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 2006 
ii

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 concentrations 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 calculated 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 run by changing VOC and/or NOx emissions 

as well as removing individual 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 emissions 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 test for the Puget Sound area. .......................................... 55 
iii

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 speed predicted by MM5 compared with 

observations at the Sea-Tac airport....................................................................... 11 

Figure 6. Surface temperatures and wind speed 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 observed 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 levels 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 run and 2015 low RVP run at 16 

PDT on July 27. .................................................................................................... 49 

Figure 23a-c. Ozone difference plot between the 2015 high RVP and low RVP run on 

July 26-28, 2015. (2015 high RVP – 2015 low RVP) ......................................... 51 
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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 Summaries .......................................................................................................71 

Emissions Inventory Limitations .......................................................................................73 

Comparisons between Seattle and Portland Area Emissions.................................74 

VOC/NOX 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 NOX 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/ NOX Ratios for King and Multnomah Counties, 1998 Ozone Event.......75 

Table 8. VOC/ NOX Ratios for Puget Sound Area, 1998 Ozone Event ..........................76 

Appendix A Figures 

Figure 1. Ozone 8-hour concentrations 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............................................................................................................ 78 
v

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 SO2 Emission Factors in grams per mile................................. 89

Table 11: Example PM10, PM2.5 and NH3 Emission Factors in grams per mile...................................... 89
1 

1 Summary 

In the Puget Sound area, continued population growth, changes in transportation and 

shipping, and technological changes will affect future emissions of Volatile Organic 

Compounds (VOC) and Nitrogen Oxides (NOx), which are precursors to ozone 

formation. It is expected that total mobile source emissions may decrease, while 

emissions from marine shipping may increase 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 included multiple scenarios to determine if the 

Puget Sound area is likely to maintain federal attainment status of the ozone national 

ambient air quality standard 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 highest ozone levels in recent history in the 

Puget Sound area, and is the most extreme meteorology available. 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. Air quality during this 

period was simulated using the Mesoscale Meteorological model version 5/ Sparse 

Matrix Operator Kernel Emissions Modeling System / Community Multi-scale Air 

Quality Modeling System (MM5/SMOKE/CMAQ). 

In the first phase of this study, the model performance was evaluated. For the 

meteorology simulation, the Noah Land-surface model (Chen and Dudhia, 2001) was 

used along with observational nudging with available surface wind observations, and 

MM5 showed acceptable performance in comparison to an independent set of surface 

wind and temperature data. “Nudging” is a numerical process that adjusts the numerical 

meteorological fields toward available observations 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 guidelines (EPA, 1999 and EPA, 2005). Results 

from time series analyses, scatter plots of observed vs. predicted, and Quantile-Quantile 

plots reflected the generally good agreement 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 run). The second applied the same growth 

factors, but without the low RVP control strategy (high RVP run). 

In western Washington counties within the modeling domain, the total NOx and VOC 

emissions were projected to decrease approximately 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 mobile sources. Emissions of NOx were 

projected to increase (up to 33%) for the nonroad mobile source category due to 

substantial growth of marine shipping operations. For the 2015 low RVP run, ozone 

reductions around 10 ppb were predicted at the Enumclaw and Pack Forest monitoring 
2

sites where maximum ozone was observed in the 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 run, 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 the range from 0.75 to 

1.25 times the base case emissions. Second, the 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% increase 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 results 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 concentrations 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%. Removing nonroad or onroad mobile emissions 

resulted in 13% and 10% reductions in maximum ozone levels, respectively. 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 compare to the federal standard, were calculated 

and demonstrated that Puget Sound area monitoring sites remain in federal attainment 

status in 2015. DVFs are based on current design values from actual monitoring sites, 

and take into account the uncertainty in model performance for peak ozone by using 

model results in a relative sense. In this study, the model under-predicted peak ozone 

levels at the Enumclaw monitoring 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-prediction 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 sites (Weyerhaeuser Mill, Pack Forest, and 
3

Lake Sammamish) have ozone data for all five 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 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, 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 Sammamish 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 growth in marine activities 

(shipping and excursion). These changes may off-set precursor emissions reductions 

from cleaner vehicles that are being introduced into the fleet continuously. The Puget 

Sound Clean Air Agency (the Agency) has concerns about the potential 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 numerical 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 region 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 Pressure (RVP) gasoline in summer months. 

Low RVP summer gasoline is one of the main strategies that came out of the 2001 

CO/Ozone stakeholder process. This modeling effort supports the charge from the 2001 

stakeholders (PSCAA, 2001): 

“...prudence requires that early identification 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 this study and presents the results from the 

modeling analyses. This study involved the use the Mesoscale Meteorological model 

version 5 (MM5) nudged with available surface meteorological data. The meteorological 

output was used to drive the Community Multi-scale Air Quality (CMAQ) model. 

Emission inventories were developed using the Sparse Matrix 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 reflect expected changes for the year 2015, and 

additional simulations were performed to assess potential ozone levels 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 modeling domain and at specific air quality monitoring stations. 

