Summary Technical Report in Support of 

the PM2.5 Clean Air Performance 

Commitment (CAPC) Process for 

Tacoma Washington 

October 14, 2010 

Prepared and Reviewed by: 

Gina Bonifacino, Robert Elleman, Robert Kotchenruther, and Krishna Viswanathan; EPA Region 10 

Kathy Himes and Erik Saganic; Puget Sound Clean Air Agency 

Clint Bowman, Sarah Clouse, Ranil Dhammapala, Jeff Johnston, Sally Otterson, 

Nancy Pritchett and Doug Schneider; Washington Department of Ecology 

Brian Lamb and Joe Vaughan; Washington State University 
Table of Contents: 

1. INTRODUCTION..............................................................................................................................................3

2. CURRENT UNDERSTANDING OF PM2.5 EXCEEDANCES IN TACOMA, WASHINGTON ...............4

2.1. MONITORING RECORD IN TACOMA .............................................................................................................4

2.2. SPATIAL DISTRIBUTIONS OF PM2.5 IN THE NONATTAINMENT AREA ............................................................6

2.3. METEOROLOGICAL CONDITIONS RELATED TO PM2.5 EXCEEDANCES ..........................................................9

2.4. SOURCE APPORTIONMENT.........................................................................................................................11

2.4.1. Overall Contributions..........................................................................................................................11

2.4.2. Seasonal Contributions........................................................................................................................12

2.4.3. Contributions on Days When the Measured Total Mass Concentration Was Elevated.......................12

2.4.4. Conclusions..........................................................................................................................................13

2.5. LEVOGLUCOSAN MONITORING RESULTS...................................................................................................14

2.6. EMISSIONS DATA AND ANALYSIS..............................................................................................................15

3. AVAILABLE ATTAINMENT DEMONSTRATION MODELS ................................................................20

3.1. LINEAR ROLLBACK MODELING.................................................................................................................21

3.1.1. Model Description ...............................................................................................................................21

3.1.2. Pros and Cons of Using Linear Rollback for Attainment Demonstration in Tacoma..........................22

3.1.3. Steps Required to Make an Attainment Demonstration with Linear Rollback.....................................23

3.2. 3-DIMENSIONAL GRID-BASED PHOTOCHEMICAL MODELING....................................................................24

3.2.1. Model Description ...............................................................................................................................24

3.2.2. Current Modeling Capability...............................................................................................................24

3.2.3. Current Model Performance................................................................................................................26

3.2.3.1. EPA Region 10 Model Performance Analysis ........................................................................................... 26

3.2.3.2. WSU and Ecology Model Performance Analysis ...................................................................................... 27

3.2.3.3. Model Performance Results and Discussion .............................................................................................. 32

3.2.4. Options for Improving Model Performanc ..........................................................................................32

3.2.5. Pros and Cons of Using Photochemical Models for Attainment Demonstration in Tacoma...............33

3.2.6. Steps to Make an Attainment Demonstration with a Photochemical Modeling System.......................33

A. APPENDICES ..................................................................................................................................................34

A.1. BOXPLOTS AND MAPPING OF MOBILE MONITORING DATA FROM TACOMA..............................................34

A.2. MODEL PERFORMANCE STATISTICS USED.................................................................................................40

A.3. TABULATED MODEL PERFORMANCE STATISTICS ......................................................................................40

A.4. TABLE OF ACRONYMS AND ABBREVIATIONS ............................................................................................44

 

2
1. Introduction 

On April 20, 2010, U.S. Environmental Protection Agency (EPA) Region 10, EPA’s Office of Air Quality Planning 

and Standards, the Washington Department of Ecology (Ecology), the Puget Sound Clean Air Agency (PSCAA) and 

the Puyallup Tribe signed a Memorandum of Understanding (MOU) to begin work on a Clean Air Performance 

Commitment (CAPC) for the Tacoma PM2.5 nonattainment area. Tacoma was designated as a nonattainment area 

for the 24-hour PM2.5 standard on October 8, 2009.1

 The boundaries of the nonattainment area are shown in Figure 

1.1. 

