table 7. VOC/ NO X Ratios for King and Multnomah Counties, 1998 Ozone Event.......75 Table 8. VOC/ NO X Ratios for Puget Sound Area, 1998 Ozone Event ..........................76



### Appendix A Figures

Figure 1. Ozone 8-hour concentratio ns at Puget Sound area monitors.............................61 Figure 2. Modeling Domain for Emissions Inventory......................................................63



### Appendix B Table of Contents

King County.................................................................................................................... .. 77 Kitsap County .................................................................................................................. . 77 Pierce County.................................................................................................................... 78 Snohomish County............................................................................................................ 78v 

### Appendix C Table of Contents

1 Project Description..........................................................................................................

........... 81 2 Onroad Mobile Source Inventories............................................................................................ 81 2.1 Activity Level .............................................................................................................

....... 81 2.2 Emission Rates: MOBILE6.2 ............................................................................................ 83 2.2.1 Evaluation Month.........................................................................................................

.. 84 2.2.2 Registration Distribution................................................................................................ 84 2.2.3 Humidity .................................................................................................................

....... 85 2.2.4 Inspection and Maintenance (I/M) Program.................................................................. 85 2.2.5 Reid Vapor Pressure (RVP)........................................................................................... 87 2.2.6 Gasoline Sulfur Content (base case 1998 only)............................................................. 88 2.2.7 Diesel Sulfur Content....................................................................................................

. 89 2.2.8 Example Emission Rates................................................................................................ 89 2.3 Emissions Estimates........................................................................................................

... 90 3 Temporal Projection Factors..................................................................................................

.... 90 3.1 Washington and Oregon .................................................................................................... 90 3.2 British Columbia...........................................................................................................

..... 90 4 Emissions Inventory Data Formatting for SMOKE................................................................... 90 4.1 Emissions Files ............................................................................................................

...... 91 4.2 Spatial Allocation Files...................................................................................................

... 91 4.3 Temporal Allocation Files ................................................................................................. 91 4.4 Chemical Speciation Files.................................................................................................. 92 4.5 Projection Files ...........................................................................................................

....... 92



### Appendix C List of Tables

Table 1: County ADVMT Estimates ...............................................................................................

........ 82 Table 2: I/M Program Area County Fractions....................................................................................

..... 83 Table 3: Registration Distribution Assignments................................................................................

...... 85 Table 4: July Humidity Mixing Ratios, Longterm Averages .................................................................. 85 Table 5: I/M Program Parameters: King, Pierce and Snohomish Counties, 1998................................... 86 Table 6: I/M Program Parameters: Clark County, 1998.......................................................................... 86 Table 7: I/M Program Parameters: King, Pierce and Snohomish Counties, 2015................................... 87 Table 8: I/M Program Parameters: Clark County, 2015.......................................................................... 87 Table 9: Fuel RVP Assignments, psi............................................................................................

........... 88 Table 10: Example TOG, NOx, CO and SO 2 Emission Factors in grams per mile................................. 89 Table 11: Example PM 10 , PM 2.5 and NH 3 Emission Factors in grams per mile...................................... 891 1 Summary

In the Puget Sound area, con tinued population growth, changes in transportation and shipping, and technological changes will aff ect future emissions of Volatile Organic Compounds (VOC) and Nitrogen Oxides ( NOx), which are precursors to ozone formation. It is expected that total m obile source emissions may decrease, while emissions from marine shipping may incr ease from 2006 through 2015. In order to estimate the impact of ozone due to these emission changes, an ozone modeling study was completed for the region. The study include d multiple scenarios to determine if the Puget Sound area is likely to maintain federa l attainment status of the ozone national ambient air quality standa rd in the future.

An ozone episode which occurred during July 22-28, 1998 was selected as a base case for this analysis. This episode recorded the hi ghest ozone levels in recent history in the Puget Sound area, and is the most extreme me teorology available. During this episode, one hour average ozone concen trations as high as 140 ppb were observed downwind of Seattle and 8 hour average concentrations reached 111 ppb. Air quality during this period was simulated using the Mesoscale Meteorological model version 5/ Sparse Matrix Operator Kernel Emissions Mode ling System / Community Multi-scale Air Quality Modeling System (MM5/SMOKE/CMAQ).

In the first phase of this study, the m odel performance was evaluated. For the meteorology simulation, the Noah Land-su rface model (Chen and Dudhia, 2001) was used along with observational nudging with available surface wind observations, and MM5 showed acceptable performance in compar ison to an independent set of surface wind and temperature data. “Nudging” is a num erical process that adjusts the numerical meteorological fields toward available obser vations or analysis fields. For the CMAQ simulation, model performance statistics for most monitoring sites were within U.S. EPA suggested 1-hr and 8-hr ozone modeling gui delines (EPA, 1999 and EPA, 2005). Results from time series analyses, scatter plots of observed vs. predicted, and Quantile-Quantile plots reflected the generally good agr eement between model simulations and observations.

In the second phase, emissions were adjusted to reflect expected changes for the year 2015, and two additional simulations were performed to assess potential ozone levels in the future. The first involved projected future growth and assumed use of low Reid Vapor Pressure (RVP) gasoline (low RVP r un). The second applied the same growth factors, but without the low RVP control strategy (high RVP run).

In western Washington counties within th e modeling domain, the total NOx and VOC emissions were projected to decrease appr oximately 21% and 31%, respectively, in the 2015 low RVP run compared to the 1998 base case simulation. This included 65% reductions for VOC and NOx from onroad mob ile sources. Emissions of NOx were projected to increase (up to 33%) for the nonroad mobile source category due to substantial growth of mari ne shipping operations. For the 2015 low RVP run, ozone reductions around 10 ppb were predicted at the Enumclaw and Pack Forest monitoring2 sites where maximum ozone was observed in th e July 1998 base case. Some increases in ozone were predicted close to the Seattle urban core area due to increased VOC/NOx ratios. The urban core area still appeared to be VOC limited in the future year run, but to a lesser degree compared to 1998. For the 2015 high RVP run, the increases of ozone were less than 1 ppb compared to the low RVP r un, which is consistent with the fact that the changes in the emission inventory were fairly small.

To further investigate ozone formation in the region and examine possible control strategies, several sensitivity analyses were also conducted. First, uniform changes in anthropogenic VOC and/or NOx emissions were applied covering th e range from 0.75 to 1.25 times the base case emissions. Second, th e impact of individual source categories was examined by completely removing emissions for each source category while holding all other emissions at the base case levels.

Changes in VOC emissions by ±25% resulted in linear changes in maximum 8-hr ozone with a 9% increase/decrease downwind of the Puget Sound area. Increasing NOx emissions by 25% decreased maximum 8-hr ozone concentrations by 2% to 5%. Decreasing NOx emissions by 25% resulted in a 5% to 12% increa se during the three days from the episode with the highest concentrations. Reducing VOC and NOx emissions each by 25% resulted in relatively small changes ranging from -1% to -5%. For one case, NOx emissions were increased by 25% and VOC emissions were decreased by 25%; this yielded resu lts very similar to the pattern obtained with only VOC emissions reduced.

Among the individual emission source categories, biogenic emissions appeared to have the largest effect on reducing the maximum 8- hr ozone concentrati ons due to the large amount of biogenic VOC emissions within the region. In contrast, removing point sources resulted in very small changes. When anthropogenic area sources were removed, the ozone reductions were around 8%. Rem oving nonroad or onroad mobile emissions resulted in 13% and 10% reductions in ma ximum ozone levels, re spectively. If only VOC emissions were removed from nonroad or onroad sources, the ozone reductions were up to 14%. However, when only NOx emissions were removed from nonroad or onroad sources, large increases (up to 13%) in maximum 8-hr ozone concentrations were predicted. This increase of ozone is expected from the change in titration of ozone due to fresh NO emissions in urban areas.

Future design values (DVFs), used to compar e to the federal standard, were calculated and demonstrated that Puget Sound area monito ring sites remain in federal attainment status in 2015. DVFs are based on current de sign values from actual monitoring sites, and take into account the uncertainty in model performance for peak ozone by using model results in a relative sens e. In this study, the model under-predicted peak ozone levels at the Enumclaw mon itoring site on two of the three high ozone days, due largely to uncertainty in the meteorology simulations of wind direction and Planetary Boundary Layer (PBL) heights. The model under-predi ction is not included in the DVF because it uses a relative reduction factor (RRF) that “ normalizes” errors in the model. Within the Puget Sound area, three ozone monitoring site s (Weyerhaeuser Mill, Pack Forest, and3 Lake Sammamish) have ozone data for all fi ve years from 1996 to 2000 and were used in the modeled attainment test. EPA’s guidance requires five years of data to calculate DVFs (EPA, 1999 and EPA, 2005). All thr ee monitoring sites pass the modeled attainment test and will maintain compliance with the 8-hr ozone standard for year 2015 by having future design values less than 84 ppb, the current federal standard. Weyerhaeuser Mill and Pack Forest were predicted to have the same future concentrations (68 ppb) with relative reduction factor of 0.901 and 0.936, respectively. Smaller reduction was predicted for Lake Samm amish with relative reduction factor of 0.970 and 58 ppb for the future design value.4 2 Introduction

The Puget Sound region of western Washington periodically experiences elevated ozone levels downwind of the Seattle urban area. Despite emissions reductions in ozone precursors, ozone levels have not decreased in a similar trend. Continued growth is predicted for the Puget Sound area, including dramatic gr owth in marine activities (shipping and excursion). These changes ma y off-set precursor emissions reductions from cleaner vehicles that are being introdu ced into the fleet c ontinuously. The Puget Sound Clean Air Agency (the Agency) has co ncerns about the pot ential for periodic ozone episodes to produce exceedances and possibly violations of the National Ambient Air Quality Standard (NAAQS) for ozone in the future.

To help address these concerns, a nume rical photochemical modeling analysis was conducted for the area. The Puget Sound Clean Air Agency (the Agency) will use the results of this work for internal planning. Specifically, the Agency will use modeling results to help predict if the Puget Sound regi on is likely to remain in ozone attainment with future growth. The Agency would also like to determine how long it is prudent for local refiners to produce low Reid Vapor Pr essure (RVP) gasoline in summer months. Low RVP summer gasoline is one of the ma in strategies that came out of the 2001 CO/Ozone stakeholder process. This mode ling effort supports the charge from the 2001 stakeholders (PSCAA, 2001):

“... prudence requires that early identificat ion of any potential impediments to remaining in attainment be vigorously pursued so that moderate, responsible action can be initiated first, in order to prevent the need for more drastic measures later.”

This report describes the methods used in th is study and presents the results from the modeling analyses. This st udy involved the use the Mesoscale Meteorological model version 5 (MM5) nudged with av ailable surface meteorological data. The meteorological output was used to drive the Community Mu lti-scale Air Quality (CMAQ) model. Emission inventories were de veloped using the Sparse Ma trix Operating Kernel for Emissions (SMOKE) processing tool.

The study employed meteorological and emissions data for a historical episode that occurred during July 22-28, 1998 as a basis for analyses. During this episode, one hour average ozone concentrations as high as 140 ppb were observed downwind of Seattle and 8 hour average concentrations reached 111 ppb. In the first phase of this study, several simulations of this period were conducted to develop the optimal meteorological model set-up, and model performance was evaluated using available meteorological and ozone observations for this period. The evaluation included comparison with an independent subset of surface wind observations that had not been used in the nudging process. In the second phase, emissions were adjusted to refl ect expected changes for the year 2015, and additional simulations were performed to a ssess potential ozone le vels in 2015. These simulations included scenarios with a specific change in the Reid Vapor Pressure (RVP) for gasoline fuels in 2015 as well as a number of more generic emission sensitivity cases.5 The results are presented primarily in terms of maximum 8-hour average ozone concentrations within the mode ling domain and at specific ai r quality monitoring stations. The 8-hour average is the rele vant regulatory form, as the 1-hour standard is no longer applicable in the Puget Sound area.

This study follows a previous Puget Sound ozone study based on a 1996 ozone event. Results of this 1996 episode showed that Puget Sound ozone levels were relatively insensitive to small changes in NOx and VOC emissions. This modeling also showed that the Puget Sound urban area is “VOC limited”. The Agency used this information in its 2001 CO/Ozone Stakeholder process and report (PSCAA, 2001). The 1998 episode chosen for this exercise represents a di fferent, more severe set of meteorological conditions for ozone formation compared to the 1996 episode. Also, significant method changes have been made since the initial 1996 episode: improvements in EPA’s mobile emission factor model (MOBILE5B vs MOBI LE6.2), a different photochemical model (CALGRID vs. CMAQ), and improvements in MM5 meteorology (Simple Soil Model vs. Noah Land Surface Model).

3 Modeling System Description

3.1 Model Domain

The Mesoscale Meteorological model Version 5 (MM5, Grell et al., 1994) was used to provide the 3-D meteorologica l field for air quality modeli ng. Three one-way nested domains with grid cell horizontal sizes of 36 km, 12 km, and 4 km were applied. The two outer domains consisted of 98x95 a nd 133x151 grid cells, respectively. The innermost domain consisted of 151x94 grid cells which extended from north of Vancouver, British Columbia to south of Salem, Oregon and from the Pacific coast on the west to the Cascade Mountai n range on the east. Vertica lly, 38 sigma layers were specified. The emission and CMAQ modeling doma in was slightly smaller than the MM5 innermost domain with 138x81 horizontal grid cells at 4 km x 4 km spacing and with 22 layers in the vertical. This 4-km CMAQ do main was the same as used in the AIRPACT2 modeling system (recently updated to AIRPACT-3; http://www.airpact-3.wsu.edu ) which is a real-time air quality forecast system developed and operated by Washington State University (WSU). The 12 km and 4 km MM5 domains are shown in Figure 1.6 Figure 1. 12 and 4 km MM5 domain (D03 and D04)

3.2 Episode Selection

The ozone episode from July 22-28, 1998 was selected as the base case. Multiple exceedances of both the 1-hour and 8-hour ozone standards occurred during this period at Washington and Oregon air quality monitoring s ites. Elevated ozone was observed at the Pack Forest, Enumclaw (Weyerhaeuse r Mill and Mud Mountain), Yelm, Lake Sammamish, Carus and Milwaukie monitoring s ites. Table 1 shows some selected data from the 1998 episode.

There were elevated temperatures over most of the period and light NW winds during the episode. As summarized by Barna et al . (2000), the typical synoptic conditions conducive to high ozone concentrations in th e region are characteri zed by the building of an upper-level ridge of high pressure over the west coast of North America and a thermal trough extending northward from California along the coast. This pattern is apparent in the surface weather map for July 27, 1998 as shown in Figure 2.7 Thus, this episode was selected for analysis because the meteorology for the period appears to be generally representative of c onditions that are typical for ozone episodes in the region and because elevated ozone concentrations occurred at multiple sites. This period was previously modeled as a base cas e for a State Implementation Plan analysis conducted for the Portland, OR/Vancouver WA ar ea, and so there was a substantial data base of meteorological observations and emissions available for the period.

Figure 2. Surface weather map on Monday, July 27, 1998.8 Table 1. Selected peak ozone concentrati ons during the July 22-28, 1998 episode (ppm)

Site Date 1hr_obs 8hr_obs

Pack_Forest_WA 26-Jul 0.126 0.101

Pack_Forest_WA 27-Jul 0.140 0.109

Pack_Forest_WA 28-Jul 0.102 0.086

Mud_Mt_WA 26-Jul 0.115 0.092

Mud_Mt_WA 27-Jul 0.135 0.110

Mud_Mt_WA 28-Jul 0.135 0.111
Weyerhaeuser_Mill_WA 26-Jul 0.123 0.100
Weyerhaeuser_Mill_WA 27-Jul 0.128 0.106
Weyerhaeuser_Mill_WA 28-Jul 0.133 0.105
Se_Yelm_WA 26-Jul 0.090 0.080
Se_Yelm_WA 27-Jul 0.126 0.095
Se_Yelm_WA 28-Jul 0.105 0.085
Lake_Sammamish_WA 26-Jul 0.111 0.090
Lake_Sammamish_WA 27-Jul 0.100 0.087
Lake_Sammamish_WA 28-Jul 0.114 0.089
Carus_OR 26-Jul 0.136 0.117
Carus_OR 27-Jul 0.133 0.100
Carus_OR 28-Jul 0.137 0.098
Milwaukie_OR 26-Jul 0.124 0.095 *bold numbers indicate exceedances of the O3 standard (120 ppb for 1-hr and 80 ppb for 8-hr)

3.3 Meteorology Modeling Methods

MM5 version 3.7.2 was used to develop the 3-D meteorology fields. The MM5 run was initialized using NCEP Eta model analysis ar chived on a 40 km grid at every 3 hrs. Analysis nudging was applied in the coarse domains (36 km, 12 km) and observational nudging was applied in the inner 4 km domai n. Nudging is a numerical process that adjusts the numerical meteorological fields to ward available observations or analysis fields. For analysis nudging, 3D nudging wa s employed for winds, temperature, and moisture, but the boundary layer was excluded for nudging of temperature and moisture. Surface observational nudging was applied for winds, but not temperature and moisture. Wind speed and direction observations from about 50 surface stations were used for observational nudging of the winds in the inner 4 km domain. Data from a small number of surface stations (mostly Ecology sites) we re withheld from the nudging analysis so that an independent evaluation could be conducted at these sites. For all runs, land use data were derived from a 1 km USGS global terrain and land use database.

The MM5 physics options used in the run in cluded use of the Kain-Fritsch cumulus parameterization scheme (Kain and Fritsc h, 1993), the MRF PBL scheme (Hong and Pan, 1996), Reinser 1 moisture, CCM2 radiation, and Noah Land Surface Model (Chen and Dudhia, 2001). Several simulations were c onducted using different physics options and model setup configurations to obtain an optimal result for the air quality analysis.9 Output from MM5 was processed by the Me teorology-Chemistry Interface Processor (MCIP) so that the meteorological data coul d be used for air quality modeling. MCIP deals with reconstructing data on different grid and layer structures, converting data format and units, diagnostically estimating additional required fields, as well as extracting data for window domains. Duri ng the MCIP run, all pl anetary boundary layer (PBL) parameters and radiation fields we re passed through from MM5 without recalculation.

3.4 Meteorology Modeling Results

Surface temperature contours are shown in Figure 3 in the afternoon of July 27, 1998. Above-normal temperatures (close to 35 ° C) were predicted over large areas covering Portland and Salem in Oregon. In the Puget S ound area, elevated temperatures were also predicted but to a slightly lesser extent. Surface wind patterns are shown in Figure 4 for the same time period. The model predicted northwesterly winds in northern Washington. After reaching the Puget Sound area, the winds flowed mainly from the north along the I5 corridor, which later backed to northw esterly and moved through the Portland area.

Predicted surface temperature and wind speed were compared with observations as shown in Figure 5 at the Sea-Tac airport and in Portland (Figure 6) from July 26 to 28, 1998. Wind directions were compared (Figure 7 and 8) at the same monitoring sites. At Sea-Tac, the model appeared to overpredic t both the daytime and nighttime temperatures by a few degrees. The observed winds were mo stly below 4 m/s, and the model generally showed good agreement with the observations , except for some underprediction on July 26. Observed winds shown as zero m/s in Fi gures 5 and 6 are actually winds that are no more than 1.5 m/s, the minimum detected wind speed at the airports. Thus, the comparison between observed and predicted wind speeds may actually be better than presented in figures 5 and 6. For wind direc tion, the model captured the trend most times but did miss the westerly flow around noon on July 27 and July 28 which might explain the underprediction of ozone leve ls at Enumclaw on these two days as described in later sections. At Portland, relati vely low wind speed and above -normal temperatures also occurred, and the model simulated both surf ace temperature and winds fairly well.10 Figure 3. Surface temperature pattern from MM5 for 16:00 PDT on July 27, 1998.