The 8-hour average is the relevant 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 different, 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 MOBILE6.2), 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 meteorological field for air quality modeling. 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 and 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 Mountain range on the east. Vertically, 38 sigma layers were 

specified. The emission and CMAQ modeling domain 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 domain 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 sites. Elevated ozone was observed at the 

Pack Forest, Enumclaw (Weyerhaeuser Mill and Mud Mountain), Yelm, Lake 

Sammamish, Carus and Milwaukie monitoring sites. 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 the region are characterized 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 conditions 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 case for a State Implementation Plan analysis 

conducted for the Portland, OR/Vancouver WA area, 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 concentrations 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 archived 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 domain. Nudging is a numerical process that 

adjusts the numerical meteorological fields toward available observations or analysis 

fields. For analysis nudging, 3D nudging was 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) were 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 included use of the Kain-Fritsch cumulus 

parameterization scheme (Kain and Fritsch, 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 conducted 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 Meteorology-Chemistry Interface Processor 

(MCIP) so that the meteorological data could 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. During the MCIP run, all planetary boundary layer 

(PBL) parameters and radiation fields were 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 Sound 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 northwesterly 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 overpredict both the daytime and nighttime temperatures 

by a few degrees. The observed winds were mostly 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 Figures 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 direction, 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 levels at Enumclaw on these two days as described in later 

sections. At Portland, relatively low wind speed and above-normal temperatures also 

occurred, and the model simulated both surface 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 speed predicted by MM5 compared with 

observations at the Sea-Tac airport. 

Figure 6. Surface temperatures and wind speed 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

Time

Temperature ( degree C)

0

2

4

6

8

10

12

14

16

18

Wind speed (m/s)

T_obs

T_pre

WS_o

Ws_p_10m

PDX

0

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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

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Temperature ( degree C)

0

2

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12

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Wind speed (m/s)

T_obs

T_pre

WS_o

Ws_p_10m
12

Figure 7. Surface wind direction predicted by MM5 compared with observations at the 

Sea-Tac airport. Wind calms (no direction) 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

Time

Wind direction (degree)

Wd_o

Wd_p

Wd_o_calm

PDX

0

90

180

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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

Time

Wind direction (degree)

Wd_o

Wd_p

Wd_o_calm
13

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 higher than the typical PBL heights observed in 

the region. This might also cause the underprediction 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. However, 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 wind 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

pre
16

The meteorological simulations were also evaluated using the mean bias, mean gross 

error, and index of agreement for wind speed, direction, temperature, and relative 

humidity (shown in Table 2). 

The index of agreement is defined as: 

∑

∑

=

=

− + −

−

= − N

i

i i

N

i

i i

P O O O

P O

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 correlation between the two. The mean bias 

is calculated as: 

∑=

= −

N

i

Pi Oi NMB

1

( ) 1 (2) 

and the mean gross error is calculated as: 

∑=

= −

N

i

Pi Oi NME

1

| | 1 (3) 

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

Ecology network sites were selected as independent sites and withheld from 

observational nudging for surface winds. The locations of the surface stations are shown 

in Figure 11. For all these variables, the index of agreement was close or above 0.8, 

indicating good agreement between 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 sensitivity levels at different monitoring sites 

designating low wind speeds as 0 m/s. As for 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 calculated separately for the overall stations and 

those withheld from observational nudging; these 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 statistics 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 that withheld from nudging 
18

3.5 Photochemical Modeling Methods and Boundary Conditions 

The U.S. EPA Models-3 Community Multi-scale Air Quality (CMAQ) 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 precursors and oxidants, particulate matter, and 

airborne toxics. It incorporates the output fields from the MM5/MCIP meteorological 

simulations and emissions derived 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 including aqueous chemistry and aerosol 

dynamics was employed. The new aerosol module (aero4) is different from previous 

versions in that it includes emissions and thermodynamics of sea-salt. The chemical 

speciation of the emission inventory was performed by SMOKE according to the 

SAPRC99 mechanism. The Euler Backward Interactive (EBI) solver was used to solve 

the chemical kinetic equations. The new global mass-conserving scheme (yamo) was 

used as the advection scheme. This scheme uses the Piecewise Parabolic Method (ppm) 

in horizontal and drives the vertical velocity at each grid cell from the continuity 

equation. Multiscale and eddy diffusion are used 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 derived by averaging over ten years of July 

runs (1990-1999) based upon a long term climate 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 converted to equivalent pressure 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 sites 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 Island; WR: Wishram; MV: Mt. View; MW: 

Milwaukie; CA: Carus; TN: Turner) 

Observed and simulated 1-hr surface ozone concentration time series are shown in Figure 

13. Monitoring sites are presented in order, from north to south. It is important to note 

that the y-axes, depicting 1-hour ozone concentrations, differ between sites. At Pack 
20

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 predicted ozone patterns were 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 standards were recorded. The model was able to 

predict the elevated ozone levels on the first high ozone day (July 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 two days. The model also had difficulty in 

capturing the nighttime low ozone concentrations. At Beacon Hill, about five miles 

southeast of downtown Seattle, maximum observed ozone levels were lower than 60 ppb 

during the entire episode. Model 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 Packwood are all located in areas of complex terrain. At 

Tahoma Woods, the model showed consistent overprediction of ozone. At Paradise, an 

average level around 50 ppb were observed with peaks occasionally reaching 90 ppb. 