Figure 4. Surface wind pattern from MM5 for 16:00 PDT on July 27, 1998.11 Figure 5. Surface temperatures and wind sp eed predicted by MM5 compared with observations at the Sea-Tac airport.

Figure 6. Surface temperatures and wind sp eed predicted by MM5 compared with observations at Portland. SEA

0 5 10 15 20 25 30 35 40 45

7/26/98 0:00 7/27/98 0:00 7/28/98 0:00 7/29/98 0:00 7/30/98 0:00

TimeTemperature ( degree C)

0 2 4 6 8 10 12 14 16 18Wind speed (m/s) T_obs T_pre WS_o Ws_p_10m

PDX

0 5 10 15 20 25 30 35 40 45

7/26/98 0:00 7/27/98 0:00 7/28/98 0:00 7/29/98 0:00 7/30/98 0:00

TimeTemperature ( degree C)

0 2 4 6 8 10 12 14 16 18Wind speed (m/s) T_obs T_pre WS_o Ws_p_10m12 Figure 7. Surface wind direction predicted by MM5 compared with observations at the Sea-Tac airport. Wind calms (no directio n) are represented by green circles.

Figure 8. Surface wind direction predicted by MM5 compared with observations at Portland. Wind calms (no direction) are represented by green circles. SEA

0 90 180 270 360

7/26/98 0:00 7/27/98 0:00 7/28/98 0:00 7/29/98 0:00 7/30/98 0:00

TimeWind direction (degree)

Wd_o Wd_p
Wd_o_calm

PDX

0 90 180 270 360

7/26/98 0:00 7/27/98 0:00 7/28/98 0:00 7/29/98 0:00 7/30/98 0:00

TimeWind direction (degree)

Wd_o Wd_p
Wd_o_calm13 MM5 predicted planetary boundary layer (PBL) heights on the three peak ozone days are plotted in Figure 9 for the afternoon hours. Over western Washington, the model predicted PBL heights were mostly below 900 m on July 26. On July 27 and 28, there were obvious increases of predicted PBL height over the region compared to the first day. Especially on July 28, the predicted boundary layer heights exceeded 1300 m over the Seattle urban area, which appeared to be hi gher than the typical PBL heights observed in the region. This might also cause the underpre diction of ozone levels at Enumclaw on that day.

Vertical profiles of temperature, wind speed, and wind direction were also compared with the upper air soundings at Quillayute, WA and Salem, OR at 17 PDT on July 28 as shown in Figure 10. At Quillayute, the model captured the vertical temperature profile and the shallow mixed layer fairly well. The model tended to slightly overestimate the wind speed below 2500 m and showed some underprediction above that. For wind direction, data were missing below 800 m. Howe ver, it appeared that the model tended to predict northwesterly flow in contrast to the observed westerly flow around 800 m. At Salem, the model predicted a slightly cooler boundary layer, but did capture the mixing height well. There was good agreement in wi nd direction but some underestimations in wind speed.14 9a

9b 9c

Figure 9a-c. Predicted PBL heights by MM5 on July 26-28, 1998 (shown as GMT).15 Figure 10a-f. Observed and predicted vertical profiles of temperature, wind speed, and wind direction at Quillayute, WA and Salem, OR. 10a . Quillayute July 28, 1998 17:00 PDT

0 500 1000 1500 2000 2500 3000 3500 4000 4500

0 10 20 30
Temp (degree C)Height (m) obs

pre

10c . Quillayute July 28, 1998 17:00 PDT

0 500 1000 1500 2000 2500 3000 3500 4000 4500

0 90 180 270 360

Wd (degree)Height (m) obs

pre 10b . Quillayute July 28, 1998 17:00 PDT

0 500 1000 1500 2000 2500 3000 3500 4000 4500

0 5 10 15

Ws (m/s)Height (m) obs

pre 10d . Salem July 28, 1998 17:00 PDT

0 500 1000 1500 2000 2500 3000 3500 4000 4500

0 10 20 30 40 50
Temp (degree C)Height (m) obs

pre

10e . Salem July 28, 1998 17:00 PDT

0 500 1000 1500 2000 2500 3000 3500 4000 4500

0 5 10 15 20

Ws (m/s)Height (m) obs

pre

10f . Salem July 28, 1998 17:00 PDT

0 500 1000 1500 2000 2500 3000 3500 4000 4500

0 90 180 270 360

Wd (degree)Height (m) obs

pre16 The meteorological simulations were also ev aluated using the mean bias, mean gross error, and index of agreement for wind sp eed, direction, temperature, and relative humidity (shown in Table 2).

The index of agreement is defined as:



## ∑ ∑

= =

− +

− −

− = N

i i i N

i i i

O O O P O P

I

1 2 1 2

|) | | (| ) ( 1 (1)

An index of agreement value of 1 indicates perfect agreement between model prediction and observation, and a value of 0 means no co rrelation between the two. The mean bias is calculated as:



# ∑

= − = N

i i i O P N MB

1 ) ( 1 (2)

and the mean gross error is calculated as: 

# ∑

= − = N

i i i O P N ME

1 | | 1 (3)

where N= sample size, P = pr ediction, and O = observation.

Ecology network sites were selected as independent sites and withheld from observational nudging for surface winds. The loca tions of the surface stations are shown in Figure 11. For all these va riables, the index of agreement was close or above 0.8, indicating good agreement betw een model and observations. The model performance for wind speed was relatively good in terms of mean bias and mean gross error. Wind speed model performance may be affected by sensit ivity levels at different monitoring sites designating low wind speeds as 0 m/s. As fo r wind direction, mean gross errors around
50 ° were reasonable values for MM5 simulations in the western US. The statistics for wind direction and wind speed were calculate d separately for the overall stations and those withheld from observational nudging; th ese results show that model performance was similar for these two groups of stations. This might be due to the relative low density of the surface stations available for observational nudging in the region.17 Figure 11. Surface stations used in MM5 observational nudging. The stations in dark red were used in nudging. The sites in blue were the independent sites withheld from nudging.

Table 2. MM5 performance stat istics for July 1998 episode

units Mean Bias Mean Gross Error Index of Agreement Temp ° C 1.9 3.3 0.74 Wspd* m/s 0.0 1.2 0.82
Wdir* ° 6.6 49.4 0.94
RH % -2.1 12.5 0.96 Wspd** m/s 0.2 1.3 0.78
Wdir** ° 15.2 51.8 0.95 * includes all stations ** only includes stations th at withheld from nudging18 3.5 Photochemical Modeling Methods and Boundary Conditions

The U.S. EPA Models-3 Community Multi-sca le Air Quality (CMA Q) Modeling System (Byun and Ching, 1999) version 4.5 was used for photochemical air quality modeling. Based on state-of-science techniques, CMAQ is a multi-scale, multi-pollutant air quality model that simulates the transportation, transformation, and deposition of atmospheric pollutants including photochemical precursor s and oxidants, particulate matter, and airborne toxics. It incorpor ates the output fields from the MM5/MCIP meteorological simulations and emissions deri ved from the Sparse Matrix Operator Kernel Emissions Modeling System (SMOKE) (http://cf.unc.edu/ cep/empd/products/smoke/index.cfm), and then simulates the chemical transport using the CMAQ Chemical Transport Model (CCTM).

The SAPRC99 photochemical mechanism in cluding aqueous chemistry and aerosol dynamics was employed. The new aerosol m odule (aero4) is differe nt from previous versions in that it includes emissions a nd thermodynamics of sea-salt. The chemical speciation of the emission inventory wa s performed by SMOKE according to the SAPRC99 mechanism. The Euler Backward In teractive (EBI) solver was used to solve the chemical kinetic equations. The new global mass-conserving scheme (yamo) was used as the advection scheme. This sche me uses the Piecewise Parabolic Method (ppm) in horizontal and drives the vertical veloci ty at each grid cell from the continuity equation. Multiscale and eddy diffusion are us ed as horizontal and vertical diffusion algorithms, respectively. The first two days of the episode were run as model spinup to minimize the effects of initial conditions.

CMAQ boundary conditions were extracted from the MOZART-2 global atmospheric chemistry-transport model by matching the CMAQ boundary with the MOZART cells. The July average MOZART-2 values were de rived by averaging over ten years of July runs (1990-1999) based upon a long term clim ate simulation conducted as part of a separate analysis of the impact of global change on regional air quality at WSU (Avise et al., 2006). Since MOZART-2 and CMAQ use different vertical grid structures, MOZART and CMAQ were first co nverted to equivalent pressu re coordinates, and then MOZART mixing ratios were linearly interpolated to CMAQ vertical layers (personal communication, Jeremy Avise).19 4 1998 Base Case Photochemical Modeling

4.1 Results The locations of the ozone monitoring site s within the modeling domain are shown in Figure 12.

Figure 12. Ozone monitoring sites within the modeling domain. (CU: Custer; RG: Ranger; OP: Olympic NP; BH: Beacon Hill; LS: Lake Sammamish; NB: North Bend; WM: Weyerhaeuser Mill ; MM: Mud Mountain; YM: Se Yelm; PF: Pack Forest; PA: Paradise; TW: Tahoma Woods; PW: Packwood; CR: Castlerock; HK: Hockinson; WD: Woodland; SI: Sauvie Is land; WR: Wishram; MV: Mt. View; MW: Milwaukie; CA: Carus; TN: Turner)

Observed and simulated 1-hr surface ozone conc entration time series are shown in Figure 13. Monitoring sites are presented in order, from north to sout h. It is important to note that the y-axes, depicting 1-hour ozone concen trations, differ between sites. At Pack20 Forest, a rural site directly east of the I-5 corridor, maximum 1-hr ozone concentrations reached 126 and 140 ppb on July 26 and July 27. The model captured both the daytime peaks and nighttime levels fairly well on two of the three high ozone days (July 26 and July 28), but missed the ozone peak on July 27. At Yelm, another rural site slightly northwest of Pack Forest, the observed and pr edicted ozone patterns we re very similar to those at Pack Forest.

At Weyerhaeuser Mill and Mud Mountain, the two Enumclaw sites, multiple exceedances of the 1-hr and 8-hr ozone standa rds were recorded. The model was able to predict the elevated ozone levels on the fi rst high ozone day (Jul y 26), but failed to capture the maximum on the next two days.

North Bend and Lake Sammamish are located along the I-90 corridor, with the latter close to the junction of I-5 and I-90. Peak 1-hr ozone levels around 100 ppb were observed at those two sites. The model also performed well on the first peak ozone day, but tended to miss the maximum on the next tw o days. The model also had difficulty in capturing the nighttime low ozone concentrations. At Beacon Hill, about five miles southeast of downtown Seattle, maximum obser ved ozone levels were lower than 60 ppb during the entire episode. Mode l performance was generally good for this site for both day and nighttime levels although there was some overprediction on the first peak ozone day and underprediction on the next two days.

Tahoma Woods, Paradise, and Pack wood are all located in areas of complex terrain. At Tahoma Woods, the model showed consistent ov erprediction of ozone. At Paradise, an average level around 50 ppb were observed w ith peaks occasionally reaching 90 ppb. The model appeared to match the average leve l, but missed the variations. At Packwood, observed ozone levels were less than 60 ppb during the entire episode, and the model prediction showed poor correla tion with the observations.

There are three sites north of the Seattle area: Olympic National Park, Ranger, and Custer. Custer is close to I-5 and the Ca nadian border. The model predicted daytime peaks fairly well, but tended to overestimate th e nighttime levels. At Ranger, a rural site further east of I-5 corridor, the model cap tured the daytime peaks but overpredicted nighttime levels significantly. At Olympic National Park, observed ozone concentrations were below 30 ppb during most of the time ex cept for one peak of 60 ppb on July 26. The model tended to capture the daytime peaks correctly at this site, but was unable to produce the observed diurnal variations.

For the Portland area, a total of nine monitoring sites were involved. At the sites where high and moderate high levels of ozone we re observed (Carus, Milwaukie, Woodland, Sauvie Island, Castlerock, Mt View, and Turn er), the model had very good performance except for missing peaks at Carus and Milwauki e on the first high ozone day. At the two sites that are influenced heavily by comple x terrain (Hockinson and Wishram), the model captured the trend at Wishram correctly, but missed the peaks at Hockinson on high ozone days.21 Figure 13. Observed and simulated 1-hr surface ozone concentrations for July 1998 episode. Monitoring sites presented from north to south. Y-axes differ between sites. (Red lines show observations and bl ue lines show model predictions.)22232425 4.2 CMAQ Statistics Evaluation

CMAQ model performance was evaluated using the following set of ozone statistics based on the simulations from July 22 to 28.

1) Normalized bias and normalized gross e rror between observed and predicted 1-hr and 8-hr ozone concentrations. Only hour s with observations greater than 40 ppb were used in evaluation. (Shown in Table 3) 2) Normalized bias between observed and predicted 8-hr daily maxima using spatially paired means. Only hours with observations greater than 40 ppb were used in evaluation. (Shown in Table 4) 3) Normalized bias between observed and pred icted 8-hr daily maxima using all data pairs. Only hours with observations great er than 40 ppb were used in evaluation. (Shown in Table 5) 4) Scatter plots of observed and predicted 8- hr daily maxima using spatially paired means. (Shown in Figure 14) 5) Quantile-Quantile plots of observed and pr edicted 8-hr daily maxima using all data pairs (Shown in Figure 15)

Statistics were calculated for matched obser vations and predictions using the exact grid where the monitoring site was located and also using the maximum value from a 5 x 5 set of grids centered on the monito ring site. The latter comparison is recommended as a way to account for typical errors in wind dire ction from the meteorological model.26 The normalized gross error is defined as:



## ∑

= −

= N

i i i i O O P

N NE

1 | | 1 (4)

and the normalized bias is defined as:



## ∑

= −

= N

i i i i O O P

N NB

1 1 (5)

where N = sample size, P = prediction, and O = observation.

EPA suggested guidelines for ozone mode ling (originally based upon 1 hr ozone concentrations) require that nor malized bias to be within ± 15% and normalized gross error to be within 35% (EPA, 1999). The ove rall mean normalized bias and error (as shown in Table 3) for 1-hr ozone was -3% a nd 21% respectively, which are well within the guidelines. When looking at the sites wh ere high ozone levels were observed, the model performance was very good at Pack Fore st (8-hr NB=3%, NE= 15%). At the two Enumclaw sites (Weyerhaeuser Mill and Mud Mountain), the model generally showed some underpredictions with normalized bias within -10% and normalized error within 25%. At the sites heavily influenced by co mplex terrain such as Paradise, poor model performance occurred with larger bias and error.

EPA suggested guideline for normalized bias between mean observed and predicted 8-hr daily maxima is within 20% (EPA, 1999). As shown in Table 4, the model performance meets this requirement at most monitoring locations. Again the model performance was good at Pack Forest, and showed negative bi as around -15% at the two Enumclaw sites when looking at the exact grid. When taki ng into account the surrounding grids, the bias was reduced to within -10%. When looking at each individual high ozone day as shown in Table 5, the bias is around -10% at Pack Forest for all three days, indicating close matching of the ozone peaks. However, the model did show some large underpredictions at the Weyerhaeuser Mill and Mud Mountain site s, especially on July 28 with bias around -40%. This could be related to the fact th at MM5 didn’t capture the westerly flow around noon and possible overpredictions of PBL heights on that day.

A scatter diagram of average observed and simu lated 8-hr daily maxima (paired in space) is shown in Figure 14. When plotted w ith the surrounding 5x5 grid points, the model tended to slightly underpredict the peaks and overpredict the low concentrations. When looking at the exact grid cell, the model show ed a slightly larger degree of scatter.

A Quantile-Quantile plot (Figure 15) is also shown for the observed and simulated 8-hr daily maxima (independent of location) whic h compares the ranked distributions of the observed and predicted ozone concentrations . Figure 15 presents both the 8-hour value from the exact grid cell (1x1), as well as the maximum 8-hour valu e from the surrounding 5x5 grid. The plots showed generally good agreement between the observed and27 predicted concentration distri butions, but there was some underprediction of the peak ozone and overprediction of lower ozone around 40 ppb.

Table 3. Mean Normalized Bias and Error ca lculated by obs ozone gr eater than 40 ppb.

1hr_bias 1hr_error 8hr_bias 8hr_error
Beacon_Hill_WA -3% 32% Castlerock_WA 3% 16% 4% 11%
Custer_WA -1% 19% 6% 19%
Hockinson_WA -12% 23% -8% 15% Lake_Sammamish_WA -13% 20% -9% 20%
Mt_View_WA -20% 23% -15% 18%
Paradise_WA -26% 28% -24% 26%
Tahoma_Woods _WA 36% 38% 47% 47%
Mud_Mt_WA -4% 25% -1% 22%
NorthBend_WA -1% 13% 5% 10% OlympicNP_WA 4% 5% 19% 19%
Pack_Forest_WA 0% 17% 3% 15%
Packwood_WA -19% 25% -10% 19% Ranger_WA 8% 11% Se_Yelm_WA -6% 27% -6% 24% Weyerhaeuser_Mill_WA -10% 25% -6% 22%
Wishram_WA -15% 18% -13% 15%
Woodland_WA 12% 21% 11% 14% Carus_OR -9% 25% -9% 21%
Milwaukie_OR -3% 21% -2% 25%
Sauvie_Island_OR 13% 20% 15% 19%
Turner_OR 0% 19% 6% 16% average -3% 21% 1% 20%28 Table 4. Normalized Bias between average observed and predicted 8-hr daily maxima (ppm) using 1x1 and 5x5 grids. (Only hours with observations grea ter than 40 ppb were used.)

Site Ave Obs 8-hr daily max Ave Pre 8-hr daily max 1x1 Ave Pre 8-hr daily max 5x5 NB (Ave 8-hr daily max 1x1) NB(Ave 8-hr daily max 5x5)
Beacon_Hill_WA
Castlerock_WA 0.071 0.071 0.078 1% 11%
Custer_WA 0.053 0.055 0.063 3% 19%
Hockinson_WA 0.069 0.057 0.064 -17% -7%
Lake_Sammamish_WA 0.075 0.064 0.072 -14% -3%
Mt_View_WA 0.068 0.055 0.076 -19% 11%
Paradise_WA 0.061 0.043 0.050 -29% -18%
Tahoma_Woods _WA 0.048 0.081 0.093 71% 95%
Mud_Mt_WA 0.089 0.077 0.081 -14% -9%
NorthBend_WA 0.066 0.062 0.069 -6% 3% OlympicNP_WA 0.046 0.055 0.060 19% 30%
Pack_Forest_WA 0.082 0.078 0.081 -4% 0%
Packwood_WA 0.050 0.055 0.060 11% 21% Ranger_WA
Se_Yelm_WA 0.071 0.065 0.072 -8% 2% Weyerhaeuser_Mill_WA 0.088 0.076 0.081 -14% -8%
Wishram_WA 0.059 0.052 0.055 -13% -8%
Woodland_WA 0.064 0.070 0.072 10% 12%
Carus_OR 0.090 0.079 0.085 -13% -5%
Milwaukie_OR 0.066 0.064 0.070 -2% 7%
Sauvie_Island_OR 0.060 0.068 0.071 14% 19%
Turner_OR 0.068 0. 069 0.072 2% 6% average 0.067 0.065 0.071 -1% 9%29 Table 5. Normalized Bias between observed and predicted 8-hr daily maxima (ppm) using all data pairs based on 5x5 and 1x1 grids. (Only hours with observations gr eater than 40 ppb were used and only data from July 26 to 28 were listed here.)

Site Date 8hr_obs 8hr_max_1x18hr_max_5x5 NB (8-hr daily max 1x1) NB(8-hr daily max 5x5) Beacon_Hill_WA 26-Jul 0.037 0.056 0.078 Beacon_Hill_WA 27-Jul 0.033 0.027 0.056 Beacon_Hill_WA 28-Jul 0.033 0.027 0.043 Castlerock_WA 26-Jul 0.077 0.072 0.083 -6% 7% Castlerock_WA 27-Jul 0.077 0.083 0.090 7% 17% Castlerock_WA 28-Jul 0.072 0.069 0.074 -4% 2%

Custer_WA 26-Jul 0.057 0.051 0.059 -11% 3% Custer_WA 27-Jul 0. 067 0.070 0.081 4% 21% Custer_WA 28-Jul 0. 057 0.072 0.075 28% 33% Hockinson_WA 26-Jul 0.070 0.059 0.065 -16% -7% Hockinson_WA 27-Jul 0.083 0.059 0.070 -28% -15% Hockinson_WA 28-Jul 0.082 0.055 0.065 -32% -21% Lake_Sammamish_WA 26-Jul 0.090 0.083 0.090 -8% 0% Lake_Sammamish_WA 27-Jul 0. 087 0.063 0.078 -27% -11% Lake_Sammamish_WA 28-Jul 0. 089 0.058 0.062 -34% -30% Mt_View_WA 26-Jul 0. 071 0.050 0.073 -29% 2% Mt_View_WA 27-Jul 0. 078 0.067 0.090 -15% 16% Mt_View_WA 28-Jul 0. 076 0.059 0.080 -22% 5% Paradise_WA 26-Jul 0.055 0.043 0.045 -22% -19% Paradise_WA 27-Jul 0.071 0.038 0.045 -47% -37% Paradise_WA 28-Jul 0.071 0.042 0.044 -41% -38% Tahoma_Woods _WA 26-Jul 0.051 0.081 0.092 59% 80% Tahoma_Woods _WA 27-Jul 0.044 0.082 0.094 85% 111% Tahoma_Woods _WA 28-Jul 0.015 0.061 0.073 Mud_Mt_WA 26-Jul 0.092 0.089 0.091 -3% -2% Mud_Mt_WA 27-Jul 0.110 0.083 0.084 -25% -24% Mud_Mt_WA 28-Jul 0.111 0.058 0.067 -48% -39% NorthBend_WA 26-Jul 0.073 0.070 0.078 -5% 7% NorthBend_WA 27-Jul 0. 078 0.063 0.073 -20% -6% NorthBend_WA 28-Jul 0.061 0.058 0.061 -4% 0% OlympicNP_WA 26-Jul 0.046 0.055 0.060 19% 30% OlympicNP_WA 27-Jul 0.021 0.033 0.039 OlympicNP_WA 28-Jul 0.019 0.025 0.029 Pack_Forest_WA 26-Jul 0.101 0.092 0.093 -9% -8% Pack_Forest_WA 27-Jul 0. 109 0.094 0.096 -13% -12% Pack_Forest_WA 28-Jul 0. 086 0.076 0.077 -11% -10%

Packwood_WA 26-Jul 0.047 0.041 0.047 -12% -1% Packwood_WA 27-Jul 0.051 0.051 0.057 -1% 13% Packwood_WA 28-Jul 0.052 0.050 0.055 -4% 5%

Ranger_WA 26-Jul 0.032 0.044 0.052 Ranger_WA 27-Jul 0.030 0.053 0.061 Ranger_WA 28-Jul 0.023 0.052 0.061 Se_Yelm_WA 26-Jul 0.080 0.085 0.091 7% 14% Se_Yelm_WA 27-Jul 0.095 0.067 0.075 -29% -21%30 Table 5 cont. Normalized Bias between obser ved and predicted 8-hr daily maxima (ppm) using all data pairs based on 5x5 and 1x1 grids.

Se_Yelm_WA 28-Jul 0.085 0.057 0.064 -33% -25% Weyerhaeuser_Mill_WA 26-Ju l 0.100 0.089 0.091 -10% -9% Weyerhaeuser_Mill_WA 27-Jul 0.106 0.083 0.084 -22% -21% Weyerhaeuser_Mill_WA 28-Jul 0.105 0.057 0.068 -46% -36% Wishram_WA 26-Jul 0.060 0.047 0.049 -23% -18% Wishram_WA 27-Jul 0.060 0.047 0.050 -22% -18% Wishram_WA 28-Jul 0.061 0.056 0.060 -7% 0% Woodland_WA 26-Jul 0.069 0.068 0.069 -1% -1% Woodland_WA 27-Jul 0.089 0.091 0.093 2% 5% Woodland_WA 28-Jul 0.054 0.065 0.068 21% 27%

Carus_OR 26-Jul 0.117 0.066 0.071 -44% -39% Carus_OR 27-Jul 0.100 0.089 0.100 -11% 0% Carus_OR 28-Jul 0.098 0.104 0.108 7% 10% Milwaukie_OR 26-Jul 0.095 0.061 0.064 -36% -33% Milwaukie_OR 27-Jul 0.085 0.090 Milwaukie_OR 28-Jul 0.074 0.081 0.091 9% 23% Sauvie_Island_OR 26-Jul 0.074 0.069 0.070 -7% -5% Sauvie_Island_OR 27-Ju l 0.078 0.092 0.093 18% 20% Sauvie_Island_OR 28-Ju l 0.063 0.071 0.073 13% 16% Turner_OR 26-Jul 0.076 0.064 0.069 -16% -9% Turner_OR 27-Jul 0. 098 0.100 0.102 2% 4% Turner_OR 28-Jul 0. 078 0.082 0.084 5% 7%31 Figure 14. Scatter plot of the average obser ved and predicted 8-hr daily maxima for 5x5 (maximum 8-hour value) and 1x1 grids for July 1998 episode.

Figure 15. Quantile-Quantile pl ot of observed and predicted 8-hr daily maxima using all data pairs for 5x5 (maximum 8-hour valu e) and 1x1 grids for July 1998 episode. average observed and predicted 8-hr maxima

0 0.02 0.04 0.06 0.08 0.1 0.12

0 0.02 0.04 0.06 0.08 0.1 0.12
observed O3 (ppm)predicted O3 (ppm)

1x1

5x5

1:1 line

Q-Q: 8-hr daily maxima (all data pairs)

0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140

0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140
observed O3 (ppm)predicted O3 (ppm)

1x1

5x5

1:1 line32 5 Sensitivity Analyses

Sensitivity analysis was conducted to furthe r understand the ozone formation in the region. Similar to the 1996 ozone report, we examined the perturbation in the maximum 8-hr ozone concentration due to the cha nges in emissions. First, changes in anthropogenic VOC and/or NOx were applied. The combinations analyzed included:

• 25% decrease in NOx only (VOC constant)

• 25% increase on NOx only (VOC constant)

• 25% decrease in VOC only (NOx constant)

• 25% increase in VOC only (NOx constant)

• 25% decrease in both VOC and NOx

• 25% decrease in VOC combined with a 25% increase in NOx

Second, changes from removing individual em ission source categories were examined. The source categories included biogenic, point, nonroad, nonroad NOx, nonroad VOC, onroad, onroad NOx, and onroad VOC.

The effects upon maximum 8-hr ozone from changing anthropogenic VOC and/or NOx are plotted in Figure 16 for July 26 to July 28. In these plots, the x-axis shows the fractional change in VOC and/or NOx emissions . One exception is that for the run with 25% increase in NOx and combined 25% decr ease in VOC, the x-axis of the point was plotted according to the changes in VOC only (s hown as green triangles). The results are also summarized as shown in Table 6.

The overall trend was similar on all three high ozone days. Increasing or decreasing VOC emissions alone resulted in linear chan ges in maximum ozone concentrations on July 27 and 28. The pattern on July 26 was slig htly different in that the increase in ozone was greater compared to the other two days. Increasing NOx emissions by 25% decreased ozone concentrations by 2% to 5%. Decreasing NOx emissions by 25% resulted in increases of ozone by 5% to 12% with the maximum changes on July 26. Reducing VOC and NOx both by 25% resulted in relatively small changes ranging from - 1% to -5%. Increasing NOx emissions by 25% and simultaneously decreasing VOC emissions by 25%, produced maximum 8-hr ozone concentrations that were similar to the pattern obtained by decreasing VOC only on July 26 and 28. On July 27, the change in the 8-hr maximum was -13%, which is clos e to combining the changes from increasing NOx only and decreasing VOC only.

The results obtained by removing each indivi dual source category ar e also shown in Table 6. Within all source categories, bioge nic emissions appeared to have the largest effect on reducing maximum 8-hr ozone concentrations. This is because of the large total mass emissions of VOC emissions from bioge nic sources. In cont rast, removing point sources resulted in very small changes. Wh en anthropogenic area sources were removed, the ozone reductions were around 8%. Rem oving nonroad or onroad mobile emissions resulted in up to 13% and 10% reductions. If only VOC emissions were removed from nonroad or onroad sources, the ozone reducti ons were up to 14%. However, when only33 NOx emissions were removed from nonroad or onroad sources, larg e increases (up to 13%) were predicted. This increase of ozone could be explained by the lack of titration effects from NO emissions.

Figure 16a-c. Sensitivity curves of maximum 8-hr ozone downwind of Puget Sound to changes in anthropogenic NOx and VOC emissions on July 26-28, 1998. 16a . Sunday: July 26, 1998

80 90 100 110 120

0.5 0.75 1 1.25 1.5
fractional change in emissionspeak ozone (ppbV)

NOx

VOC

NOx&VOC
1.25NOx_0.75VOC

16b . Monday: July 27, 1998

80 90 100 110 120

0.5 0.75 1 1.25 1.5
fractional change in emissionspeak ozone (ppbV)

NOx

VOC

NOx&VOC
1.25NOx_0.75VOC

16c . Tuesday: July 28, 1998

60 70 80 90 100

0.5 0.75 1 1.25 1.5
fractional change in emissionspeak ozone (ppbV)

NOx

VOC

NOx&VOC
1.25NOx_0.75VOC34 Table 6. Predicted maximum 8-hr ozone (ppm) downwind of Puget Sound and the percentage change from the 1998 base run by changing VOC and/or NOx emissions as well as removing individual source categories.

8-hr Fractional change in emissions 26-Jul 27-Jul 28-Jul ppb ppb ppb
Base 1 0.093 0.096 0.077
NOx 0.75 0.104 0.101 0.083
VOC 0.75 0.085 0.087 0.070
NOx&VOC 0.75 0.091 0.091 0.076

VOC 1.25 0.106 0.105 0.084
NOx 1.25 0.091 0.092 0.073 1.25NOx_0.75VOC 1.25NOx & 0.75VOC 0.085 0.084 0.069

No area 0.088 0.088 0.071 No biogenic 0.075 0.075 0.053
No on-road 0.088 0.084 0.070
No on-road VOC 0.088 0.083 0.067
No on-road NOX 0.105 0.098 0.082
No non-road 0.084 0.086 0.074
No non-road VOC 0.080 0.085 0.070
No non-road NOX 0.103 0.098 0.082 No point 0.095 0.095 0.076

% change from 1998 base
NOx 0.75 12% 5% 8%
VOC 0.75 -9% -9% -9%
NOx&VOC 0.75 -2% -5% -1%

VOC 1.25 14% 9% 9%
NOx 1.25 -2% -4% -5% 1.25NOx_0.75VOC 1.25NOx & 0.75VOC -9% -13% -10%

No area -5% -8% -8% No biogenic -19% -22% -31%
No on-road -5% -13% -9%
No on-road VOC -5% -14% -13%
No on-road NOX 13% 2% 6%
No non-road -10% -10% -4%
No non-road VOC -14% -11% -9%
No non-road NOX 11% 2% 6% No point 2% -1% -1%35 6 Future Year (2015) Modeling

To investigate the impact of future emissions growth and possible c ontrol strategies, two future emission inventories were developed: One involved projected growth assuming the use of low Reid Vapor Pressure gasolin e fuels (low RVP run); The other used the same growth factors, but assumed the use of higher RVP gasoline fuels (high RVP run). The emission inventories were then used w ith the 1998 meteorology to simulate future conditions. The low and high RVP runs assume d gasoline vapor pressu res of 7.8 and 8.7 psi, respectively. Additional details on th e low and high RVP emission inventories are included in Appendix A to this report.

For the low RVP run, the emissions change s in VOC and NOx compared to the 1998 base run are summarized in Table 7 for Tuesda y, July 28. The table includes emissions estimates for northwest Oregon counties a nd western Washington counties included in the modeling domain. It should be noted th at the temperature adjustment for onroad mobile sources was run outside of SMOKE, and therefore, the adjustments are not
reflected in Table 7 in which emissions at 75 ° F were assumed. In western Washington, large decreases (about 65%) in VOC and NOx are expected from onroad mobile sources. At the same time, VOC emissions from area sources are projected to increase by 11%. Emissions from point sources were assumed to be same in 2015 as 1998. The largest increases are projected from nonroad sour ces where NOx emissions would increase by 33% in western Washington due to increased marine ship emissions. The overall VOC and NOx emissions are expected to decrea se 21% and 31% in western Washington.

For the high RVP run, the emissions change s in VOC and NOx compared to the low RVP run are summarized in Table 8 for Tuesday, Ju ly 28. Because the temperature effects on onroad mobile emissions, the difference might be slightly understated. In the high RVP run, only onroad and nonroad emissions are s lightly different from those in the low RVP run. The VOC emissions from onroad and nonroad sources are about 3.5% and 0.6% higher in the high RVP run comp ared to the low RVP run.

Although not included in the table below, bi ogenic emissions were assumed to be the same in 2015 as 1998. A description of the em ission inventory projections is provided in the Appendix A to this report.36 Table 7

. Anthropogenic VOC and NOx emissi ons comparison between the 2015 low RVP run and 1998 base run on Tuesday, July 28.
OR_2015 WA_2015 OR_1998 WA_1998 % change OR % change WA tons/day tons/day tons/d ay tons/day tons/day tons/day

area NOX 12.6 20.9 18 .4 20.8 -31% 0%

VOC 264.8 296.1 232.8 265.6 14% 11%

onroad NOX 59.9 163.4 206. 8 476.5 -71% -66%

VOC 43.7 138.0 154. 5 371.7 -72% -63%

nonroad NOX 95.7 452.7 123.5 339.8 -22% 33%

VOC 59.7 143.9 97.7 224.5 -39% -36% point NOX 33.3 138.7 33.3 138.7 0% 0%

VOC 26.7 46.3 26.7 46.3 0% 0%

sum NOX 201.6 775.8 382. 0 975.9 -47% -21%

VOC 394.9 624.2 511. 6 908.1 -23% -31%

Table 8

. Anthropogenic VOC and NOx emissi ons comparison between the 2015 high RVP run and 2015 low RVP run on Tuesday, July 28. (Only onroad and nonroad mobile sources in Western Washington are different and shown here.)

WA_2015_high_RVP WA_2015_low_RVP % change WA tons/day tons/day tons/day

onroad NOX 163.4 163.4 0.0%

VOC 142.8 138.0 3.5%

nonroad NOX 452.7 452.7 0.0%

VOC 144.7 143.9 0.6%

6.1 Low-RVP Run Results

Ozone surface contour plots are plotted in Figures 17-19 from July 26 to 28 for the 2015 low RVP run. For comparison, the contour maps from 1998 base run and the difference plots between the two years ar e also plotted. Daily ma ximum 8-hr ozone for 2015 lowRVP run and the relative changes from 1998 base case at each monitoring site are shown in Table 9 using 1x1 and 5x5 grids. Simulated 1-hr surface ozone time series for 2015 low RVP run and observed and predicted ozone levels during the July 1998 episode are shown in Figure 20. It is important to not e that the y-axes, depicting 1-hour ozone concentrations, differ between sites.

The difference plots show ozone reduction ba nds stretching over the Enumclaw and Pack Forest monitoring sites, where the maximum ozone occurred in the July 1998 episode.37 These ozone reduction bands are shaded in dark green in the difference plots. As a result, the maximum 8-hr concentrations are reduced by about 10 ppb at th e two Enumclaw sites and Pack Forest as shown in Table 9. Howe ver, closer to and within the Seattle urban area, increases of ozone are predicted. Th ese bands of ozone increases are shaded in yellow, orange and red in the difference plots. For example at the Beacon Hill site (Table 9) there is up to 18 ppb increase of ozone at the exact grid cell. At Yelm, which is slightly upwind of Pack Forest, up to 3 ppb oz one increase is also predicted when looking at 5x5 grids.

The regional increase of ozone in the urba n area can be explained by the increase of VOC/NOx ratios in the 2015 emissions inve ntory. The VOC/NOx ratios from surface layer emissions are plotted in Figure 21 in the afternoon of July 27 for the 2015 low RVP run and 1998 base run. In the Seattle urba n area, the VOC/NOx ratios are mostly below 4 in 1998 (as shaded in orange ), but increase to around 6 in 2015 (as shaded in green). With an increase in VOC/NOx ratio, less NO titration occurs and thus higher ozone levels can be expected.

The [O3]/[NOx] concentration ratio can also be examined to explain the increase of ozone in the urban area. The [O3]/[NOx] has be en shown to be a reliable indicator of O3 response to VOC or NOx emission changes (T onnesen and Dennis, 2000b). Values of the ratio < 15 indicate the strongly radical-l imited, VOC sensitive condition. Values > 46 indicate strongly NOx-limited a nd therefore NOx sensitive re gion (Arnold et al., 2003). The predicted [O3]/[NOx] contours in th e afternoon of 1998 and 2015 are shown in Figure 22. For 1998, the entire Seattle, Portland urban area, and I-5 corridor appear to be well into the VOC-sensitive region. In contrast, for 2015 the VOC-limited areas are smaller, which is consistent with the increased VOC/NOx ratios. With the urban area to be less VOC-limited, less NO titration of ozone would occur.38 17a

17b

17c

Figure 17a-c. Simulated ozone contour at 17 PDT on July 26 for 1998, 2015, and ozone difference contour between 1998 and 2015 (2015-1998)39 18a

18b

18c

Figure 18a-c. Simulated ozone contour at 16 PDT on July 27 for 1998, 2015, and ozone difference contour between 1998 and 2015 (2015-1998)40 19a

19b

19c

Figure 19a-c. Simulated ozone contour at 17 PDT on July 28 for 1998, 2015, and ozone difference contour between 1998 and 2015 (2015-1998)41 Table 9. Daily maximum 8-hr ozone (ppm ) for 2015 low RVP run and the relative changes from 1998 base run using 1x1 and 5x5 grids. (Ozone increases are highlighted in red.)