The model appeared to match the average level, but missed the variations. At Packwood, 

observed ozone levels were less than 60 ppb during the entire episode, and the model 

prediction showed poor correlation 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 Canadian border. The model predicted daytime 

peaks fairly well, but tended to overestimate the nighttime levels. At Ranger, a rural site 

further east of I-5 corridor, the model captured the daytime peaks but overpredicted 

nighttime levels significantly. At Olympic National Park, observed ozone concentrations 

were below 30 ppb during most of the time except 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 were observed (Carus, Milwaukie, Woodland, 

Sauvie Island, Castlerock, Mt View, and Turner), the model had very good performance 

except for missing peaks at Carus and Milwaukie on the first high ozone day. At the two 

sites that are influenced heavily by complex 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 blue lines show model predictions.) 
22
23
24
25

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 error between observed and predicted 1-hr 

and 8-hr ozone concentrations. Only hours 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 predicted 8-hr daily maxima using all data 

pairs. Only hours with observations greater 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 predicted 8-hr daily maxima using all 

data pairs (Shown in Figure 15) 

Statistics were calculated for matched observations 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 monitoring site. The latter comparison is recommended as a way 

to account for typical errors in wind direction from the meteorological model. 
26

The normalized gross error is defined as: 

∑=

− =

N

i i

i i

O

P O

N

NE

1

1 | |

 (4) 

and the normalized bias is defined as: 

∑=

− =

N

i i

i i

O

P O

N

NB

1

1 (5) 

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

EPA suggested guidelines for ozone modeling (originally based upon 1 hr ozone 

concentrations) require that normalized bias to be within ±15% and normalized gross 

error to be within 35% (EPA, 1999). The overall mean normalized bias and error (as 

shown in Table 3) for 1-hr ozone was -3% and 21% respectively, which are well within 

the guidelines. When looking at the sites where high ozone levels were observed, the 

model performance was very good at Pack Forest (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 complex 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 bias around -15% at the two Enumclaw sites 

when looking at the exact grid. When taking 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 sites, especially on July 28 with bias around 

-40%. This could be related to the fact that 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 simulated 8-hr daily maxima (paired in space) 

is shown in Figure 14. When plotted with 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 showed 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) which 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 value from the surrounding 

5x5 grid. The plots showed generally good agreement between the observed and 
27

predicted concentration distributions, but there was some underprediction of the peak 

ozone and overprediction of lower ozone around 40 ppb. 

Table 3. Mean Normalized Bias and Error calculated by obs ozone greater 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 greater 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 greater 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 observed 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-Jul 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-Jul 0.078 0.092 0.093 18% 20% 

Sauvie_Island_OR 28-Jul 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 observed and predicted 8-hr daily maxima for 5x5 

(maximum 8-hour value) and 1x1 grids for July 1998 episode.

Figure 15. Quantile-Quantile plot of observed and predicted 8-hr daily maxima using all 

data pairs for 5x5 (maximum 8-hour value) 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 line
32

5 Sensitivity Analyses 

Sensitivity analysis was conducted to further 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 changes 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 emission 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% decrease in VOC, the x-axis of the point was 

plotted according to the changes in VOC only (shown 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 changes in maximum ozone concentrations on 

July 27 and 28. The pattern on July 26 was slightly 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 close to combining the changes from increasing 

NOx only and decreasing VOC only. 

The results obtained by removing each individual source category are also shown in 

Table 6. Within all source categories, biogenic 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 biogenic sources. In contrast, removing point 

sources resulted in very small changes. When anthropogenic area sources were removed, 

the ozone reductions were around 8%. Removing 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 reductions were up to 14%. However, when only 
33

NOx emissions were removed from nonroad or onroad sources, large 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 emissions

peak 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 emissions

peak 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 emissions

peak ozone (ppbV)

NOx

VOC

NOx&VOC

1.25NOx_0.75VOC
34

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 control strategies, two 

future emission inventories were developed: One involved projected growth assuming 

the use of low Reid Vapor Pressure gasoline 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 with the 1998 meteorology to simulate future 

conditions. The low and high RVP runs assumed gasoline vapor pressures of 7.8 and 8.7 

psi, respectively. Additional details on the low and high RVP emission inventories are 

included in Appendix A to this report. 

For the low RVP run, the emissions changes in VOC and NOx compared to the 1998 

base run are summarized in Table 7 for Tuesday, July 28. The table includes emissions 

estimates for northwest Oregon counties and western Washington counties included in 

the modeling domain. It should be noted that 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 sources 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 decrease 21% and 31% in western Washington. 

For the high RVP run, the emissions changes in VOC and NOx compared to the low RVP 

run are summarized in Table 8 for Tuesday, July 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 slightly 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 compared to the low RVP run. 

Although not included in the table below, biogenic emissions were assumed to be the 

same in 2015 as 1998. A description of the emission inventory projections is provided in 

the Appendix A to this report. 
36

Table 7. Anthropogenic VOC and NOx emissions 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/day 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 emissions 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 are also plotted. Daily maximum 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 note that the y-axes, depicting 1-hour ozone 

concentrations, differ between sites. 

The difference plots show ozone reduction bands 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 the two Enumclaw sites 

and Pack Forest as shown in Table 9. However, closer to and within the Seattle urban 

area, increases of ozone are predicted. These 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 ozone increase is also predicted when looking 

at 5x5 grids. 