Site Date 1998 2015 2015-1998

8hr_max_1x1 8hr_max_5x5 8hr_max_1x1 8hr_max_5x5 8hr_max_1x1 8hr_max_5x5
Beacon_Hill_WA 26-Jul 0.056 0.078 0.066 0.080 0.010 0.003
Beacon_Hill_WA 27-Jul 0.027 0.056 0.045 0.060 0.018 0.004
Beacon_Hill_WA 28-Jul 0.027 0.043 0.039 0.050 0.013 0.007
Castlerock_WA 26-Jul 0.072 0.083 0.067 0.073 -0.005 -0.010
Castlerock_WA 27-Jul 0.083 0.090 0.076 0.082 -0.007 -0.009
Castlerock_WA 28-Jul 0.069 0.074 0.066 0.069 -0.003 -0.004

Custer_WA 26-Jul 0.051 0.059 0.043 0.050 -0.008 -0.010
Custer_WA 27-Jul 0.070 0.081 0.066 0.076 -0.003 -0.005
Custer_WA 28-Jul 0.072 0.075 0.071 0.075 -0.001 0.000
Hockinson_WA 26-Jul 0.059 0.065 0.053 0.060 -0.006 -0.006
Hockinson_WA 27-Jul 0.059 0.070 0.052 0.062 -0.007 -0.008
Hockinson_WA 28-Jul 0.055 0.065 0.054 0.062 -0.001 -0.003
Lake_Sammamish_WA 26-Jul 0.083 0.090 0.079 0.091 -0.004 0.002
Lake_Sammamish_WA 27-Jul 0.063 0.078 0.063 0.071 0.000 -0.007
Lake_Sammamish_WA 28-Jul 0.058 0.062 0.057 0.061 -0.001 -0.001
Mt_View_WA 26-Jul 0.050 0.073 0.048 0.068 -0.002 -0.005
Mt_View_WA 27-Jul 0.067 0.090 0.063 0.083 -0.004 -0.007
Mt_View_WA 28-Jul 0.059 0.080 0.058 0.080 -0.001 0.000
Paradise_WA 26-Jul 0.043 0.045 0.042 0.044 -0.001 -0.001
Paradise_WA 27-Jul 0.038 0.045 0.038 0.043 0.000 -0.002
Paradise_WA 28-Jul 0.042 0.044 0.042 0.043 0.000 0.000
Tahoma_Woods _WA 26-Jul 0.081 0.092 0.074 0.085 -0.007 -0.007
Tahoma_Woods _WA 27-Jul 0.082 0.094 0.074 0.085 -0.008 -0.009
Tahoma_Woods _WA 28-Jul 0.061 0.073 0.054 0.066 -0.007 -0.008
Mud_Mt_WA 26-Jul 0.089 0.091 0.078 0.082 -0.011 -0.009
Mud_Mt_WA 27-Jul 0.083 0.084 0.073 0.075 -0.010 -0.009
Mud_Mt_WA 28-Jul 0.058 0.067 0.053 0.060 -0.006 -0.008
NorthBend_WA 26-Jul 0.070 0.078 0.063 0.070 -0.006 -0.008
NorthBend_WA 27-Jul 0.063 0.073 0.058 0.067 -0.005 -0.007
NorthBend_WA 28-Jul 0.058 0.061 0.055 0.057 -0.003 -0.004 OlympicNP_WA 26-Jul 0.055 0.060 0.056 0.060 0.001 0.000 OlympicNP_WA 27-Jul 0.033 0.039 0.034 0.039 0.001 0.000 OlympicNP_WA 28-Jul 0.025 0.029 0.025 0.028 -0.001 -0.001
Pack_Forest_WA 26-Jul 0.092 0.093 0.084 0.086 -0.009 -0.007
Pack_Forest_WA 27-Jul 0.094 0.096 0.088 0.089 -0.007 -0.007
Pack_Forest_WA 28-Jul 0.076 0.077 0.071 0.074 -0.005 -0.003

Packwood_WA 26-Jul 0.041 0.047 0.039 0.043 -0.002 -0.003
Packwood_WA 27-Jul 0.051 0.057 0.045 0.050 -0.005 -0.007
Packwood_WA 28-Jul 0.050 0.055 0.045 0.048 -0.005 -0.006

Ranger_WA 26-Jul 0.044 0.052 0.046 0.052 0.001 -0.001
Ranger_WA 27-Jul 0.053 0.061 0.050 0.057 -0.003 -0.004
Ranger_WA 28-Jul 0.052 0.061 0.048 0.057 -0.004 -0.004
Se_Yelm_WA 26-Jul 0.085 0.091 0.074 0.079 -0.012 -0.01242 Table 9 cont. Daily maximum 8-hr ozone ( ppm) for 2015 low RVP run and the relative changes from 1998 base run using 1x1 and 5x5 grids.

Se_Yelm_WA 27-Jul 0.067 0.075 0.067 0.078 -0.001 0.003
Se_Yelm_WA 28-Jul 0.057 0.064 0.058 0.066 0.000 0.002
Weyerhaeuser_Mill_WA 26-Jul 0.089 0.091 0.080 0.083 -0.010 -0.008
Weyerhaeuser_Mill_WA 27-Jul 0.083 0.084 0.075 0.076 -0.007 -0.008
Weyerhaeuser_Mill_WA 28-Jul 0.057 0.068 0.051 0.060 -0.006 -0.007
Wishram_WA 26-Jul 0.047 0.049 0.045 0.047 -0.002 -0.003
Wishram_WA 27-Jul 0.047 0.050 0.046 0.049 -0.001 -0.001
Wishram_WA 28-Jul 0.056 0.060 0.053 0.056 -0.003 -0.004
Woodland_WA 26-Jul 0.068 0.069 0.061 0.063 -0.007 -0.005
Woodland_WA 27-Jul 0.091 0.093 0.080 0.083 -0.011 -0.010
Woodland_WA 28-Jul 0.065 0.068 0.060 0.064 -0.005 -0.004

Carus_OR 26-Jul 0.066 0.071 0.066 0.068 0.000 -0.003
Carus_OR 27-Jul 0.089 0.100 0.092 0.096 0.003 -0.004
Carus_OR 28-Jul 0.104 0.108 0.089 0.098 -0.016 -0.010
Milwaukie_OR 26-Jul 0.061 0.064 0.062 0.065 0.001 0.001
Milwaukie_OR 27-Jul 0.085 0.090 0.084 0.087 -0.002 -0.003
Milwaukie_OR 28-Jul 0.081 0.091 0.086 0.096 0.005 0.005
Sauvie_Island_OR 26-Jul 0.069 0.070 0.060 0.064 -0.009 -0.006
Sauvie_Island_OR 27-Jul 0.092 0.093 0.080 0.083 -0.012 -0.010
Sauvie_Island_OR 28-Jul 0.071 0.073 0.065 0.068 -0.006 -0.005
Turner_OR 26-Jul 0.064 0.069 0.060 0.069 -0.003 0.000
Turner_OR 27-Jul 0.100 0.102 0.083 0.086 -0.018 -0.016
Turner_OR 28-Jul 0.082 0.084 0.074 0.075 -0.009 -0.00943 Figure 20. Simulated 1-hr surface ozone concentrations for 2015 low RVP run compared to the observed the predicted ozone le vels during the July 1998 episode. (The green, red, and blue lines show 2015 low RVP run, 1998 observations, and 1998 model predictions, respectively.). Sites shown north to south. Y-axes differ between sites.4445464748 21a

21b

Figure 21a-b. VOC/NOx ratios (moleC/mole) in surface layer emissions from 1998 base run and 2015 low RVP run at 16 PDT on July 27.49 22a

22b

Figure 22a-b. O3/NOx contour plot from the 1998 base r un and 2015 low RVP run at 16 PDT on July 27.50 6.2 High-RVP Run Results

The difference plots between the 2015 high RVP run and low RVP run are plotted in Figure 23 for July 26 to 28. On all three days , the increases of ozone concentrations due to use of high RVP gasoline are only up to 1 ppb. This is expected since the changes in emissions are also fairly small. Daily maximum 8-hr ozone fo r 2015 high RVP run and the relative changes from the 2015 low RVP run at each monitoring site are shown in Table 10 using 1x1 and 5x5 grids. For individua l monitoring sites, the increase is less than 0.3 ppb.51 23a

23b

23c

Figure 23a-c. Ozone differen ce plot between the 2015 hi gh RVP and low RVP run on July 26-28, 2015. (2015 high RVP – 2015 low RVP)52 Table 10. Daily maximum 8-hr ozone ( ppm) for 2015 high RVP run and the relative changes from 2015 low RVP run using 1x1 and 5x5 grids. Only changes greater than 0.0001 ppm are shown in the difference column.

Site Date 2015 high RVP 2015 low RVP 2015 high RVP-2015 low RVP
8hr_max_1x1 8hr_max_5x5 8hr_max_1x1 8hr_max_5x5 8hr_max_1x1 8hr_max_5x5
Beacon_Hill_WA 26-Jul 0.0662 0.0804 0.0662 0.0803 0.0001
Beacon_Hill_WA 27-Jul 0.0446 0.0599 0.0446 0.0598 0.0001
Beacon_Hill_WA 28-Jul 0.0394 0.0497 0.0394 0.0496 0.0001
Castlerock_WA 26-Jul 0.0671 0.0731 0.0670 0.0729 0.0001 0.0002
Castlerock_WA 27-Jul 0.0759 0.0819 0.0757 0.0817 0.0002 0.0002
Castlerock_WA 28-Jul 0.0663 0.0691 0.0662 0.0690 0.0001

Custer_WA 26-Jul 0.0431 0.0495 0.0431 0.0495
Custer_WA 27-Jul 0.0665 0.0758 0.0665 0.0758
Custer_WA 28-Jul 0.0710 0.0753 0.0710 0.0753
Hockinson_WA 26-Jul 0.0532 0.0597 0.0531 0.0596 0.0001
Hockinson_WA 27-Jul 0.0522 0.0617 0.0521 0.0616
Hockinson_WA 28-Jul 0.0537 0.0617 0.0537 0.0616
Lake_Sammamish_WA 26-Jul 0.0789 0.0917 0.0787 0.0914 0.0002 0.0003
Lake_Sammamish_WA 27-Jul 0.0627 0.0711 0.0626 0.0710 0.0001
Lake_Sammamish_WA 28-Jul 0.0575 0.0614 0.0574 0.0613 0.0001
Mt_View_WA 26-Jul 0.0479 0.0680 0.0478 0.0679 0.0001
Mt_View_WA 27-Jul 0.0630 0.0831 0.0628 0.0829 0.0002 0.0002
Mt_View_WA 28-Jul 0.0582 0.0797 0.0581 0.0796
Paradise_WA 26-Jul 0.0422 0.0441 0.0422 0.0441
Paradise_WA 27-Jul 0.0376 0.0425 0.0376 0.0425
Paradise_WA 28-Jul 0.0420 0.0433 0.0420 0.0433
Tahoma_Woods _WA 26-Jul 0.0747 0.0848 0.0745 0.0845 0.0002 0.0003
Tahoma_Woods _WA 27-Jul 0.0742 0.0852 0.0740 0.0849 0.0003 0.0003
Tahoma_Woods _WA 28-Jul 0.0545 0.0657 0.0544 0.0656
Mud_Mt_WA 26-Jul 0.0783 0.0822 0.0781 0.0819 0.0002 0.0003
Mud_Mt_WA 27-Jul 0.0733 0.0757 0.0731 0.0754 0.0002 0.0003
Mud_Mt_WA 28-Jul 0.0526 0.0598 0.0525 0.0598
NorthBend_WA 26-Jul 0.0635 0.0699 0.0634 0.0698 0.0001
NorthBend_WA 27-Jul 0.0578 0.0666 0.0578 0.0665 0.0001
NorthBend_WA 28-Jul 0.0555 0.0569 0.0555 0.0569 OlympicNP_WA 26-Jul 0.0561 0.0602 0.0561 0.0602 OlympicNP_WA 27-Jul 0.0336 0.0386 0.0336 0.0386 OlympicNP_WA 28-Jul 0.0248 0.0284 0.0248 0.0284
Pack_Forest_WA 26-Jul 0.0838 0.0867 0.0836 0.0864 0.0003 0.0003
Pack_Forest_WA 27-Jul 0.0878 0.0894 0.0876 0.0892 0.0002 0.0002
Pack_Forest_WA 28-Jul 0.0715 0.0746 0.0713 0.0744 0.0001 0.0002

Packwood_WA 26-Jul 0.0390 0.0432 0.0390 0.0432
Packwood_WA 27-Jul 0.0452 0.0504 0.0452 0.0503 0.0001
Packwood_WA 28-Jul 0.0451 0.0485 0.0451 0.0484 0.0001

Ranger_WA 26-Jul 0.0458 0.0516 0.0458 0.0515 0.0001 Ranger_WA 27-Jul 0.0497 0.0566 0.0497 0.0566 Ranger_WA 28-Jul 0.0479 0.0568 0.0479 0.0568
Se_Yelm_WA 26-Jul 0.0738 0.0791 0.0736 0.0789 0.0001 0.000253 Table 10 cont. Daily maximum 8-hr ozone ( ppm) for 2015 high RVP run and the relative changes from 2015 low RVP run using 1x1 and 5x5 grids.

Se_Yelm_WA 27-Jul 0.0668 0.0785 0.0666 0.0782 0.0002 0.0003
Se_Yelm_WA 28-Jul 0.0577 0.0663 0.0577 0.0661 0.0002
Weyerhaeuser_Mill_WA 26-Jul 0.0799 0.0831 0.0798 0.0829 0.0002 0.0002
Weyerhaeuser_Mill_WA 27-Jul 0.0757 0.0764 0.0754 0.0761 0.0002 0.0003
Weyerhaeuser_Mill_WA 28-Jul 0.0513 0.0603 0.0513 0.0602 0.0001
Wishram_WA 26-Jul 0.0449 0.0465 0.0449 0.0465
Wishram_WA 27-Jul 0.0459 0.0488 0.0459 0.0488
Wishram_WA 28-Jul 0.0532 0.0561 0.0531 0.0560
Woodland_WA 26-Jul 0.0611 0.0634 0.0610 0.0633 0.0001 0.0001
Woodland_WA 27-Jul 0.0799 0.0835 0.0796 0.0833 0.0003 0.0002
Woodland_WA 28-Jul 0.0600 0.0638 0.0599 0.0636 0.0002

Carus_OR 26-Jul 0.0656 0.0680 0.0655 0.0679 0.0001
Carus_OR 27-Jul 0.0920 0.0957 0.0919 0.0956
Carus_OR 28-Jul 0.0887 0.0984 0.0886 0.0984
Milwaukie_OR 26-Jul 0.0623 0.0651 0.0623 0.0650 0.0001
Milwaukie_OR 27-Jul 0.0840 0.0871 0.0838 0.0869 0.0002 0.0002
Milwaukie_OR 28-Jul 0.0864 0.0963 0.0864 0.0962 0.0001
Sauvie_Island_OR 26-Jul 0.0600 0.0639 0.0599 0.0638 0.0001 0.0001
Sauvie_Island_OR 27-Jul 0.0798 0.0835 0.0796 0.0833 0.0002 0.0002
Sauvie_Island_OR 28-Jul 0.0655 0.0676 0.0654 0.0675
Turner_OR 26-Jul 0.0603 0.0688 0.0603 0.0688
Turner_OR 27-Jul 0.0829 0.0862 0.0829 0.0862
Turner_OR 28-Jul 0.0737 0.0750 0.0736 0.0748 0.0001 0.000254 6.3 Modeled Attainment Test

To analyze the potential to exceed the ozone st andard in 2015 and to take into account the uncertainty in model performance for peak ozone, calculations were completed to estimate the future design values. Due to m odel uncertainty, especially uncertainty in the meteorology simulations of wi nd direction and Planetary B oundary Layer (PBL) heights, the ozone peaks were underestimated at th e Enumclaw monitoring site on two of the three high ozone days in the 1998 baseline prediction. The un derprediction was calibrated by using the model results in a relative sense, which is to apply the relative reduction factors (RRF) between the futu re and 1998 baseline predictions.

The modeled attainment test is illustrated in Table 11. In the test, first the current design values (DVC) were calculated based on the fourth highest 8-hr daily maximum ozone concentrations from year 1996 to 2000. With in the Puget Sound area, only three ozone monitoring sites (Weyerhaeuser Mill, Lake Sammamish, and Pack Forest) have ozone data for all these five years, and therefore, th ey were used in the te st. Second, the mean 1998 baseline 8-hr maximum concentrations (MMax1998) and mean 2015 predicted 8-hr maximum concentrations (MMax2015) were calculated for the three peak ozone days (July 26-28) using the model output. Third, the relative reduc tion factor (RRF) was obtained for each monitoring site which is the ratio of the mean 2015 predicted 8-hr maximum concentration to the mean 1998 base line 8-hr maximum concentration. Last, the future design values (DVF) were estimated by multiplying the modeled relative reduction factor by the observati on-based current design value:

1998 2015

MMax MMax RRF = (6)

DVC RRF DVF

⋅ = (7)

Table 9 shows that all three monitoring sites pass the modeled attainment test and will maintain compliance with the 8-hr ozone standard for year 2015 by having future design values less than 84 ppb (EPA, 1999 and EPA, 20 05). Weyerhaeuser Mill and Pack Forest were predicted to have the same future con centrations (68 ppb) w ith relative reduction factor of 0.901 and 0.936, respectively. Smaller reduction was predicted for Lake Sammamish with relative reduction factor of 0.970 and 58 ppb for the future design value.55 Table 11. Modeled attainment te st for the Puget Sound area. (concentrations shown in ppm).

Year 4th High

Enumclaw Lake Sammamish Pack Forest

1996 0.092 0.073 0.078

1997 0.072 0.058 0.066

1998 0.081 0.063 0.086

1999 0.067 0.054 0.065

2000 0.067 0.057 0.059
Design values

1996-1998 0.082 0.065 0.077

1997-1999 0.073 0.058 0.072

1998-2000 0.072 0.058 0.070 current design value (DVC) 0.0756 0.0603 0.0730

Weyerhaeuser Mill (Enumclaw)

Day Current Design Value (DVC) 1998 Baseline 8-hr Max 2015 predicted 8-hr

max Relative Reduction Factor (RRF) Future Design Value (DVF)

1 0.091 0.083

2 0.084 0.076

3 0.068 0.06

mean 0.0756 0.0810 0.0730 0.901 0.068

Lake Sammamish

Day Current Design Value (DVC) 1998 Baseline 8-hr Max 2015 predicted 8-hr

max Relative Reduction Factor (RRF) Future Design Value (DVF)

1 0.09 0.091

2 0.078 0.071

3 0.062 0.061

mean 0.0603 0.0767 0.0743 0.970 0.058

Pack Forest

Day Current Design Value (DVC) 1998 Baseline 8-hr Max 2015 predicted 8-hr

max Relative Reduction Factor (RRF) Future Design Value (DVF)

1 0.093 0.086

2 0.096 0.089

3 0.077 0.074

mean 0.0730 0.0887 0.0830 0.936 0.068 NOTE: 1998 and 2015 values are based on the maximum grid cell of a 5x5 array centered on the grid cell containing the monitor.56 Reference

Avise, J., J. Chen, B. Lamb, C. Wiedinmyer, A. Guenther, J.-F. Lamarque, E. Salathe, C. Mass 2006. Sensitivity of regional air quality due to globally-forced boundary conditions. Submitted to Journal of Applied Meteorology

J.R. Arnold, R.L. Dennis and G.S. Tonnesen, 2003. Diagnostic eval uation of numerical air quality models with specialized am bient observations: testing the Community Multiscale Air Quality modeling system (C MAQ) at selected SOS 95 ground sites, Atmos. Environ. 37, pp. 1185–1198.

Byun, D.W., Ching, J.K.S. (Eds.), 1999. Science algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. EPA Report No. EPA600/R-99/030, Office of Research and Deve lopment, US Environmental Protection Agency, Washington, DC.

Barna, M.G., Lamb, B.K., O’Neill, S.M ., Westberg, H., Figueroa-Kaminsky, C., Otterson, S., Bowman, C. and DeMay, J., 2000. Modeling ozone formation and transport in the Cascadia region of the Pacific Northw est. Journal of Applied Meteorology 39, pp. 349–366.

Grell, G.A., Dudhia, J., Stau!er, D.R., 1994. A description of the "fifth generation Penn State/NCAR mesoscale model (MM5). NC AR Technical Note NCAR/TN-398#STR, 138

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Hong, S.-Y., and H.-L. Pan, 1996. Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 2322–2339.

Kain, J. S., and J. M. Fritsch, 1993: Conv ective parameterization for mesoscale models: The Kain–Fritsch scheme. The Representa tion of Cumulus Conv ection in Numerical Models, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 165–170.

The Puget Sound Clean Air Agency. Final Re port of the Puget Sound Clean Air Agency CO/Ozone Stakeholders Group. Prepared with Ross & Associates Environmental Consulting Ltd. June 27, 2001. http://www.pscleanair.org/ne ws/other/co_ozone_report.pdf

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Tonnesen, G.S. and Dennis, R.L., 2000. Analys is of radical propagation efficiency to assess ozone sensitivity to hydrocarbons and NOX. Part II: long-lived species as indicators or ozone concentration sensitivity . Journal of Geophysical Research 105, pp. 9227–9241.

U.S. EPA, (May 1999). Draft Guidance on the Use of Models and Other Analyses in Attainment Demonstrations for the 8-Hour Ozone NAAQS, EPA-454/R-99-004, http://www.epa.gov/scram001/guidance/guide/drafto3.pdf U.S.EPA, (Oct, 2005). Guidance on the Use of Models and Other An alyses in Attainment Demonstrations for57 the 8-hour Ozone NAAQS, EPA-454/R-05-002, http://www.epa.gov/scram001/guidance/guide /8-hour-o3-guidance-final-version.pdf

Vukovich, J. and T. Pierce, 2002. The Impl ementation of BEIS3 within the SMOKE Modeling Framework, In Proceedings of the 11thInternational Emissions Inventory Conference, Atlanta, Georgia, April 15-18, 2002. (Available online:www.epa.gov/ttn/chief/conf erence/ei11/modeling/vukovich.pdf)58 

## Appendix A

Emissions Inventory Documentation July 1998 Ozone Event Base and Projection Year

in coordination with the Washingt on State Department of Ecology59 

### Appendix A Table of Contents
Introduction................................................................................................................... .....60 Emissions Inventory Approach..........................................................................................61 Base Year Point Sources Inventory ..................................................................................65 Base Year Stationary Ar ea Sources Inventory...................................................................65 Base Year Non-road Mobile Sources Inventory................................................................66 Non-road Sources included in NONROAD model................................................66 Non-road sources not included in the NONROAD model ....................................66 Spatial and temporal allocation..............................................................................67 Base Year On-Road Mobile Sources Inventory.................................................................67
Base Year Biogenic Inventory...........................................................................................68 Future Year (2015) Emissions Projections ........................................................................68 Point Sources .........................................................................................................68 Stationary Area Sources.........................................................................................69 Non-Road Mobile Sources.....................................................................................69 On-Road Mobile Sources.......................................................................................70 Biogenic Emissions................................................................................................71 Emissions Su mmaries .......................................................................................................71 Emissions Inventory Limitations .......................................................................................73 Comparisons between Seattle and Portland Area Emissions.................................74 VOC/NOX Ratios..........................................................................75 

### Tables Table 1. Washington and Oregon Counties in Modeling domain ....................................64 Table 2. Local Non-Road Emissions Estimates Substitutions, Tons/Year.......................67 Table 3. Puget Sound VOC Emissions, Base and Future Cases.......................................72 Table 4. Puget Sound NO X Emissions, Base and Future Cases........................................73 Table 5. Ration of Seattle Area/Portland Area Emissions................................................74 Table 6: Comparison of Portland, Seattle NEI 1999, and Seattle Local Estimates..........75 Table 7: VOC/ NO X Ratios for King and Multnomah Counties, 1998 Ozone Event.......75 Table 8 VOC/ NO X Ratios for Puget Sound Area, 1998 Ozone Event ...........................76



### Figures

Figure 1. Ozone 8-hour concentratio ns at Puget Sound area monitors.............................61 Figure 2. Modeling Domain for Emissions Inventory......................................................6360 

## Introduction

The Puget Sound Clean Air Agency (The Agency) ha s contracted with Washington State University (WSU) to perform ozone modeling for a 1998 ozone event. The Agency sought this modeling to help inform how changes in ozone precursor emi ssions over the next ten years may affect ozone concentrations in the Puget Sound region.

The Agency previously contracted with WSU to perform ozone modeling for a 1996 ozone event. WSU performed this modeling using the CALGRID dispersion model. Results based on the 1996 episode showed that Puget Sound area ozone levels were relatively insensitive to reductions in NOx and VOC emissions. This modeling also showed that this area is considered “VOC limited”, which helped inform that strategies targeting VOC reductions will most effectively reduce ozone concentrations in the Puget Sound area. The Agency used this information in its 2001 CO/Ozone Stakeholder process and report to guide ozone reduction strategies. 1

Emissions changes over the next te n years will affect ozone precu rsors in the Puget Sound area. Lower vehicle emissions due to federal standard s and fleet turnover are among the largest changes expected. Partially off-setting this emissions decrease are an increase in population, a modest increase of VMT, and a large predicted increase in shipping activity (marine port and rail) in the Puget Sound area over the next ten ye ars. Investigating the potentia l effect on the ozone levels from this shipping activity increase is one of the main objectives of this modeling exercise.

The 1998 episode chosen for this exercise represen ts a different, more severe meteorology for ozone formation than the previously-modeled 1996 episode . Also, significant technological improvements have been made since modeling th e 1996 episode. For this modeli ng exercise, the Agency and the Washington State Department of Ecology (Ecology) used EPA’s latest MOBILE6.2 on-road mobile model and NONROAD non-road mobile model. WS U used the Mesoscale Meteorological version 5 (MM5) with land surface model meteorology for meteorological modeling and the Community Multi-scale Air Quality Modeling System (CM AQ) for dispersion modeling.

The Agency will use the results of this work for in ternal planning. Specifically, the Agency will use information from this modeling as a tool to predict if this region is likely to remain in ozone attainment when the changes mentioned above ar e taken into account. The Agency has requested that WSU perform modeling runs for two different fu ture year scenarios: one that includes the low Reid Vapor Pressure (RVP) agreement that refiners have voluntarily agreed to since 1999, and one that does not include this agreement. This agr eement effectively lowers the RVP of gasoline from 8.7 psi to 7.8 psi, which in turn reduces some oz one precursor emissions. The Agency would like to determine whether low RVP gasoline is an effective strategy to help keep th e region in attainment in future years.

1 Puget Sound Clean Air Agency. CO/Ozone Final Stakeholder Report. 2001. http://www.pscleanair.org/news/other/co_ozone_report.pdf

.61 This modeling effort supports the char ge from the 2001 ozone stakeholder process 2 :

“...prudence requires that early id entification of any potential impediments to remaining in attainment be vigorously pursued so that modera te, responsible action can be initiated first, in order to prevent the need for more drastic measures later.”

The figure below shows the July 1998 ozone event c hosen for this modeling exercise. Three days of the episode exceeded the 8-hour federal standard of 84 ppb (July 26 th , 27 th ,and 28 th ).

Figure 1. Ozone 8-hour c oncentrations at Puget Sound area monitors



## Emissions Inventory Approach

The Agency compiled this emissions inventory al ong with partners to s upport the ozone modeling study performed by Washington State University (WSU). The Wash ington State Department of Ecology (Ecology) served as a main emissions inve ntory partner; their summa ry of onroad mobile emissions and emissions processing follows this report. The Agency and partners created and edited this emissions inventor y from September 2005 through Marc h 2006. A monthly conference call provided a forum to discuss i nventory issues. This informal emission inventory (EI) technical group included:

Kathy Himes Puget Sound Clean Air Agency John Anderson Puget Sound Clean Air Agency Kwame Agyei Puget Sound Clean Air Agency Beth Carper Puget Sound Clean Air Agency Sally Otterson Washington State Department of Ecology

2 The Puget Sound Clean Air Agency. Final Report of the Puget Sound Clean Air Agency CO/Ozone Stakeholders Group. Prepared with Ross & Associates Environmental Consulting Ltd. June 27, 2001. http://www.pscleanair.org/news/other/co_ozone_report.pdf

.62 Clint Bowman Washington State Department of Ecology Doug Schneider Washington St ate Department of Ecology Brian Lamb Washington State University Joe Vaughan Washington State University Ying Xie Washington State University

This inventory is a compilation of previously published inventories, and inventories created specifically for this project. It relies heavily upon the Environmental Protection Agency’s (EPA) 1999 National Emission Inventory (NEI). 3 The inventory includes local emissions estimates where appropriate and available.

1999 emissions were generally consid ered to be similar to 1998 emi ssions, the year of the episode. 1999 emissions were used as a surrogate for the ba se year (1998) for point sources, area sources, and some non-road sources (ships, locomotives, and aircrafts). 1998 emissions estimates were available for on-road mobile and non-road mobile s ources that were determined using EPA models. A master list of source classifi cation codes (SCCs) used in the pr oject is available upon request.

The Agency and its partners developed projection factors to estimate emissions in 2015. Two 2015 emissions scenarios were created: one that included a low Reid Vapor Pressure (RVP) agreement for on-road and non-road gasoline, and one that did not include this agreement.

The Agency and partners collected emissions es timates for the domain of this ozone modeling exercise, covering western Washington, a portion of northern Oregon, and a portion of southern British Columbia. This area is consistent wi th Washington State University’s “AIRPACT2” domain, and shown in Figure 2. Table 1 show s Oregon and Washington counties included in the modeling domain, along with estimated 1999 popul ations. 1999 populatio n estimates were extremely close to 1998 estimates, within 2%. 4, 5, 6

The Agency and partners collected emissions es timates for volatile organic compounds (VOC), nitrogen oxides (NO X ), sulfur oxides (SO X ), and carbon monoxide (CO) fo r all source categories. WSU also used fine particulate matter (PM 2.5 ) information provided by its AIRPACT forecasting system to better inform modeling results. 7

Modeling inventories require emi ssions estimates to be allocated to 4km by 4 km grids over the modeling domain by hour and chemical species groups . Temporal, spatial, and chemical profiles currently used in the Sparse Matrix Operator Kernel Emissions (SMOKE) emissions processor for the AIRPACT forecasting system and other regiona l modeling initiatives formed the basis for making the allocations. The profiles were supplem ented with additional information as necessary.

3 1999 NEI National Emissions Inventory Data and Documentation – Final Version 3.0. http://www.epa.gov/ttn/chief/net/1999inventory.html

. 4 U.S.Census Bureau. Annual Estimates of the Population for Counties of Washington: April 1, 2000 to July 1, 2004 (CO-EST2004-01053). April 14, 2005.

5 U.S. Census Bureau. Annual Estimates of the Population for Counties of Oregon: April 1, 2000 to July 1, 2004 (COEST2004-01-41). April 14, 2005.

6 Office of Financial Management. State of Washington 1999 Population Trends. Forecasting Divison. September 1999.

7 Washington State University. Air Indicator Re port for Public Access an d Community Tracking. http://www.airpact.wsu.edu/

.63 Figure 2. Emissions Inv entory Modeling Domain64 Table 1. Washington and Ore gon Counties in Modeling Domain

County State 1999 Population

. King Washington 1,664,846

. Pierce Washington 688,807

. Snohomish Washington 596,598

. Clark Washington 336,268

. Kitsap Washington 236,560

. Yakima Washington 220,785

. Thurston Washington 205,459

. Whatcom Washington 160,310

. Skagit Washington 101,180

. Cowlitz Washington 91,949

. Island Washington 73,490

. Lewis Washington 68,621

. Grays Harbor Washington 67,102

. Clallam Washington 64,690

. Chelan Washington 60,835

. Mason Washington 50,357

. Okanogan Washington 38,432

. Kittitas Washington 32,021

. Jefferson Washington 26,748

. Pacific Washington 20,768

. Klickitat Washington 19,530

. San Juan Washington 12,898

. Skamania Washington 9,831

. Wahkiakum Washington 3,841 Multnomah Oregon 633,224 Washington Oregon 409,305 Clackamas Oregon 338,251 Marion Oregon 272,760 Linn Oregon 105,337 Yamhill Oregon 83,424 Benton Oregon 77,192 Polk Oregon 62,396 Coos Oregon 61,670 Columbia Oregon 45,368 Lincoln Oregon 44,985 Clatsop Oregon 35,323 Tillamook Oregon 24,420 Wasco Oregon 23,346 Hood River Oregon 19,917 Jefferson Oregon 16,86165 

## Base Year Point Sources Inventory

1999 National Emission Inventory (NEI) estimates were used for western Washington point

sources. 8 The ozone modeling episode occurred in Ju ly 1998. The EI technical group made an assumption that 1999 and 1998 point source emissions are very similar, and that 1999 values are sufficient to characterize emissions from 1998. The Department of Ecology downloaded 1999 NEI files (EM, EP, ER, and SI) for western Washi ngton counties included in the modeling domain. 9 All of the sources have associated la titudes and longitudes. . Afte r all emissions inventories were collected, Ecology processed point s ource emission summaries using SMOKE. 10 Washington State University used temporal assumptions from previous work for point sources. 11 These assumptions are available upon request.

There were a few exceptions to 1999 NEI point source emissions used:

• Environment Canada provided British Columb ia point source information directly to Washington State University for use in the AI RPACT forecasting system. British Columbia point source emissions are for the year 2000. The Agency and partners made an assumption that 2000 emissions are similar to 1998 emissions.

• The Agency provided 1999 point source informa tion directly from its emission inventory database for sources within its jurisdiction. 12 In instances where latitude/longitude information was incomplete for an Agency point source, latitude/longitu de coordinates were created using geocoding. 13

• Southwest Clean Air Agency and Oregon Depart ment of Environmental Quality had already estimated Oregon 1998 point sources for the sa me July 1998 ozone episode for previous modeling work. 14 Southwest Clean Air Agency provi ded Oregon point source information to the Agency. The Agency then forwarded this file to Ecology for SMOKE pre-processing.

In instances where stack parameters were not assigned to a point source, the SMOKE data processing algorithm provided default parameters . These default parameters are available upon request.


## Base Year Stationary Area Sources Inventory

1999 NEI stationary area source emissions estimat es were used for the western Washington and Oregon counties within the modeling domain. The Agency collected area source information from

8 The Agency initially attempted to gather point source info rmation from individual western Washington air agencies and aggregate emissions information to a single point; however, the EI technical group decided that the more complete NEI data would provide better modeling information

9 Washington State Department of Ecology’s download of 1999 NEI point sources was used. Values were consistent with DOE’s WEDS point source database values.

10 Refer to Department of Ecology’s documentation for details on SMOKE data processing. Washington State Department of Ecology Air Quality Program. Puget Sound Ozone Modeling Emissions Inventory Documentation. Contact: Sally Otterson.

11 The PTREF file is from some WRAP work and PTPRO file is currently used fo r AIRPACT forecasting system.

12 This was due to difficulty processing NEI detailed tables for the Agency’s jurisdiction. The Agency’s jurisdiction covers King, Kitsap, Pierce , and Snohomish counties.

13 Google Earth. www.earth.google.com

. 14 Ying Xie and Brian Lamb. Washington State University. Historical and Future Ozone Simulations using the MM5/SMOKE/CMAQ System in the Por tland/Vancouver Area. December 31 st , 2005.66 various local air agencies in we stern Washington and performed a comparison of their inventories and the 1999 NEI. Differences were negligible and it was convenient to use the 1999 NEI for clarity and documentation. Environment Canada provided British Columbia area sources directly to Washington State University, for use in the AIRPACT forecasting system.

Current AIRPACT system spatial and temporal assu mptions for area sources were applied in data pre-processing. Temporal and spatial assumptions are available upon request. Due to temporal allocation, smoke emissions from woodstoves and fireplaces were eliminated (no burning during warm summertime ozone event). Also, the EI technical group ex amined wildfires that took place during the ozone event. None we re large or close enough to impact the modeling, so all wood and forest burning was deleted from the inventory. This includes land-clearing burning as well.


## Base Year Non-road Mobile Sources Inventory

All Washington and Oregon non-road mobile sources were divided into two categories, those included in EPA’s 2005 Final NONROAD model, and those not included in the model

.

Environment Canada submitted 2000 emissions dire ctly to Washington State University for all British Columbia non-road mobile sources,



### Non-road Sources included in NONROAD model

The Agency ran EPA’s 2005 final NONROAD Model, the latest tool ava ilable to estimate emissions from miscellaneous non-road mobile sour ces. These sources include but are not limited to lawn and garden, recreational marine, comm ercial construction equi pment, airport support equipment, and rail yard maintenance equipment. 15 The Agency ran the NONROAD model for all U.S. counties included in the modeling domain (western Washington and northwest Oregon). The Agency ran the model for 1998, using EPA annual defaults for the Puget Sound area. Defaults include oxygen content in gasoline and sulfur content in gasoline, diesel, compressed natural gas (CNG), and liquefied petroleum gas (LPG). An nual temperatures from SeaTac and Portland International Airport were used for temperatures. 16, 17

NONROAD model annual defaults, temperature information, and output files are available upon request.



### Non-road sources not included in the NONROAD model

EPA’s NONROAD Model does not estimate emissions for aircraft, commercial marine sources, line-haul rail, and switchyard rail. As with other categorie s, 1999 NEI estimates for these categories were generally used for these sources. Exceptions were made for some aircraft, marine, and rail emissions in the Agency’s jurisdiction when NEI emissions were not available or when local emissions estimates were more appropriate. For example, the NEI does not provide estimates

15 Environmental Protection Ag ency. Final 2005 Nonroad Model. December 2005. http://www.epa.gov/otaq/models/nonrdmdl/nonrdmdl2005/nr05inst.htm The Agency had previously run the 2004 draft NONROAD model, and re-ran scenar ios when the final version becam e available in December 2005.

16 1998 SeaTac Meteorological Summaries 17 www.wrh.noaa.gov/pqr/climate/pdx-clisummary.php, and Southwest Clean Air Agency67 for ferries, tug boats, or military marine vessels. In Kitsap County, the NEI did not provide an estimate for underway ships. In these cases, the Agency used 1999 local emission estimates.

Marine and aircraft source categorie s that were replaced with local estimates are summarized in the table 2, by county. The methodology used for each category is provided in Appendix B.

Table 2. Local Non-Road Emissions Estimates Substitutions, Tons/Year County Category SCC Description CO NOx SOx VOC King 2280002020 Harbor Vessels 808 5672 572 306 King 2280002040 Military Vessels 4 32 9 1 Kitsap 2280002010 Underway Ships 678 4128 470 267 Kitsap 2280002020 Harbor Vessels 900 6309 636 340 Kitsap 2280002040 Military Vessels 9 68 20 3 Pierce 2275001000 Military Aircraft 562 221 14 436
Pierce 2280002020 Harbor Vessels 100 701 71 38 Pierce 2280002040 Military Vessels 0 2 1 0 Snohomish 2280002020 Harbor Vessels 184 1289 130 70 Snohomish 2280002040 Military Vessels 2 12 3 1



### Spatial and temporal allocation

Vessel traffic in the Puget Sound area is typically within a few seconds we st and east of 'the separation zone' shown in Coast Survey Charts published by National Oceanic & Atmospheric Agency. 18 The Agency provided information on ship pa ths for modeling. However, given resource and time constraints, all vessel traffic is allocated to “water” in the modeling domain.

All aircraft emissions in King and Multnomah counties are assigned to SeaTac and Portland international airports, respectively. Aircraft emissi ons for all other counties are assigned to landuse type “commercial/industrial/transportation.”

Temporal and spatial allocations for non-road mobile sources are available upon request.


## Base Year On-road Mobile Sources Inventory

Ecology performed on-road mobile modeling, and the Agency performed a comprehensive review of the work. A complete description and documen tation of the on-road mobile source modeling and data processing are provided in Ecol ogy’s documentation in Appendix C.

Ecology used EPA’s on-road mobile model MOBILE 6.2 to determine emission factors for on-road vehicles. These emissions were grouped into he avy duty diesel, light du ty diesel, and gasoline source groups. Emission factors were calculated for each area that had a unique combination of Reid Vapor Pressure (RVP) and Insp ection and Maintenance (I/M) Plan.

18 National Oceanic and Atmospheric Agency, National Ocean Servic e, Coast Survey. Puget Sound - Northern Part (Chart No.
18441) Puget Sound - Southern Part (Chart No. 18448). September 2005.68 The emission factors were assigned on a county level and were weight ed based on the percentage of the county involved in each unique RVP and I/M plan combination. These factors were then multiplied by the Average Daily Vehicle Miles Traveled (ADVMT). 19

MOBILE6.2 input files are available upon request.