The regional increase of ozone in the urban area can be explained by the increase of 

VOC/NOx ratios in the 2015 emissions inventory. 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 urban 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 been shown to be a reliable indicator of O3 

response to VOC or NOx emission changes (Tonnesen and Dennis, 2000b). Values of 

the ratio < 15 indicate the strongly radical-limited, VOC sensitive condition. Values > 46 

indicate strongly NOx-limited and therefore NOx sensitive region (Arnold et al., 2003). 

The predicted [O3]/[NOx] contours in the 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 -0.012 
42

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 -0.009 
43

Figure 20. Simulated 1-hr surface ozone concentrations for 2015 low RVP run compared 

to the observed the predicted ozone levels 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. 
44
45
46
47
48

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 run 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 for 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 individual monitoring sites, the increase is less 

than 0.3 ppb. 
51

23a 

23b 

23c 

Figure 23a-c. Ozone difference plot between the 2015 high 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 0.0002
53

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 0.0002
54

6.3 Modeled Attainment Test 

To analyze the potential to exceed the ozone standard 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 model uncertainty, especially uncertainty in the 

meteorology simulations of wind direction and Planetary Boundary Layer (PBL) heights, 

the ozone peaks were underestimated at the Enumclaw monitoring site on two of the 

three high ozone days in the 1998 baseline prediction. The underprediction was 

calibrated by using the model results in a relative sense, which is to apply the relative 

reduction factors (RRF) between the future 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. Within 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, they were used in the test. 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 reduction 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 baseline 8-hr maximum concentration. Last, 

the future design values (DVF) were estimated by multiplying the modeled relative 

reduction factor by the observation-based current design value: 

1998

2015

MMax

MMax RRF = (6) 

DVF = RRF ⋅ DVC (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, 2005). 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 

Sammamish with relative reduction factor of 0.970 and 58 ppb for the future design 

value. 
55

Table 11. Modeled attainment test 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 evaluation of numerical 

air quality models with specialized ambient observations: testing the Community 

Multiscale Air Quality modeling system (CMAQ) 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 Development, 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 Northwest. 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). NCAR Technical Note NCAR/TN-398#STR, 138 

pp. 

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: Convective parameterization for mesoscale models: 

The Kain–Fritsch scheme. The Representation of Cumulus Convection in Numerical 

Models, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 165–170. 

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. 

Tonnesen, G.S. and Dennis, R.L., 2000. Analysis 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 Analyses in Attainment Demonstrations for 
57

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 Implementation 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/conference/ei11/modeling/vukovich.pdf) 
58

Appendix A 

Emissions Inventory Documentation 

July 1998 Ozone Event 

Base and Projection Year 

in coordination with the Washington State Department of Ecology 
59

 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 Summaries .......................................................................................................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 NOX 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/ NOX Ratios for King and Multnomah Counties, 1998 Ozone Event.......75 

Table 8 VOC/ NOX Ratios for Puget Sound Area, 1998 Ozone Event ...........................76 

Figures 

Figure 1. Ozone 8-hour concentrations at Puget Sound area monitors.............................61 

Figure 2. Modeling Domain for Emissions Inventory......................................................63 
60 

Introduction 

The Puget Sound Clean Air Agency (The Agency) has 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 emissions 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 ten years will affect ozone precursors in the Puget Sound area. 

Lower vehicle emissions due to federal standards 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 years. Investigating the potential 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 represents a different, more severe meteorology for ozone 

formation than the previously-modeled 1996 episode. Also, significant technological improvements 

have been made since modeling the 1996 episode. For this modeling 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. WSU used the Mesoscale Meteorological version 

5 (MM5) with land surface model meteorology for meteorological modeling and the Community 

Multi-scale Air Quality Modeling System (CMAQ) for dispersion modeling. 

The Agency will use the results of this work for internal 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 are taken into account. The Agency has requested 

that WSU perform modeling runs for two different future 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 agreement effectively lowers the RVP of gasoline from 

8.7 psi to 7.8 psi, which in turn reduces some ozone precursor emissions. The Agency would like to 

determine whether low RVP gasoline is an effective strategy to help keep the 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 charge from the 2001 ozone stakeholder process2

: 

“...prudence requires that early identification 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.” 

The figure below shows the July 1998 ozone event chosen for this modeling exercise. Three days 

of the episode exceeded the 8-hour federal standard of 84 ppb (July 26th, 27th,and 28th). 

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

Emissions Inventory Approach 

The Agency compiled this emissions inventory along with partners to support the ozone modeling 

study performed by Washington State University (WSU). The Washington State Department of 

Ecology (Ecology) served as a main emissions inventory partner; their summary of onroad mobile 

emissions and emissions processing follows this report. The Agency and partners created and 

edited this emissions inventory from September 2005 through March 2006. A monthly conference 

call provided a forum to discuss inventory 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 State 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 considered to be similar to 1998 emissions, the year of the episode. 

1999 emissions were used as a surrogate for the base 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 sources that were determined using EPA models. 