## Base Year Biogenic Inventory

Washington State University (WSU) estimated bioge nic emissions from trees, plants, and crops. WSU processed biogenic emissions us ing the BEIS model, version 3.09. 20 The model estimates VOC emissions from vegetation based on land-use categories and nitric oxide emissions from microbial activity based on soil types. The BEIS 3 modeling system uses the Biogenic Emissions Landcover Database, version 3, which consists of 1 km gridded data 230 land-use types 21 . BELD3 is a hybrid data set based on the USGS North American Land Character istics Data (1km, 21 classes). It incorporates crop sp ecies distribution and cr op area from 1992 US Agricultural Census, forest area from US forest densit y data, and tree species distributi on from US Forest Inventory and Analysis (FIA) data. There are two steps involved in BEIS3 modeling. In the first step, the model uses the BELD3 land-use data and emissions factor s, and outputs gridded, normalized emissions at
30 ° C and 1000 umol m -2 ·s -1 Photosynthetically Active Radiati on (PAR). In the second step, the model uses meteorological data, speciation profiles, and normalized emissions generated from the previous step to compute gridded and speciated hourly biogenic emissions.


## Future Year (2015) Emissions Projections

Emissions were projected to 2015 and two scenarios were modeled. One scenario included a low Reid Vapor Pressure (RVP) agreement (“low RVP”) and another did not include a low RVP agreement (“high RVP”). 22 Details of projections follo w under source categories on following

pages.



### Point Sources

The EI technical group performed a comparison of 1999 NEI and latest available NEI (2002) point source inventories. No major changes in emissions were noted. 23 The Agency’s compliance division and other air agency compliance divisions were also consulted to determine major facilities

19 Larry Blaine, Puget Sound Regional Council. 20 Vukovich, J. and T. Pierce. The Implemen tation of BEIS3 within the SMOKE Modeling Framework, In Proceedings of the 11thInternati onal Emissions Inventory Conference, Atlanta, Georgia, April 15-18, 2002. :www.epa.gov/ttn/chief/conference/ei11/modeling/vukovich.pdf.

21 The BELD3 data and BEIS model are further described at EPA’s Biogenic Emissions Sources site. http://www.epa.gov/ttn/chief/emch/biogenic/

. 22 Two different future year RVP scenarios were created for on-road and non-road mobile sources. The area and point source categories are not as af fected by RVP differences and only required one future year emission inventory.

23 A comparison of VOC emissions for the 1999 and 2002 NEI found them generally less than 1% different. The major exceptions, with differences of 10% and greater (up to 12x difference), were the indoor and outdoor burning categories. These large differences do not affect this inventory for a summertime ozone event.69 likely to go on or off-line before 2015. Additionally , the EI technical group voiced concerns that 2002 emissions may not be representative, as 2002 was considered a “down” economic year. All point sources were held cons tant from 1999 values for 2015 (projection factor of 1).



### Stationary Area Sources

As with point sources, the EI technical gr oup performed a comparison of 1999 NEI and latest available NEI (2002) area source inventories. As with point sources, no major changes in emissions were noted. 24 Based on this comparison, projections were based on the 1999 base year. Area source projections relied largely on popula tion forecasts and in some cases economic forecasts. 25 Growth factors that dire ctly link population or economic growths to emissions growth were applied to base year emissions. Applying an activity level to base year emissions results in future emissions that are linearly scaled across pollu tants. Stationary area source projec tion factors ranged from 0.24 (for residential coal combustion in King County) to 1.51 (for liquefied petroleum gas use in King County).

The Agency submitted Whatcom County, Washington emissions projection factors as a surrogate for British Columbia area source projection factors.

Projection factors from Oregon Department of E nvironmental Quality’s previous ozone modeling work were used for Oregon counties. These projec tion factors were generally comparable to Clark County, Washington projections. For a few SCCs , Clark County surrogate factors were used instead.

Stationary area source growth factors for each SCC and county are available upon request.



### Non-Road Mobile Sources

NONROAD Model

The 2005 NONROAD model was run for year 2015 for wester n Washington and northwestern Oregon counties. 26 To account for different scenarios, NONROAD was run with both an RVP of 7.8 and 8.7 psi for western Washington countie s. Annual defaults and annual temperature characteristics were kept the same as the base case year. NONROAD output files for 2015 are available upon request.

NONROAD was not run for British Co lumbia for 2015. Instead, proj ection factors were developed using the ratio of 2015 emissions to 1998 emissi ons for Whatcom County, Washington. Ratios were developed for each pollutant and source cate gory. This means that the projected emission rates better represent future te chnology changes that cause greater reductions in some pollutants than others.

24 A comparison of VOC emissions for the 1999 and 2002 NEI generally showed a difference of less than one percent. The major exceptions, with differences of 10% and greater (up to 12x difference), were the indoor and outdoor burning categories. These large differences do not aff ect this inventory for a summertime ozone event.

25 Office of Financial Management, State of Washington. 1999 Population Trends. September 1999.

26 Environmental Protection Agency. Final 2005 NONROAD Model.70 Aircraft

SeaTac International Airport and Boeing Field activ ity projections were taken from projections published by the airports

. 27, 28 Military aircraft was kept consta nt at 1999 emission levels. No local military bases are slated for closure.

Marine

The Agency went through a careful process to estima te growth factors for the marine sector. While TEUs (twenty-foot-equivalents, a unit of measure fo r cargo) are predicted to dramatically increase in the Northwest, vessel calls (the number of trip s) are expected to increase more slowly due to larger ships being used.

Ultimately, the Agency chose a projection factor of 2 (indicating a doubling of activity from 1999 to 2015) that balances the dramatic TEU increase and th e lower increase in vesse l calls. This factor was also used for marine sources in British Co lumbia, and is based on the following projections:

• Port of Seattle: 2.21 TEU growth factor fr om and 1.23 vessel call gr owth factor from 1999 to 2015. 29

• Port of Tacoma: 4.8 TEU growth factor from 1999 to 2015. 30

• Washington Public Ports Association cites P uget Sound containers more than doubling (2.5) from 2002 to 2025 (2.8 million to 6.9 million). The same report cites total tonnage to increase 66 percent (this in cludes Columbia River ports). 31

• The Port of Vancouver, BC cites a projected cargo tripling from 2004 to 2020 (1.7 million to 5.3 million TEUs). 32

• Growth forecasts used in a diesel particul ate study at the Port of Long Beach and Los Angeles predict that trade will triple by 2020. 33

• 

### On-Road Mobile Sources

EPA’s MOBILE 6.2 model was run for year 2015 with future vehicle miles traveled (VMT) predictions. The model takes into account technologies resulting in a cleaner fleet when comparing 2015 to 1998. As with the non-road model, MOBI LE 6.2 was run with two RVP scenarios of 7.8 and 8.7 psi for most western Washington counties. A complete description of the VMT assumptions and modeling methodology are provided in Ecology’s documentation in Appendix C.

27 Camp Dresser & McKee. Seattle Tacoma International Airport Emissions Inventory, 2000-2015. Prepared for Maggie Corbin, Air Program Manager. April 2003.

28 King County. NEPA Environmental Assessment/SEPA Environmental Impact Statement for Proposed Master Plan Improvements at King County International Airport (Boeing Field). February 23 rd , 2004.

29 Based on 1,490,048 TEUs in 1999 and an estimated 3,300,000 TEUS in 2015, per LaTonja Brown, Port of Seattle.

30 Based on an increase from 1.28 to 6.3 million TEUs from 1999 to 2015. Cindy Lin, Port of Tacoma.

31 Washington Public Ports Association and the Washington State Department of Transportation. 2004 Marine Cargo Forecast. May 19, 2004.

32 National Post. December 17, 2005. Te rminal Velocity: As China races ahead with port expansions, Vancouver’s plans for Deltaport run aground.

33 California Air Resources Board. Diesel Particulate Ma tter Exposure Assessment Study for the Ports of Los Angeles and Long Beach. October 2005. Growth predictions on page 6.71 MOBILE6.2 was not run for British Columbia for 2015. Instead, projection factors were developed using the ratio of 2015 emissions to 1998 emissi ons for Whatcom County, Washington. Ratios were developed for each pollutant and source cate gory. This means that the projected emission rates better represent future te chnology changes that cause greater reductions in some pollutants than others.

MOBILE6.2 input files are available upon request

. 

### Biogenic Emissions

Biogenic emissions were held constant, assuming no change from 1998 to 2015. This is consistent with the approach used in the Puget S ound modeling report based on the 1996 episode.



### Emissions Summaries

Tables 3 and 4 show emission summaries for precursors NO X and VOC for the Puget Sound area (Snohomish, King, Pierce, and Kitsap counties) for bot h base and future year scenarios. Emissions have been spatially and temporally allocated, and ar e shown in tons/day for the five highest days of the ozone episode. Percent changes in emissions from the 1998 base year are also included.

Table 3 shows that, from 1998 to 2015, VOC emissi ons in the Puget Sound area are projected to reduce by approximately 20%. The majority of VOC reductions occur in the on-road and non-road mobile categories, as much as 65% . These reductions are slightly off-set by projected increases in area source categories, as much as 14%. Area source emissions increases are due largely to increasing population forecasts.

The 2015 “High RVP” assumes that no low RVP agr eement is in place in the Puget Sound area in 2015 (8.7 psi). The “low RVP” assumes that a low RVP agreement is in place in the Puget Sound area in 2015 (7.8 psi). The 2015 “High RVP”scenar io’s on-road and non-road mobile emissions are slightly higher than “low RVP” on-road and non-road emissions (by approximately 1%).

Table 4 shows that, from 1998 to 2015, NO X emissions are projected to decrease by approximately 25%. The majority of the decrease comes from on -road mobile emissions, projected to be almost 70% less. Predicted increases of more than 33% in non-road mobile (due in part to increased to marine growth) partially off-set the large reduc tion in on-road mobile. The values for both 2015 “high RVP” and “low RVP” scenarios ar e identical, as RVP does not affect NO X emissions.72 Table 3. Puget Sound VOC Emissions, Base and Future Cases Emissions in Tons/Day % Change from 1998 Baseline
Sat. Sun. Mon. Tues. Wed. Sat. Sun. Mon. Tues. Wed. Year (Scenario) Category 7/25 7/26 7/27 7/28 7/29 7/25 7/26 7/27 7/28 7/29
1998 Area 155 107 136 155 155 na na na na na
1998 Point 10 17 17 17 17 na na na na na
1998 Non-road 168 177 136 128 128 na na na na na
1998 On-road 223 191 223 237 240 na na na na na 1998 Biogenic 264 407 469 477 433 na na na na na

Total 821 898 982 1015 973 na na na na na

2015 (low RVP) Area 172 122 153 172 172 11 14 12 11 11 2015 (low RVP) Point 10 17 17 17 17 0 0 0 0 0 2015 (low RVP) Non-road 95 101 76 70 70 -44 -43 -44 -46 -46 2015 (low RVP) On-road 78 66 78 83 84 -65 -65 -65 -65 -65 2015 (low RVP) Biogenic 264 407 469 477 433 0 0 0 0 0 2015 (low RVP) Total 619 714 793 819 776 -25 -21 -19 -19 -20

2015 (high RVP) Area 172 122 153 172 172 11 14 12 11 11 2015 (high RVP) Point 10 17 17 17 17 0 0 0 0 0 2015 (high RVP) Non-road 95 102 76 70 70 -43 -42 -44 -45 -45 2015 (high RVP) On-road 81 69 81 86 87 -64 -64 -64 -64 -64 2015 (high RVP) Biogenic 264 407 469 477 433 0 0 0 0 0 2015 (high RVP) Total 623 717 797 823 780 -24 -20 -19 -19 -20 Note: Puget Sound area includes emissions from Ki ng, Pierce, Snohomish, and Kitsap counties. Note: For “Nonroad”, VOC includes all TOG categories in NONROAD model, summed together. Note: For “Onroad”, VOC includes all HC cate gories in the ONROAD model, summed together.73 Table 4. Puget Sound NO X Emissions, Base and Future Cases Emissions in Tons/Day % Change from 1998 Baseline
Sat. Sun. Mon. Tues. Wed. Sat. Sun. Mon. Tues. Wed. Year (Scenario) Category 7/25 7/26 7/27 7/28 7/29 7/25 7/26 7/27 7/28 7/29
1998 Area 14 13 14 14 14 na na na na na 1998 Point 14 27 27 27 27 na na na na na 1998 Non-road 183 171 200 211 211 na na na na na 1998 On-road 291 248 290 309 312 na na na na na 1998 Biogenic 2 2 2 2 2 na na na na na

Total 502 461 533 564 567 na na na na na

2015 (low RVP) Area 13 12 13 13 13 -6 -6 -6 -5 -5 2015 (low RVP) Point 14 27 27 27 27 0 0 0 0 0 2015 (low RVP) Non-road 262 258 267 271 271 43 50 33 28 28 2015 (low RVP) On-road 93 79 93 99 100 -68 -68 -68 -68 -68 2015 (low RVP) Biogenic 2 2 2 2 2 0 0 0 0 0 2015 (low RVP) Total 383 378 402 413 414 -24 -18 -25 -27 -27

2015 (high RVP) Area 13 12 13 13 13 -6 -6 -6 -5 -5 2015 (high RVP) Point 14 27 27 27 27 0 0 0 0 0 2015 (high RVP) Non-road 262 258 267 271 271 43 50 33 28 28 2015 (high RVP) On-road 93 79 93 99 100 -68 -68 -68 -68 -68 2015 (high RVP) Biogenic 2 2 2 2 2 0 0 0 0 0 2015 (high RVP) Total 383 378 402 413 414 -24 -18 -25 -27 -27 Note: Nitrogen oxide, “NO”, bi ogenic emissions counted as NO X
. 

## Emissions Inventory Limitations

This inventory relied largely on the 1999 NEI, associated with its own limitations because it is a national inventory. The NEI is large in scope, and primarily a top-down approach.

The inventory relied on different years (1999, 2000) to serve as surrogates for the “true” 1998 base

year.

Using inventories from differe nt sources and methodologies ( 1999 NEI and Oregon DEQ modeling exercise) may have introduced some geographic bi ases into the inventory. These biases are expected to be small, and are explored in the following sections.

Future projections are, by natu re, uncertain. Changes in local economies, fuel policy, and other local factors could influence estimated grow th factors positively or negatively.74 

### Comparisons between Seattle a nd Portland Area Emissions

The EI technical group had concerns about the emissions rates of the Portland Oregon area and the Seattle Washington area. A comparison of thr ee counties around each me tropolitan area showed a Seattle to Portland population ratio of approximately 2.1. 34 A comparison of the vehicle miles traveled (VMT) for the same area showed a ratio of 2.4. One can reasonably expect emissions to reflect these ratios. This was generally the cas e, as seen in Table 5. The exception was area sources, which appeared to be low for the Seattle ar ea, with a ratio of 0.6. Point sources were not included in this comparison, as they can be expected to be different due to different industries in different metropolitan areas. Area, non-road, and on -road categories would be expected to track the population and VMT ratios.

Table 5: Ratio of Seattle Area/Portland Area Emissions 35

Source Category VOC NO X Area 0.6* 1.3 Non-road 2.1 3.1 On-road 3.1 2.7Total (Area, Non-road, Onroad) 1.2 2.7 *Area source ratio appeared low to the EI technical group

The Portland area performed well in initial modeling runs. The EI group expressed concern that area source VOC emissions in the Puget Sound area may be under-estimated.

To address these concerns, the EI group exam ined area source categor ies with the largest discrepancies between Portland and Seattle emissi ons estimates. The seven area source categories with the largest discrepancies are shown in table 6, as well as the total area source emissions. The “Portland” Column represents annual VOC emis sions for Multnomah, Clackamas, and Washington counties. The “Seattle 1999 NEI” category represents 1999 NEI VOC emissions for King, Snohomish, and Pierce counties. The “Seattle Local” represents local estimates that were performed to quality check the 1999 NEI – these incl ude Kitsap in addition to the other three Seattle area counties. Kitsap County has ve ry little business and industry. The local estimates are based on local surveys and activity level information.

Table 6 shows that there are some discrepancie s for individual SCCs between Seattle local estimates and the 1999 NEI. One example is SCC 2401070, where the 1999 NEI (36 tons/year) is substantially less than the local estimate (1, 038 tons/year). Nonethel ess, the total 1999 NEI emissions and local Seattle estimates are close, with 60,627 and 59,127 tons/year, respectively. Changing the emissions inventor y to reflect local Seattle em issions estimates would not significantly change the VOC ratio for Seattle to Portland. The Po rtland area, with about half the population of the Seattle area, shows area source estimates greater than Seattle’s. It’s possible that VOC area source emission estimates in the Portland inventory are over-estimates.

34 Comparison of 2000 census population for King, Pierce, and Snohomish counties in Washington and Multnomah, Clackamas, and Washington counties in Oregon.

35 Ratios provided by Washington State Department of Ecology. Seattle area includes King, Pierce, and Snohomish counties. Portland area includes Multnom ah, Clackamas, and Washington counties.75 Table 6: Comparison of Portland, Sea ttle NEI 1999, and Seattle Area Source VOC Emissions Estimates (in tons/year) Seattle Seattle SCC Description Description Portland NEI 99 Local

2401005 Surface Coating Auto Refinishing 1,995 643 664

2401050 Surface Coating Miscellaneous Finished Metals 17,650 no estimate 1,004

2401065 Surface Coating Electronic and Other Electrical 2,250 197 193

2401070 Surface Coating Motor Vehicles 1,196 36 1,038

2415355 Degreasing Automotive Dealers: Cold Cleaning 1,829 27 315 2425000 Graphic Arts All Processes 5,779 812 1,187

2401045 Surface Coating Metal Coils: SIC 3498 1,397 257 included in 1,004 above

TOTAL ALL 108 AREA SOURCES 98,048 60,627 59,197



### VOC/NO X



### Ratios

In addition to comparing the emissions estimates between geographic areas, the EI technical group compared the VOC/NO X ratio for source categories. Table 7 examines the VOC/NOx ratios for source categories between King County (Seattle) and Multnomah County (Portland). With the exception of the point source and biogenics categori es, the ratios were relatively close for source categories. Table 11 shows the VOC/NO X ratios for the Puget Sound Area for the event (King, Snohomish, Pierce, and Kitsap counties).

Table 7. VOC/NO X Ratios for King and Multnomah Counties, 1998 Ozone Event

Area Sources Point Sources Non-road Mobile Sources On-road Mobile Sources Biogenic Sources Date King Mult King Mult King Mult King Mult King Mult 26-Jul 7.8 5.7 0.5 2.0 1.0 1.0 0.8 0.7 226.1 68.4 27-Jul 9.6 9.0 0.5 2.1 0.7 0.7 0.8 0.7 237.9 79.8 28-Jul 10.9 11.1 0.5 2.1 0.7 0.6 0.8 0.7 235.2 79.0 29-Jul 10.9 11.1 0.5 2.1 0.7 0.6 0.8 0.7 226.8 63.7

King = King County, Mult = Multnomah County76 Table 8: VOC/NO X Ratios for Puget Sound Area, 1998 Ozone Event

Area Sources Point Sources Non-road Mobile Sources On-road Mobile Sources Biogenic Sources Total 7/25/1998 11.5 0.7 0.9 0.8 176.2 1.6 7/26/1998 8.2 0.6 1.0 0.8 224.3 1.9 7/27/1998 9.9 0.6 0.7 0.8 236.0 1.8 7/28/1998 11.1 0.6 0.6 0.8 237.4 1.8 7/29/1998 11.1 0.6 0.6 0.8 226.3 1.777 

## Appendix B

Methodology for Local Non-road Mobile Source Emission Substitutions

Marine and aircraft source categor ies that were replaced with local estimates are summarized in Table 2 of Appendix A, also below. County-by county descriptions of methodology for each of the categories are described below.

Table 2. Local Non-Road Emission Substitutions, Tons/Year County Category SCC Description CO NOx SOx VOC King 2280002020 Harbor Vessels 808 5672 572 306 King 2280002040 Military Vessels 4 32 9 1 Kitsap 2280002010 Underway Ships 678 4128 470 267 Kitsap 2280002020 Harbor Vessels 900 6309 636 340 Kitsap 2280002040 Military Vessels 9 68 20 3 Pierce 2275001000 Military Aircraft 562 221 14 436
Pierce 2280002020 Harbor Vessels 100 701 71 38 Pierce 2280002040 Military Vessels 0 2 1 0 Snohomish 2280002020 Harbor Vessels 184 1289 130 70 Snohomish 2280002040 Military Vessels 2 12 3 1

King County

The 1999 NEI estimated 55 tons of VOC and 1,030 tons of NOx/year for harbor vessels. Harbor vessels include smaller ships such as ferries, tugs, and tour boats. Based on local surveys, this estimate is too low. The Agency substituted a 1999 emission estimate using a power method. The power method uses the horsepower of a ship, a load factor (an estimate of the fraction of rated horsepower the vessel is operating at), and time sp ent in an area along w ith emission factors to estimate emissions. 36 The Agency gathered power use info rmation from local ferries and cruise ships and used this to interpol ate fuel use of different ships. Using local methods brought the estimate to 306 tons of VOC and 5,672 tons NO X /year.

The NEI didn’t provide an estimate for military ve ssels in King County. Using a local power method we estimated emissions from military vessels.

Kitsap County
The 1999 NEI didn’t provide an estimate for Underw ay Ships in Kitsap C ounty. Underway ships include larger ships such as cargo and cruise ships. The Agency estimated emissions using a fuel use method. The fuel use method takes the consump tion rate of a ship (ga llons/hr) and the time

36 The Port Authority of New York & New Jersey. The Ne w York, Northern New Jersey, Long Island Nonattainment Area Commercial Marine Vessel Emissi ons Inventory. Prepared by Starcrest Consulting Group, LLC. April 2003. Emission factors used by Starcrest in this repo rt are also used in our marine estimates.78 spent in an area along with emission factors to es timate emissions. The Agency gathered fuel information from local ferries and cruise ships and us ed this to estimate fuel use of different ships.

The 1999 NEI didn’t provide an estimate for Harbor Vessels in Kitsap County. We estimated harbor vessel emissions us ing a fuel use method.

The 1999 NEI didn’t provide an estimate for Milita ry Vessels in Kitsap County. The Agency estimated military vessel emissions using the power demand method (described in King County section).

Pierce County

The 1999 NEI estimates 0 tons of VOC and 0 tons NO X /year for harbor vessels. Based on local surveys, this estimate is too lo w. We substituted a 1999 emission estimate using the fuel use method described for Kitsap County. Using these methods and local data estimated the emissions of these vessels to be 38 tons per year VOC and 701 tons per year NO X
.
The 1999 NEI doesn’t provide an estimate for M ilitary Vessels in Pierce County. The Agency estimated military vessel emissions using the pow er demand method (described in the King County section). The 1999 NEI estimates 0 tons of VOC and 0 tons NO X /year for Military Aircraft. Based on local knowledge, this is an underestimate. The Agen cy used the Emissions and Dispersion Modeling System (EDMS 4.3) to estimate military aircraft emissions. 37 The modeling results estimated the Military Aircraft to be 436 tons per year VOC and 221 tons per year NO X
.
Snohomish County

The 1999 NEI didn’t estimate harbor vessel emis sions in Snohomish County. Based on local surveys, we know that there should be some emissi ons allocated. The Agency used the fuel use method described for Kitsap County to estimat e emissions. The fuel method estimated the emissions of the harbor vessels to be 70 tons VOC and 1,289 tons NO X per year.

The 1999 NEI doesn’t provide an estimate for Milit ary Vessels in Snohomish County. The Agency estimated military vessel emissions using the pow er demand method (described in the King County section).

37 Federal Aviation Administration. Emissions and Dispersion Modeling System 4.3. http://www.faa.gov/about/office_org/headqua rters_offices/aep/models/edms_model/

.79 

## Appendix C

Puget Sound Ozone Modeling

On-Road Mobile Source Emissions In ventory and Emissions Inventory Data Pre-Processing

Emissions Inventory Documentation

Prepared by Washington State Department of Ecology Air Quality Program contact: Sally Otterson80 Appendix C Table of Contents

1 Project Description..........................................................................................................

........... 81 2 Onroad Mobile Source Inventories............................................................................................ 81 2.1 Activity Level .............................................................................................................

....... 81 2.2 Emission Rates: MOBILE6.2 ............................................................................................ 83 2.2.1 Evaluation Month.........................................................................................................

.. 84 2.2.2 Registration Distribution................................................................................................ 84 2.2.3 Humidity .................................................................................................................

....... 85 2.2.4 Inspection and Maintenance (I/M) Program.................................................................. 85 2.2.5 Reid Vapor Pressure (RVP)........................................................................................... 87 2.2.6 Gasoline Sulfur Content (base case 1998 only)............................................................. 88 2.2.7 Diesel Sulfur Content....................................................................................................

. 89 2.2.8 Example Emission Rates................................................................................................ 89 2.3 Emissions Estimates........................................................................................................

... 90 3 Temporal Projection Factors..................................................................................................

.... 90 3.1 Washington and Oregon .................................................................................................... 90 3.2 British Columbia...........................................................................................................

..... 90 4 Emissions Inventory Data Formatting for SMOKE................................................................... 90 4.1 Emissions Files ............................................................................................................

...... 91 4.2 Spatial Allocation Files...................................................................................................

... 91 4.3 Temporal Allocation Files ................................................................................................. 91 4.4 Chemical Speciation Files.................................................................................................. 92 4.5 Projection Files ...........................................................................................................

....... 92

List of Tables

Table 1: County ADVMT Estimates ...............................................................................................

........ 82 Table 2: I/M Program Area County Fractions....................................................................................

..... 83 Table 3: Registration Distribution Assignments................................................................................

...... 85 Table 4: July Humidity Mixing Ratios, Longterm Averages .................................................................. 85 Table 5: I/M Program Parameters: King, Pierce and Snohomish Counties, 1998................................... 86 Table 6: I/M Program Parameters: Clark County, 1998.......................................................................... 86 Table 7: I/M Program Parameters: King, Pierce and Snohomish Counties, 2015................................... 87 Table 8: I/M Program Parameters: Clark County, 2015.......................................................................... 87 Table 9: Fuel RVP Assignments, psi............................................................................................

........... 88 Table 10: Example TOG, NOx, CO and SO 2 Emission Factors in grams per mile................................. 89 Table 11: Example PM 10 , PM 2.5 and NH 3 Emission Factors in grams per mile...................................... 8981 1 Project Description Ozone modeling was conducted focusing on th e central Puget Sound area. The modeling was initiated by the Puget Sound Clean Air Agen cy (PSCAA) to help in control strategyplanning. Ozone modeling had recently been completed in support of the PortlandVancouver ozone maintenance plan update. In or der to leverage as much of the PortlandVancouver work as possible, the same ozone episode and future projection years were chosen, as well as the same modeling resolution (4-km). Additional leverage was obtained by using base year 2000 informa tion for British Columbia provided by the Greater Vancouver Regional District and E nvironment Canada. The episode modeled was a high ozone event which occurred duri ng July 22-28, 1998. The projection year chosen was 2015. The modeling domain en compassed western WA, northwestern OR, and southwestern BC.

Three scenarios and several sensitivity cases were modeled. The three scenarios were:

• 1998 base case

• 2015 projection with voluntary low RVP control

• 2015 projection without vol untary low RVP control

To complete the work, PSCAA contracted wi th Washington State University (WSU) to conduct the modeling, and entered into an in formal agreement with Ecology to provide technical assistance. One of Ecology's primar y roles was to assist in emissions inventory development. Ecology completed three basic inventory tasks: calculated onroad mobile source inventories, formatted emissions data for the Sparse Matrix Operator Kernel Emissions (SMOKE) emissions processor, and provided assistance in calculating projection factors. These tasks are descri bed more fully in the remainder of this document.

2 Onroad Mobile Source Inventories

Onroad mobile source emissions are thos e generated by operating vehicles on public roadways. Emissions from fuel combustion, evaporation, and brak e and tire wear were estimated. Emissions were calculated for each county for each of th ree vehicle classes: gasoline, light duty diesel and heavy duty dies el. The three vehicles classes were based on the availability of hydro carbon chemical speciation prof iles. Emissions of TOG, NOx, SO 2 , CO, PM 10 , PM 2.5 and NH 3 were estimated.

2.1 Activity Level The activity measurement for onroad mobile s ources is the number of miles driven. Units are typically given in average daily vehicle miles traveled (ADVMT). ADVMT is normally estimated from traffic counts coll ected over a sampling area, or through use of travel demand models, which simulate vehicl e travel patterns ba sed on demographic and economic parameters and are validated with traffic counts.

For this inventory, several sources of ADVMT were used. The Puget Sound Regional Council (PSRC) provided VMT for King, Kits ap, Pierce and Snohomish Counties. The82 Southwest Regional Transportation Council (RTC) provided VMT for Clark County. The Washington State Department of Tr ansportation (WSDOT) provided Highway Performance Monitoring System (HPMS) VMT for all other Washi ngton State counties. i,

ii The Oregon Department of Environmen tal Quality (ODEQ) pr ovided VMT for Oregon counties using data from METRO and the Oregon Department of Transportation (ODOT).

PSRC provided VMT for the years 2000, 2010 and 2020. An estimate of 2015 VMT for King, Kitsap, Pierce and Snohomish Coun ties was made by averaging the 2010 and 2020 VMT estimates. The 2000 ADVMT for the four counties was backcast to 1998 using the combined 4-county ratio of 1998 to 2000 ADVMT provided by WSDOT:

PSRC1998 = PSRC2000 x (WSDOT1998/WSDOT2000) where

PSRC1998 = estimated 1998 ADVMT for the 4-county area PSRC2000 = 2000 ADVMT for the 4-county area provided by PSRC WSDOT1998 = 1998 ADVMT for the 4-county area provided by WSDOT from HPMS data WSDOT2000 = 2000 ADVMT for the 4-county area provided by WSDOT from HPMS data

Projections of 2015 ADVMT in Washington co unties outside of Cl ark, King, Kitsap, Pierce, and Snohomish Counties were made using WSDOT 2004 county ADVMT and the statewide 2004-to-2015 forecast (1. 25% increase). ADVMT is shown in Table 1.

Table 1: County ADVMT Estimates

State County 1998 2015 State County 1998 2015 WA Chelan 1,682,730 2,155,818 OR Benton 1,414,458 1,946,666 WA Clallam 1,232,323 1,599,371 OR Clackamas 7,087,185 9,218,373 WA Clark 6,099,050 8,857,114 OR Clatsop 1,170,564 1,622,892 WA Cowlitz 3,152,522 4,109,601 OR Columbia 951,782 1,416,086 WA Grays Harbor 1,735,016 2,309,086 OR Crook 429,547 578,570 WA Island 1,061,965 1,384,617 OR Deschutes 2,585,715 3,992,006 WA Jefferson 832,372 1,132,109 OR GIlliam 445,135 607,324 WA King 40,489,057 49,229,391 OR Hood River 785,173 1,050,163 WA Kitsap 3,810,657 4,963,769 OR Jefferson 660,754 883,290 WA Kittitas 2,588,104 3,659,480 OR Lincoln 1,330,888 1,640,211 WA Klickitat 639,707 850,364 OR Linn 3,834,372 4,998,146 WA Lewis 2,780,149 3,537,303 OR Marion 6,600,563 8,727,365

WA Mason 1,134,642 1,488,435 OR Multnomah 13,840,22 0 16,164,92 3 WA Okanogan 1,277,978 1,532,640 OR Polk 1,501,843 2,002,531 WA Pacific 601,718 760,768 OR Sherman 401,234 479,093 WA Pierce 15,113,670 18,133,019 OR Tillamook 1,106,564 1,419,684 WA San Juan 90,260 114,060 OR Wasco 1,174,459 1,590,373 WA Skagit 3,100,431 4,261,012 OR Washington 8,009,409 9,892,729 WA Skamania 255,406 340,488 OR Wheeler 129,476 171,212 WA Snohomish 13,31 6,517 18,160,285 OR Yamhill 1,550,771 2,161,286 WA Thurston 5, 306,666 7,376,607 WA Wahkiakum 126,334 158,606 WA Whatcom 3,546,324 4,833,710 WA Yakima 4,504,971 6,208,241

Washington Total 114,478,56 9 147,155,89 4 Oregon Total 55,010,11 2 70,562,92 383 ADVMT estimated through travel demand mode ls represent average weekday VMT. The travel demand model ADVMT was adjusted to represent average daily (7-days) VMT by dividing by the HPMS weekday fraction of 1.044. iii

A further spatial breakout of ADVMT was made to incorp orate the vehicle Inspection and Maintenance (I/M) programs which are opera ted in areas of Clar k, King, Pierce, and Snohomish Counties in Washington, and in area of Clackamas, Multnomah, and Washington Counties in Oregon. The fracti on of total ADVMT within the I/M area of each county was determined using link-leve l ADVMT. The fractions are shown in Table 2. They were nearly identical for 1998 and 2015. In the Puget Sound area (King, Pierce, Snohomish Counties), the program started in a smaller geographic area in 1983 and expanded to the current area in 1993; thus , there are two programs listed for these counties.

Table 2: I/M Program Area County Fractions

State County no I/M I/M I/M expansion*
WA Clark 0.19 0.81 n/a
WA King 0.06 0.72 0.22
WA Pierce 0.09 0.00 0.91
WA Snohomish 0.19 0.34 0.47 OR Clackamas 0.27 0.73 n/a OR Multnomah 0.09 0.91 n/a OR Washington 0.11 0.89 n/a * refers to Puget Sound 1993 expansion area

2.2 Emission Rates: MOBILE6.2 Emission rates in grams per mile were generated using the EPA model MOBILE6.2. iv Emission rates were generated for temp eratures ranging from 50 to 100 degrees Fahrenheit. The emission rate s by temperature could then be matched to hourly, gridded temperatures that were made available th rough meteorological modeling for the episode. This produces a more accurate inventory thr oughout the region, since temperature is a sensitive input in MOBILE6.2. Currently this method discards diurnal hydrocarbon emissions (evaporative emissions from hea ting and cooling throughout the day). This results in a small underestimation of the total hyd rocarbon emissions.

ODEQ calculated emission rates for the Ju ly 1998 episode (and 2015 projection) for counties in Oregon in suppor t of the Portland-Vancouver ozone modeling work. ODEQ provided the data to Ecology for use in th e Puget Sound ozone modeling project. PM 10 was included, but not PM 2.5 . PM 2.5 was calculated from the PM 10 by multiplying by the ratio of PM 2.5 to PM 10 estimated for Clark County, WA.

The remainder of the section describes the parameters used to model emissions in Washington State counties. The parameters de scribed are: evaluation month, registration distribution, humidity, inspect ion and maintenance (I/M) prog ram, and fuel parameters for Reid vapor pressure (RVP), and gasoline and diesel sulfur content.84 2.2.1 Evaluation Month

The MOBILE model can calculate emission factor s that represent a January 1 or July 1 fleet age for the given year. July 1 was chosen since it was the episode month.

2.2.2 Registration Distribution Washington has a substantially ol der fleet than the national aver age. To model the effect of the older fleet, local vehicle informati on was used to calculate the vehicle age registration distribution. The distributions are age curves for each major vehicle type representing the fraction of vehi cles that area 1, 2, 3, ... 25 years old. Vehicle licensing records from the Washington State Departme nt of Licensing (DOL) were used to calculate the distributions for all vehicle types except buses. v, vi DOL does not register all transit and school buses; therefore, alternat e sources of information were obtained. Transit bus data came from Federal Tr ansit Administration Annual Report data. vii School bus data came from national default distribut ions and the Washington State Office of the Superintendent of Public Instruction. viii Distributions were assigned to evaluation years as shown in Table 3.85 Table 3: Registration Distribution Assignments

Type Evaluation Year Year of Data Source
1998 1999 DOL vehicles except buses 2015 2004 DOL
1998 1999 FTA transit buses
2015 2003 FTA 1998 EPA default EPA school buses 2015 2004 OSPI

2.2.3 Humidity Long-term average (1961-1990) humidity and pressure from several meteorological stations in Washington, Oregon and Idaho were used to develop the MOBILE6 input humidity parameters. ix

The humidity value required in MOBILE6 is a mixing ratio in mass of water vapor per unit mass of dry air. A calculation formul a based on relative humidity, temperature and pressure was distributed with MOBILE6. x EPA guidance was used to calculate the ratios using data from reference ix.

Humidity values were assigne d to counties on the basis of predominant areas of traffic and population. In eastern Washington counties were assigned a value of 40. In western Washington and Oregon, values ranged from 48 to 55. Longterm humidity values at several sites are shown in Table 4.

Table 4: July Humidity Mixing Ratios, Longterm Averages

station humidity station humidity Astoria 52 Spokane 40 Quillayute 48 Yakima 40 Olympia 46 Lewiston 45
Portland 55 Pendleton 38 SeaTac 52

2.2.4 Inspection and Maintenance (I/M) Program Vehicle Inspection and Maintena nce (I/M) programs are operate d in areas of Clark, King, Pierce, and Snohomish Counties. Input para meters have been tracked to allow modeling of the program. xi-

1 1 1 xv

MOBILE6.2 allows specification of up to seve n I/M record sets to define an overall I/M

program. xvi The records sets are shown in Table 5 through Table 8. In the Puget Sound area (King, Pierce, Snohomis h Counties), the program started in a smaller geographic area in 1983 and expanded to the current ar ea in 1993. The expansion area files are identical to the original area files except for the start year. The most recent data available was used to define the 2015 program.86 Table 5: I/M Program Parameters: King, Pierce and Snohomish Counties, 1998

Parameter set 1 set 2 Prog Start Yr 1982 original 1993 expand 1982 original 1993 expand Prog End Yr 2050 2050 Test Freq biennial biennial Program Type test only test only Test Type loaded/idle 2500/idle
Model Yrs 1968-2050 1968-2050 Vehicle Types light duty gasoline heavy duty gasoline Stringency 28% 28% Compliance 95% 95% Waiver pre-81 3% 3%
Waiver 1981+ 8% 8% Exempt Age 1967 1967 Grace Age 1 yrs 1 yrs Tech Training yes yes

Table 6: I/M Program Parameters: Clark County, 1998

Parameter set 1 set 2 set 3 Prog Start Yr 1993 1998 1993 Prog End Yr 2050 2050 2050 Test Freq biennial biennial biennial Program Type test only test only test only Test Type ASM 2525 phase-in* gas cap 2500/idle

Model Yrs 1968-2050 1968-2050 1968-2050 Vehicle Types light duty gasoline all gasoline heavy duty gasoline Stringency 36% 36% 36% Compliance 93% 93% 93% Waiver pre-81 4% 4% 4%
Waiver 1981+ 7% 7% 7% Exempt Age 1967 1967 1967 Grace Age 1 yrs 1 yrs 1 yrs Tech Training yes yes yes * ASM = acceleration simulation mode87 Table 7: I/M Program Parameters: King, Pierce and Snohomish Counties, 2015

Parameter set 1 set 2 set 3 set 4 set 5 set 6 Prog Start Yr 1982 original 1993 expand 2002 1982 original 1993 expand 2002 1982 original 1993 expand 2002

Prog End Yr 2050 2050 2050 2050 2050 2050 Test Freq biennial biennial biennial biennial biennial biennial Program Type test only test only test only test only test only test only Test Type ASM 2525 phase-in* gas cap OBD exhaust ** OBD Evap + gas cap** 2500/idle gas cap

Model Yrs 1968-1995 1968-1995 1996-2050 1996-2050 1968-2050 1968-2050 Vehicle Types light duty gasoline light duty gasoline light duty gasoline light duty gasoline heavy duty gasoline heavy duty gasoline Stringency 39.3% n/a 39.3% n/a 39.3% n/a Compliance 91.4% 91.4% 91.4% 91.4% 91.4% 91.4% Waiver pre-81 7.5% 7.5% 7.5% 7.5% 7.5% 7.5% Waiver 1981+ 11.5% 11.5% 7.8% 7.8% 10.8% 10.8% Exempt Age 25 yrs 25 yrs 25 yrs 25 yrs 25 yrs 25 yrs Grace Age 5 yrs 5 yrs 5 yrs 5 yrs 5 yrs 5 yrs Tech Training yes yes yes yes yes yes * ASM = acceleration simulation mode ** OBD = on-board diagnostics

Table 8: I/M Program Parameters: Clark County, 2015

Parameter set 1 set 2 set 3 set 4 set 5 set 6 Prog Start Yr 1993 1998 1993 2002 1993 1998 Prog End Yr 2050 2050 2050 2050 2050 2050 Test Freq biennial biennial biennial biennial biennial biennial Program Type test only test only test only test only test only test only
Test Type ASM 2525 phase-in* gas cap OBD exhaust ** OBD Evap + gas cap** 2500/idle gas cap

Model Yrs 1968-1995 1968-1995 1996-2050 1996-2050 1968-2050 1968-2050
Vehicle Types light duty gasoline light duty gasoline light duty gasoline light duty gasoline heavy duty gasoline heavy duty gasoline Stringency 42.4% n/a 42.4% n/a 42.4% n/a Compliance 91.0% 91.0% 91.0% 91.0% 91.0% 91.0% Waiver pre-81 4.9% 4.9% 4.9% 4.9% 4.9% 4.9% Waiver 1981+ 15.8% 15.8% 5.3% 5.3% 8.3% 8.3% Exempt Age 25 yrs 25 yrs 25 yrs 25 yrs 25 yrs 25 yrs Grace Age 5 yrs 5 yrs 5 yrs 5 yrs 5 yrs 5 yrs Tech Training yes yes yes yes yes yes * ASM = acceleration simulation mode ** OBD = on-board diagnostics

2.2.5 Reid Vapor Pressure (RVP) RVP varies by year, geographic area, and time of year. RVP values were determined using a combination of legal limits, voluntary agreements and fuel survey information.88 Legal RVP Limits The legal summertime RVP limits are 7.8 ps i for Oregon, and 9.0 psi for Washington. xvii Because of the proximity to Portland, Clark County gasoline was assumed to follow Portland RVP requirements.