A master list of source classification codes (SCCs) used in the project 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 estimates 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 with Washington State University’s “AIRPACT2” 

domain, and shown in Figure 2. Table 1 shows Oregon and Washington counties included in the 

modeling domain, along with estimated 1999 populations. 1999 population estimates were 

extremely close to 1998 estimates, within 2%.4,5,6

 

The Agency and partners collected emissions estimates for volatile organic compounds (VOC), 

nitrogen oxides (NOX), sulfur oxides (SOX), and carbon monoxide (CO) for all source categories. 

WSU also used fine particulate matter (PM2.5) information provided by its AIRPACT forecasting 

system to better inform modeling results.7

Modeling inventories require emissions 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 regional modeling initiatives formed the basis for 

making the allocations. The profiles were supplemented 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 Report for Public Access and Community Tracking. 

http://www.airpact.wsu.edu/. 
63

Figure 2. Emissions Inventory Modeling Domain 
64

Table 1. Washington and Oregon 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 16,861
65

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 July 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 Washington counties included in the modeling domain.9

 All 

of the sources have associated latitudes and longitudes. . After all emissions inventories were 

collected, Ecology processed point source 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 Columbia point source information directly to 

Washington State University for use in the AIRPACT 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 information 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/longitude coordinates were 

created using geocoding.13 

• Southwest Clean Air Agency and Oregon Department of Environmental Quality had already 

estimated Oregon 1998 point sources for the same July 1998 ozone episode for previous 

modeling work.14 Southwest Clean Air Agency provided 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 estimates 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 information 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 for 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 Portland/Vancouver Area. December 31st, 2005. 
66

various local air agencies in western 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 assumptions 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 examined wildfires that took place 

during the ozone event. None were 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 directly 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 available to estimate 

emissions from miscellaneous non-road mobile sources. These sources include but are not limited 

to lawn and garden, recreational marine, commercial construction equipment, 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). Annual 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 categories, 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 Agency. 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 scenarios when the final version became available in December 2005. 16 1998 SeaTac Meteorological Summaries 

17 www.wrh.noaa.gov/pqr/climate/pdx-clisummary.php, and Southwest Clean Air Agency
67

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 categories 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 west and east of 'the 

separation zone' shown in Coast Survey Charts published by National Oceanic & Atmospheric 

Agency.18 The Agency provided information on ship paths 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 emissions 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 documentation of the on-road mobile source modeling and 

data processing are provided in Ecology’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 heavy duty diesel, light duty diesel, and gasoline 

source groups. Emission factors were calculated for each area that had a unique combination of 

Reid Vapor Pressure (RVP) and Inspection and Maintenance (I/M) Plan. 

 

18 National Oceanic and Atmospheric Agency, National Ocean Service, 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 weighted 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 biogenic emissions from trees, plants, and crops. 

WSU processed biogenic emissions using 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 BEIS3 modeling system uses the Biogenic Emissions 

Landcover Database, version 3, which consists of 1 km gridded data 230 land-use types21. BELD3 

is a hybrid data set based on the USGS North American Land Characteristics Data (1km, 21 

classes). It incorporates crop species distribution and crop area from 1992 US Agricultural Census, 

forest area from US forest density data, and tree species distribution 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 factors, and outputs gridded, normalized emissions at 

30°C and 1000 umol m-2·s-1 Photosynthetically Active Radiation (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 follow 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 Implementation of BEIS3 within the SMOKE Modeling 

Framework, In Proceedings of the 11thInternational 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/. 22Two different future year RVP scenarios were created for on-road and non-road mobile sources. The area and point 

source categories are not as affected 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 constant from 1999 values for 2015 (projection factor of 1). 

Stationary Area Sources 

As with point sources, the EI technical group 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 population forecasts and in some cases economic 

forecasts. 25 Growth factors that directly 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 pollutants. Stationary area source projection 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 Environmental Quality’s previous ozone modeling 

work were used for Oregon counties. These projection 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 western 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 counties. 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 Columbia for 2015. Instead, projection factors were developed 

using the ratio of 2015 emissions to 1998 emissions for Whatcom County, Washington. Ratios 

were developed for each pollutant and source category. This means that the projected emission 

rates better represent future technology 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 affect this inventory for a summertime ozone event. 25Office 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 activity projections were taken from projections 

published by the airports.

27, 28 Military aircraft was kept constant at 1999 emission levels. No local 

military bases are slated for closure. 

Marine

The Agency went through a careful process to estimate growth factors for the marine sector. While 

TEUs (twenty-foot-equivalents, a unit of measure for cargo) are predicted to dramatically increase 

in the Northwest, vessel calls (the number of trips) 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 the lower increase in vessel calls. This factor 

was also used for marine sources in British Columbia, and is based on the following projections: 

• Port of Seattle: 2.21 TEU growth factor from and 1.23 vessel call growth factor from 1999 

to 2015.29 

• Port of Tacoma: 4.8 TEU growth factor from 1999 to 2015.30

• Washington Public Ports Association cites Puget 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 includes 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 particulate 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, MOBILE 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 23rd, 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. Terminal Velocity: As China races ahead with port expansions, Vancouver’s 

plans for Deltaport run aground. 

33 California Air Resources Board. Diesel Particulate Matter 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 emissions for Whatcom County, Washington. Ratios 

were developed for each pollutant and source category. This means that the projected emission 

rates better represent future technology 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 Sound modeling report based on the 1996 episode. 

Emissions Summaries 

Tables 3 and 4 show emission summaries for precursors NOX and VOC for the Puget Sound area 

(Snohomish, King, Pierce, and Kitsap counties) for both base and future year scenarios. Emissions 

have been spatially and temporally allocated, and are 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 emissions 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 agreement 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”scenario’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, NOX 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 reduction in on-road mobile. The values for both 2015 

“high RVP” and “low RVP” scenarios are identical, as RVP does not affect NOX 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 King, 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 categories in the ONROAD model, summed together. 
73

Table 4. Puget Sound NOX 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”, biogenic emissions counted as NOX. 

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 different sources and methodologies (1999 NEI and Oregon DEQ modeling 

exercise) may have introduced some geographic biases into the inventory. These biases are 

expected to be small, and are explored in the following sections. 

Future projections are, by nature, uncertain. Changes in local economies, fuel policy, and other 

local factors could influence estimated growth factors positively or negatively. 
74

Comparisons between Seattle and 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 three counties around each metropolitan 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 case, as seen in Table 5. The exception was area 

sources, which appeared to be low for the Seattle area, 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 Emissions35

Source Category VOC NOX

Area 0.6* 1.3 

Non-road 2.1 3.1 

On-road 3.1 2.7 

Total (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 examined area source categories with the largest 

discrepancies between Portland and Seattle emissions 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 emissions 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 include Kitsap in addition to the other three Seattle 

area counties. Kitsap County has very little business and industry. The local estimates are based on 

local surveys and activity level information. 

Table 6 shows that there are some discrepancies 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). Nonetheless, the total 1999 NEI 

emissions and local Seattle estimates are close, with 60,627 and 59,127 tons/year, respectively. 

Changing the emissions inventory to reflect local Seattle emissions estimates would not 

significantly change the VOC ratio for Seattle to Portland. The Portland 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 Multnomah, Clackamas, and Washington counties. 
75

Table 6: Comparison of Portland, Seattle 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/NOX Ratios 

In addition to comparing the emissions estimates between geographic areas, the EI technical group 

compared the VOC/NOX 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 categories, the ratios were relatively close for source 

categories. Table 11 shows the VOC/NOX ratios for the Puget Sound Area for the event (King, 

Snohomish, Pierce, and Kitsap counties). 

Table 7. VOC/NOX 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 County 
76

Table 8: VOC/NOX 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 1.7 
77 

Appendix B 

Methodology for Local Non-road Mobile Source Emission 

Substitutions 

Marine and aircraft source categories 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 spent in an area along with emission factors to 

estimate emissions.36 The Agency gathered power use information from local ferries and cruise 

ships and used this to interpolate fuel use of different ships. Using local methods brought the 

estimate to 306 tons of VOC and 5,672 tons NOX/year. 

The NEI didn’t provide an estimate for military vessels 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 Underway Ships in Kitsap County. 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 consumption rate of a ship (gallons/hr) and the time 

 

36 The Port Authority of New York & New Jersey. The New York, Northern New Jersey, Long Island Nonattainment 

Area Commercial Marine Vessel Emissions Inventory. Prepared by Starcrest Consulting Group, LLC. April 2003. 

Emission factors used by Starcrest in this report are also used in our marine estimates. 
78

spent in an area along with emission factors to estimate emissions. The Agency gathered fuel 

information from local ferries and cruise ships and used 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 using a fuel use method. 

The 1999 NEI didn’t provide an estimate for Military 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 NOX /year for harbor vessels. Based on local 

surveys, this estimate is too low. 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 NOX. 

The 1999 NEI doesn’t provide an estimate for Military Vessels in Pierce County. The Agency 

estimated military vessel emissions using the power demand method (described in the King County 

section). 

The 1999 NEI estimates 0 tons of VOC and 0 tons NOX /year for Military Aircraft. Based on local 

knowledge, this is an underestimate. The Agency 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 NOX. 

Snohomish County

The 1999 NEI didn’t estimate harbor vessel emissions in Snohomish County. Based on local 

surveys, we know that there should be some emissions allocated. The Agency used the fuel use 

method described for Kitsap County to estimate emissions. The fuel method estimated the 

emissions of the harbor vessels to be 70 tons VOC and 1,289 tons NOX per year. 

The 1999 NEI doesn’t provide an estimate for Military Vessels in Snohomish County. The Agency 

estimated military vessel emissions using the power 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/headquarters_offices/aep/models/edms_model/. 
79 

Appendix C 

Puget Sound Ozone Modeling 

On-Road Mobile Source Emissions Inventory and Emissions Inventory 

Data Pre-Processing 

Emissions Inventory Documentation 

Prepared by 

Washington State Department of Ecology Air Quality Program 

contact: Sally Otterson 
80 

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 SO2 Emission Factors in grams per mile................................. 89

Table 11: Example PM10, PM2.5 and NH3 Emission Factors in grams per mile...................................... 89
81

1 Project Description 

Ozone modeling was conducted focusing on the central Puget Sound area. The modeling 

was initiated by the Puget Sound Clean Air Agency (PSCAA) to help in control strategy 

planning. Ozone modeling had recently been completed in support of the PortlandVancouver ozone maintenance plan update. In order 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 information for British Columbia provided by the 

Greater Vancouver Regional District and Environment Canada. The episode modeled 

was a high ozone event which occurred during July 22-28, 1998. The projection year 

chosen was 2015. The modeling domain encompassed 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 voluntary low RVP control 

To complete the work, PSCAA contracted with Washington State University (WSU) to 

conduct the modeling, and entered into an informal agreement with Ecology to provide 

technical assistance. One of Ecology's primary 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 described more fully in the remainder of this 

document. 