Voluntary Programs Beginning in 1999, the Western States Petroleum Associati on entered into a voluntary agreement with the Puget Sound Clean Air Ag ency to supply gasoline with RVP 7.8 psi during July and August. King, Pierce, and Snohomish Counties are covered by the agreement. All western Washington coun ties except San Juan, Skagit and Whatcom Counties are assumed to be affected by the agreement. xviii, xix

Fuel Surveys Fuel surveys to determine actual RVP are performed periodically. Actual Washington RVP for July 1996 was obtained from the West ern States Petroleum Association (WSPA) for a prior ozone modeling project. xx Survey data from the Alliance of Automobile Manufacturers (AAM) was made available thr ough the Environmental Protection Agency (EPA) for 1999. xxi The most recent survey data ava ilable was for the year 2002. It was made available through the Western Regional Air Partnership (WRAP) and was derived from a combination of AAM data, and da ta collected by TRW/Northrop-Grumman (formerly the National Institute for Pe troleum and Energy Research, NIPER). xxii, xxiii

RVP Determinations Four geographic groups were de fined based on RVP values de scribed in the paragraphs above. RVP assignments for the 1998 ba se case and the 2015 projection without voluntary low RVP control case were identica l. For the 2015 proj ection with voluntary low RVP control case, the RVP value for c ounties affected by the RVP agreement was set to the agreement level. A ll RVP assignments are shown in Table 9.

Table 9: Fuel RVP Assignments, psi

case Clark San Juan, Skagit, Whatcom Other Western WA Eastern WA
1998 7.8 8.7 8.7 8.5 2015 w/o agreement 7.8 8.7 8.7 8.5 2015 w/agreement 7.8 8.7 7.8 8.5

2.2.6 Gasoline Sulfur Cont ent (base case 1998 only) Prior to the Tier 2 Gasoline Sulfur regulati ons taking effect in cal endar year 2000, sulfur content of gasoline is input into MOBILE6. Beginning in 2000, gasoline sulfur content is calculated internally in MOBILE6.2 based on the regulatory low-sulf ur gasoline phase-in schedules. EPA posted count y fuel parameters used to develop 1996 emissions inventories to their NEI FTP site. xxiv EPA notes two sources for the fuel parameter information: AAM and TRW/NIPER. xxv, xxvi The 1996 data was used to develop the 1998 base case sulfur content parameter. The value for eastern Washington counties was 325 ppm, and the value for west ern Washington counties was 318.89 2.2.7 Diesel Sulfur Content The Western Regional Air Partne rship contracted with Envir on to prepare onroad mobile source inventories for 1996. Environ collected information on summer and winter diesel sulfur content for eastern and western Washington. xxvii The value for eastern Washington was 310 ppm, and the value for western Washington was 260 ppm. These values were used for the 1998 base case. In 2015, diesel fuel will be required to meet a sulfur limit of 15 ppm.

2.2.8 Example Emission Rates The counties modeled were grouped according to control program characteristics. In Oregon, four groups were necessary: Clackamas County, Multnomah County, Washington County, and all othe r Oregon counties. In Washi ngton, there were separate groups for Clark, King, Pierce and Snohomi sh Counties, and groups for the eastern Washington counties, San Juan/Skagit/Wha tcom Counties, and all other western Washington Counties. Example TOG, NOx, CO and SO 2 emission rates at 75 degrees Fahrenheit are shown for each area in Table 10. Particulate ma tter (PM) is made up of several components: lead, organic and elementa l carbon, sulfate, and brake and tire wear.
PM 10 , PM 2.5 and NH 3 showed very little variation am ong the areas; they are shown in Table 11.

Table 10: Example TOG, NOx, CO and SO 2 Emission Factors in grams per mile

1998 base 2015 with RVP agreement 2015 without RVP agreement area TOG NOX CO SO2 TOG NOX CO SO2 TOG NOX CO SO2 Oregon Clackamas 2.205 3.162 22.736 0.094 0.533 0.734 5.452 0.009 0.533 0.734 5.452 0.009 Multnomah 1.994 3.034 20.788 0.094 0.517 0.715 5.171 0.009 0.517 0.715 5.171 0.009 Washington 2.018 3.048 21.004 0.094 0.519 0.717 5.203 0.009 0.519 0.717 5.203 0.009 Other Oregon 3.059 3.680 30.636 0.097 0.596 0.810 6.591 0.009 0.596 0.810 6.591 0.009 Washington Clark 2.568 3.469 24.934 0.099 0.761 0.905 6.625 0.009 0.761 0.905 6.625 0.009 King 2.664 3.498 23.166 0.099 0.745 0.892 6.396 0.009 0.778 0.892 6.396 0.009 Pierce 2.710 3.522 24.890 0.099 0.749 0.896 6.452 0.009 0.782 0.896 6.452 0.009 Snohomish 2.714 3.516 24.517 0.099 0.762 0.910 6.641 0.009 0.796 0.910 6.641 0.009 SanJuan Skagit Whatcom 2.916 3.566 28.406 0.099 0.905 1.022 8.167 0.009 0.905 1.022 8.167 0.009 Other W WA 2.916 3.566 28.406 0.099 0.865 1.022 8.167 0.009 0.905 1.022 8.167 0.009 E WA 2.886 3.608 28.277 0.104 0.896 1.036 8.143 0.009 0.896 1.036 8.143 0.009

Table 11: Example PM 10 , PM 2.5 and NH 3 Emission Factors in grams per mile

Exhaust Brake Wear Tire Wear year

NH 3 PM 10 PM 2.5 PM 10 PM 2.5 PM 10 PM 2.5 1998 0.086 0.069 0.062 0.013 0.005 0.009 0.002
2015 0.092 0.012 0.011 0.012 0.005 0.010 0.00290 2.3 Emissions Estimates The emission rates in grams per mile calcu lated by temperature were multiplied by the gridded ADVMT estimates by matching to the ho urly grid temperatures and geographic area. Temporal adjustments to the ADVMT based on month, day of week, and hour of day were made within the SMOKE em issions processor. See section 0 for more information.

3 Temporal Projection Factors The 2015 projections were made using a comb ination of growth factors and specific inventory calculations for 2015. Details are provided below.

3.1 Washington and Oregon

Onroad sources and nonroad sources calcu lated with EPA's NONROAD model were specifically estimated for 2015. The onroa d projection is described in section 0. PSCAA provided 2015 nonroad inventories for all s ources included in the NONROAD model for Oregon and Washington.

ODEQ's 2015 projection was used for point s ources, area sources, ships, locomotives, and aircraft in Oregon. PSCAA provided growth factors for area sources, ships, locomotives, and aircraft for each Washington county. PSCAA assumed that Washington point sources would remain the same as the 1998 base year.

3.2 British Columbia

Year 2000 emissions data for British Colu mbia was provided by the Greater Vancouver Regional District and Environment Canada. PSCAA provided projection factors to 2015 for area sources, ships, locomotives and rail maintenance, and aircraft and aircraft support. For most of the individual s ource categories, they used Whatcom County growth rates as surrogates.

To project the remaining nonroad sources included in the NONROAD model, Ecology calculated Whatcom County emissions fo r 2000 and 2015 using the NONROAD model. Ecology calculated growth factors for each nonro ad source and pollutant as the ratio of 2015 emissions to 2000 emissions.

The methodology for projecting onroad mob ile sources was similar to the nonroad methodology. Emission rates were calculat ed for Whatcom County for the years 1998 and 2015 as described in section 0. Year 2000 estimates were calculated using a linear interpolation between 1998 and 2015. Ecology ca lculated growth factors for each of the three onroad vehicle classes (gasoline, light duty diesel, heavy duty diesel) and pollutants as the ratio of 2015 emissions to 2000 emissions (at 75 degrees Fahrenheit).

4 Emissions Inventory Data Formatting for SMOKE All of the emissions inventory data was form atted for the SMOKE emissions processor. Much of the required supporting information (t emporal, spatial, chem ical) was available through previous modeling projects, although so me additions and updates were required. The formatting was accomplished using ex isting Practical Extraction and Report91 Language (Perl) scripts, with minor modifi cations as necessary to accommodate input data formats.

4.1 Emissions Files

The emissions files contain emissions estimates by source in either tons per year or tons per day. Point sources are defined individua lly, and area/mobile sources are defined by county. The emissions are listed by sour ce classification category (SCC) codes.

All British Columbia emissions inventory da ta (except the 2015 pr ojection factors) was made available in SMOKE format by Envi ronment Canada. PSCAA provided area and nonroad mobile source inventories, and point source inventories for Oregon and sources within PSCAA's jurisdiction. Ecology provi ded onroad inventories, and point source inventories for Washington sources outside of PSCAA's jurisdiction. The base year was 1999 for point, area, and ships/locomotives/aircr aft. The base year was 1998 for onroad, and nonroad sources estimated using EPA's NONROAD model.

Ecology formatted all emissions data for the SMOKE emissions processor. Onroad mobile sources were formatted for a special temperature adjustment program designed by WSU to work in the SMOKE framework. Mi nor edits were made to data provided by PSCAA, such as removing TRI facilities fr om the point source file, and removing all biogenic and fires from the ar ea source file (SCC 27* and 28*).

4.2 Spatial Allocation Files

Point sources have known locations that can be accurately locat ed in the modeling domain. Area and mobile sources often canno t be specifically located in the modeling domain; therefore, surrogates which can be accu rately located are us ed to allocate the sources spatially. For example, pleasure cr aft cannot be individua lly located, but they can be assigned to areas of open water. Each area/mobile source is assigned to a spatial surrogate. Surrogates used in this project include (but are not lim ited to) population, land use and land cover, mileage on roadways, railro ads, and major airports. Grid allocations of each spatial surrogate are given as the fr action of each county’s total surrogate value residing in the given grid.

All of the surrogates used had been defined in previous projects except for Puget Sound area mileage on roadways. PSRC provided ADVMT by roadway link for the base and projection years. This data was used to create new mileage spatial surrogate files.

Sources are assigned to spatial surrogates via the source SCC code. Several SCC codes had not been used in prior modeling proj ects, and thus had no existing surrogate assignment. These codes were assigned a ppropriate surrogates in consultation with PSCAA.

4.3 Temporal Allocation Files Emissions must be allocated by hour. Initial emissions estim ates were annual estimates. To allocate emissions by hour, three temporal profiles are applied: month, day of week,92 and hour of day. For each of the profiles, the temporal allocations are defined as the fraction of total profile activity occurring in the given timeframe.

The profiles are assigned to emissions sour ces by county and SCC code. Several SCC codes had not been used in prior modeling projects, and thus had no existing profile assignments. These codes were assigned a ppropriate profiles in consultation with PSCAA.

4.4 Chemical Speciation Files In order to model ozone concentrations, hydr ocarbon, nitrogen oxide, and sulfur oxide emissions must be speciated into chemi cal groups defined by the air quality model's chemical mechanism. Groups are defined ba sed on reactivity in the atmosphere. The SAPRC99 chemical mechanism groupings were used for this project. Hydrocarbon, nitrogen oxide, and sulfur oxide emissions are given by SCC code in the emissions files. Each SCC code – pollutant combination is as signed to a chemical speciation profile. Several SCC codes and pollutant names had not been used in prior modeling projects, and thus had no existing profile assignments. These code s were assigned appropriate profiles in consultation with PSCAA and WSU.

New profiles were added for onroad mobile PM 2.5 and PM 10 exhaust, brake wear and tire wear. The exhaust profiles were the same as those used in Airpact and other PM modeling projects. Brake a nd tire profiles were taken from EPA's SPECIATE system and were 34004 and 34008, respectively. Per guidance posted on EPA's emissions modeling website (http://www.epa.gov/ttn/chief /emch/speciation/), the fraction of the profiles listed as organic carbon (OC) was multiplied by 1.2 to convert to primary organic aerosol (POA). xxviii

4.5 Projection Files The projection factors described in section 3 were compiled into SMOKE's projection file format.93 i HPMS Miles and VMT by County . Washington State Department of Transportation. Information from 1998, 2000 and 2004 HPMS databases.

ii Forecast of Fuel, Vehicles, and Related Data Through 2015; Forecast Starts in FY04, (Forecast 0406). Washington State Department of Transportation.

iii Hertzog, Barbara and Borden, Hank. Adjustment Factors for Vehicle Miles Traveled

. Washington State Department of Transportation. July 1992.

iv MOBILE6.2. US Environmental Protection Ag ency. Model executable file dated September 24, 2003.

v Department of Licensing electronic data as ava ilable through the Department of Ecology's I/M Warehouse database. Data from July 1999 to December 2000.

vi Department of Licensing electronic data. Database snapshot July 2004.

vii National Transit Database Data Tables. Tables titled "Age Distribution of Active Revenue Vehicle Inventory: Details by Transit Agency Directly Operated Service." Federal Transit Administration. Data tables for 1999 and 2003. http://www.ntdprogram.com/NTD/ntdhome.nsf/ Docs/NTDPublications?OpenDocument#

viii Spreadsheet schoolbuses2004.xls (originally titled Buses for DOE 24Aug04.xls ). Transmitted from Allan J. Jones, Director of Pupil Transpor tation and Traffic Safety Education, Office of Superintendent of Public Instruction to Mich ael Boyer, WA Department of Ecology on Sept. 28, 2004.

ix Western Climatic Data Center, Western US Climate Historical Summaries, Local Climate Data Summaries (June 20, 1997).

x Spreadsheet Rel_hum1.xls . U.S. Environmental Protection Agency. Spreadsheet dated Jan 16, 2002.

xi IM Compliance Rate . Audit of Vehicles with July 1998 Expiration Date. Department of Ecology Air Quality Program. Aug. 4, 1999.

xii 1999/2000 Emission Test Data for Washington State - (Puget Sound, Spokane, Vancouver) - GVW < 8501 . Washington State Department of Ecology. March 20, 2002.

xiii Personal conversations with John Raymond, Department of Ecology.

xiv "I/M Compliance Rate, Calculated from Dece mber 2002 pre-bill, Tests counted from July 2002 to August 2004." Calculated from Dept . of Ecology I/M Database and Dept. of Licensing Vehicle Registration databases. August 27, 2004.

xv Stringency and Waiver Rates calculated from De pt. of Ecology I/M Database from tests taken from July 2002 to June 2004. August 2004.

xvi User's Guide to MOBILE6.0 Mobile Source Emission Factor Model . United States Environmental Protection Agency. Air and Radiation. EPA420-R-02-001. January 2002.

xvii 40 CFR 80.27.94 xviii Email from John Anderson, Puget Sound Clean Air Agency to Sally Otterson, Washington State Department of Ecology. Subject RE: question on voluntary low RVP agreement with WSPA . February 2, 2006.

xix Email from John Anderson, Puget Sound Clean Air Agency to Sally Otterson, Washington State Department of Ecology. Subject RE: RVP county assignments . February 9, 2006.

xx Email from John Anderson, PSCAA to Sally O tterson, Ecology. Transmittal of fuel RVP information provided by the Western States Petroleum Association. Sent Thu 3/8/2001 4:05
PM.

xxi Personal conversation with Maureen Mullen, P echan and Associates (under contract to EPA). 1996 and 1999 winter and summer survey data from the American Automobile Manufacturers' Association. January 4, 2001.

xxii Memo from Philip Heirigs and Joe Roeschen, Sierra Research to Alison Pollack, ENVIRON International Corporation. Subject: Development of Calendar Year 2002 County-Level Fuel Specification Data for the WRAP Modeling Domain . Dec. 14, 2004.

xxiii Spreadsheet of seasonal fuel RV P, sulfur and oxygen levels titled WRAP_Fuel_2002_toEnviron_121404.xls . Sierra Research.

xxiv Final_Fuel1996V3.xls as posted to the 1999 NEI HAPs Website

xxv Technical Description of MOBILE6.2 and Guidance on Its Use for Emission Inventory Preparation, Draft Report . USEPA Air and Radiation. EPA420-R-02-011. February 2002.

xxvi Documentation for the 1999 National Emissions Inventory Version 2.0 for Criteria Air Pollutants and Draft Version 3.0 for HAPs. On road Sources. Prepared for: Office of Air Quality Planning and Standards, Emission Fact or and Inventory Group, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711. Prepared by: E.H. Pechan & Associates, Inc., 5528-B Hempstead Way, Spri ngfield, VA 22151. October 2002. Attachment A: County-Specific Fuel Parameters for 1990 , 1996, and 1999 Toxic Emissions Modeling (Preparation for MOBILE6.2 Model Runs). Pr epared for: Emission Factor and Inventory Group (D205-01), Emissions, Monitoring and Analysis Division, U.S. Environmental Protection Agency, Research Triangle Park, No rth Carolina 27711. Prepared by: Eastern Research Group, Inc., 1600 Perimeter Park Driv e, Morrisville, North Carolina 27560. October 9, 2002

xxvii Printout from Environ file: F:\WRAP MSE\Task 3\Default inputs\WRAP_County_Fuel_sulfur.xls:Gasoline. Onro ad gasoline and diesel fuel sulfur levels for winter and summer by county.

xxviii Report from Mr. William Hodan, Pacific Environmental Services to Ron Ryan, Emission Factor and Inventory Group, Environmental Protection Agency. Subject: Recommendations for the Update and Improvement of Existing PM2.5 Split Factors . Contract 68-D-02-104 / WA 1-05. Sept. 29, 2003.