2 Onroad Mobile Source Inventories 

Onroad mobile source emissions are those generated by operating vehicles on public 

roadways. Emissions from fuel combustion, evaporation, and brake and tire wear were 

estimated. Emissions were calculated for each county for each of three vehicle classes: 

gasoline, light duty diesel and heavy duty diesel. The three vehicles classes were based 

on the availability of hydrocarbon chemical speciation profiles. Emissions of TOG, 

NOx, SO2, CO, PM10, PM2.5 and NH3 were estimated. 

2.1 Activity Level 

The activity measurement for onroad mobile sources is the number of miles driven. 

Units are typically given in average daily vehicle miles traveled (ADVMT). ADVMT is 

normally estimated from traffic counts collected over a sampling area, or through use of 

travel demand models, which simulate vehicle travel patterns based 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, Kitsap, Pierce and Snohomish Counties. The 
82

Southwest Regional Transportation Council (RTC) provided VMT for Clark County. 

The Washington State Department of Transportation (WSDOT) provided Highway 

Performance Monitoring System (HPMS) VMT for all other Washington State counties.i, 

ii The Oregon Department of Environmental Quality (ODEQ) provided 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 Counties 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 counties outside of Clark, 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 13,316,517 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

3
83

ADVMT estimated through travel demand models 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 incorporate the vehicle Inspection 

and Maintenance (I/M) programs which are operated in areas of Clark, King, Pierce, and 

Snohomish Counties in Washington, and in area of Clackamas, Multnomah, and 

Washington Counties in Oregon. The fraction of total ADVMT within the I/M area of 

each county was determined using link-level 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 temperatures ranging from 50 to 100 degrees 

Fahrenheit. The emission rates by temperature could then be matched to hourly, gridded 

temperatures that were made available through meteorological modeling for the episode. 

This produces a more accurate inventory throughout the region, since temperature is a 

sensitive input in MOBILE6.2. Currently this method discards diurnal hydrocarbon 

emissions (evaporative emissions from heating and cooling throughout the day). This 

results in a small underestimation of the total hydrocarbon emissions. 

ODEQ calculated emission rates for the July 1998 episode (and 2015 projection) for 

counties in Oregon in support of the Portland-Vancouver ozone modeling work. ODEQ 

provided the data to Ecology for use in the Puget Sound ozone modeling project. PM10

was included, but not PM2.5. PM2.5 was calculated from the PM10 by multiplying by the 

ratio of PM2.5 to PM10 estimated for Clark County, WA. 

The remainder of the section describes the parameters used to model emissions in 

Washington State counties. The parameters described are: evaluation month, registration 

distribution, humidity, inspection and maintenance (I/M) program, 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 factors 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 older fleet than the national average. To model the effect 

of the older fleet, local vehicle information was used to calculate the vehicle age 

registration distribution. The distributions are age curves for each major vehicle type 

representing the fraction of vehicles that area 1, 2, 3, ... 25 years old. Vehicle licensing 

records from the Washington State Department 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, alternate sources of information were obtained. 

Transit bus data came from Federal Transit Administration Annual Report data.vii School 

bus data came from national default distributions 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 

vehicles except buses 1998 1999 DOL 

2015 2004 DOL 

transit buses 1998 1999 FTA 

2015 2003 FTA 

school buses 1998 EPA default EPA 

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 formula 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 assigned 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 Maintenance (I/M) programs are operated in areas of Clark, King, 

Pierce, and Snohomish Counties. Input parameters have been tracked to allow modeling 

of the program.xi-1 1 1

xv

MOBILE6.2 allows specification of up to seven 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, Snohomish Counties), the program started in a smaller geographic 

area in 1983 and expanded to the current area 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 mode 
87

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 psi 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 Association entered into a voluntary 

agreement with the Puget Sound Clean Air Agency 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 counties 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 Western States Petroleum Association (WSPA) 

for a prior ozone modeling project.xx Survey data from the Alliance of Automobile 

Manufacturers (AAM) was made available through the Environmental Protection Agency 

(EPA) for 1999.xxi The most recent survey data available 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 data collected by TRW/Northrop-Grumman 

(formerly the National Institute for Petroleum and Energy Research, NIPER).xxii, xxiii 

RVP Determinations 

Four geographic groups were defined based on RVP values described in the paragraphs 

above. RVP assignments for the 1998 base case and the 2015 projection without 

voluntary low RVP control case were identical. For the 2015 projection with voluntary 

low RVP control case, the RVP value for counties affected by the RVP agreement was 

set to the agreement level. All 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 Content (base case 1998 only)

Prior to the Tier 2 Gasoline Sulfur regulations taking effect in calendar 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-sulfur gasoline phase-in 

schedules. EPA posted county 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 western Washington counties was 318. 
89

2.2.7 Diesel Sulfur Content

The Western Regional Air Partnership contracted with Environ 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 other Oregon counties. In Washington, there were separate 

groups for Clark, King, Pierce and Snohomish Counties, and groups for the eastern 

Washington counties, San Juan/Skagit/Whatcom Counties, and all other western 

Washington Counties. Example TOG, NOx, CO and SO2 emission rates at 75 degrees 

Fahrenheit are shown for each area in Table 10. Particulate matter (PM) is made up of 

several components: lead, organic and elemental carbon, sulfate, and brake and tire wear. 

PM10, PM2.5 and NH3 showed very little variation among the areas; they are shown in 

Table 11. 

Table 10: Example TOG, NOx, CO and SO2 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 PM10, PM2.5 and NH3 Emission Factors in grams per mile 

year Exhaust Brake Wear Tire Wear 

NH3 PM10 PM2.5 PM10 PM2.5 PM10 PM2.5

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 0.002 
90

2.3 Emissions Estimates 

The emission rates in grams per mile calculated by temperature were multiplied by the 

gridded ADVMT estimates by matching to the hourly 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 emissions processor. See section 0 for more 

information. 

3 Temporal Projection Factors 

The 2015 projections were made using a combination of growth factors and specific 

inventory calculations for 2015. Details are provided below. 

3.1 Washington and Oregon 

Onroad sources and nonroad sources calculated with EPA's NONROAD model were 

specifically estimated for 2015. The onroad projection is described in section 0. PSCAA 

provided 2015 nonroad inventories for all sources included in the NONROAD model for 

Oregon and Washington. 

ODEQ's 2015 projection was used for point sources, 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 Columbia 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 source 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 for 2000 and 2015 using the NONROAD model. 

Ecology calculated growth factors for each nonroad source and pollutant as the ratio of 

2015 emissions to 2000 emissions. 

The methodology for projecting onroad mobile sources was similar to the nonroad 

methodology. Emission rates were calculated 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 calculated 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 formatted for the SMOKE emissions processor. 

Much of the required supporting information (temporal, spatial, chemical) was available 

through previous modeling projects, although some additions and updates were required. 

The formatting was accomplished using existing Practical Extraction and Report 
91

Language (Perl) scripts, with minor modifications 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 individually, and area/mobile sources are defined by 

county. The emissions are listed by source classification category (SCC) codes. 

All British Columbia emissions inventory data (except the 2015 projection factors) was 

made available in SMOKE format by Environment Canada. PSCAA provided area and 

nonroad mobile source inventories, and point source inventories for Oregon and sources 

within PSCAA's jurisdiction. Ecology provided 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/aircraft. 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. Minor edits were made to data provided by 

PSCAA, such as removing TRI facilities from the point source file, and removing all 

biogenic and fires from the area source file (SCC 27* and 28*). 

4.2 Spatial Allocation Files 

Point sources have known locations that can be accurately located in the modeling 

domain. Area and mobile sources often cannot be specifically located in the modeling 

domain; therefore, surrogates which can be accurately located are used to allocate the 

sources spatially. For example, pleasure craft cannot be individually 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 limited to) population, land 

use and land cover, mileage on roadways, railroads, and major airports. Grid allocations 

of each spatial surrogate are given as the fraction 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 projects, and thus had no existing surrogate 

assignment. These codes were assigned appropriate surrogates in consultation with 

PSCAA. 

4.3 Temporal Allocation Files 

Emissions must be allocated by hour. Initial emissions estimates 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 sources 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 appropriate profiles in consultation with 

PSCAA. 

4.4 Chemical Speciation Files 

In order to model ozone concentrations, hydrocarbon, nitrogen oxide, and sulfur oxide 

emissions must be speciated into chemical groups defined by the air quality model's 

chemical mechanism. Groups are defined based 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 assigned 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 codes were assigned appropriate 

profiles in consultation with PSCAA and WSU. 

New profiles were added for onroad mobile PM2.5 and PM10 exhaust, brake wear and tire 

wear. The exhaust profiles were the same as those used in Airpact and other PM 

modeling projects. Brake and 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 Agency. Model executable file dated September 

24, 2003. 

v

 Department of Licensing electronic data as available 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 Transportation and Traffic Safety Education, Office of 

Superintendent of Public Instruction to Michael 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 December 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 Dept. 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 Otterson, 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, Pechan 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 RVP, 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. Onroad Sources. Prepared for: Office of Air 

Quality Planning and Standards, Emission Factor and Inventory Group, U.S. Environmental 

Protection Agency, Research Triangle Park, NC 27711. Prepared by: E.H. Pechan & 

Associates, Inc., 5528-B Hempstead Way, Springfield, VA 22151. October 2002. Attachment 

A: County-Specific Fuel Parameters for 1990, 1996, and 1999 Toxic Emissions Modeling 

(Preparation for MOBILE6.2 Model Runs). Prepared for: Emission Factor and Inventory 

Group (D205-01), Emissions, Monitoring and Analysis Division, U.S. Environmental 

Protection Agency, Research Triangle Park, North Carolina 27711. Prepared by: Eastern 

Research Group, Inc., 1600 Perimeter Park Drive, 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. Onroad 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.