 Figure 1.1. The City of Tacoma, PM2.5 nonattainment area boundaries, and surrounding areas. 

A CAPC is an innovative approach to achieving air quality goals in an area exceeding or nearly exceeding the 

National Ambient Air Quality Standards (NAAQS). It is intended, ultimately, as an alternative to the traditional SIP 

process. EPA has demonstration projects underway in two areas; Tacoma, WA and Omaha, NE/Council Bluffs, IA. 

The goals of a CAPC are: 

 to achieve same or greater environmental improvements more quickly than the traditional process, with minimal 

process, 

 to create a more flexible/adaptable process for implementing controls, while incorporating measures to ensure 

accountability, and 

 to provide early and meaningful opportunities for the public to engage in the process. 

A CAPC focuses on attaining the NAAQS efficiently and effectively by implementing targeted local control 

measures that complement national measures, establishing agreed-upon technical or accountability metrics before 

the process begins so that all parties are in agreement on the end goal, and early in the process beginning a 

community stakeholder process that results in meaningful public involvement and input regarding the State 

Implementation Plan (SIP) control measures (e.g. how to reduce emissions from wood burning appliances). 

The legal framework for the CAPC is based on EPA’s Clean Data Policy. EPA’s Clean Data Policy allows EPA to 

suspend certain SIP elements if the violating monitor achieves the standard before the attainment plan is due.2

 In 

 

1 http://www.epa.gov/pmdesignations/2006standards/documents/2009-10-08/FR-11-13-2009.pdf 2

 See 40 CFR 51.1004(c) 

3
this case, since the attainment SIP is due by December 14, 2012, monitoring data from the years 2009, 2010 and 

2011 must be “clean” or indicate attainment with the standard.3

 Ultimately, absent clean data, the State will ne

submit a SIP that contains all the elements necessary for EPA approval by December 14, 2012. These elements, in 

basic terms, include: a comprehensive emissions inventory for the nonattainment area, permanent and enforceable 

control measures that effectively return the area to attainment status before the attainment date deadline (December 

14, 2015), a demonstration through air quality modeling that shows that the selected controls will return the area to 

attainment by the attainment date, a “budget” for mobile source contributions to emissions, contingency measures to 

be implemented in the event that the area exceeds the standard. Given the 98th percentile value for PM2.5 in 2009 

(47.6 μg/m3

), it is unlikely that the Tacoma area will achieve “clean data” in this timeframe. Therefore, this CAPC 

must develop/deliver outputs that can be converted to SIP elements if needed. 

ed to 

The CAPC MOU included a schedule for specific deliverables/outputs: 

March 2010 MOU signed by the Parties 

December 31, 2010 Complete and reach consensus on technical evaluation 

January 2011 Begin comprehensive stakeholder process assuming adequate funding is identified 

Summer/Spring 2011 Conclude comprehensive stakeholder process (led by PSCAA) 

Winter 2011 Performance Agreement finalized, signed; begin implementation, including check-ins 

at interim milestones and supplementation of efforts with backup measures as necessary. 

This report is being prepared as part of the second item on the timeline; Complete and reach consensus on technical 

evaluation. More specifically it will be used by the MOU signatory organizations: 

 to help make decisions about the type of modeling to use for stakeholder process and possibly the Tacoma SIP, 

 to determine what additional technical information is needed on sources of emissions and the level of control 

needed for those sources to bring the Tacoma area into attainment with the PM2.5 NAAQS (i.e. determine where 

there are gaps in our current knowledge on sources and controls needed to bring the area into attainment) and, 

 to bring together a summary of all of the technical information available about sources contributing to 

nonattainment and how controls might affect those source contributions in the Tacoma nonattainment area. 

This report is organized into two major sections. Section 2 summarizes our current understanding of PM2.5

exceedances in Tacoma by reviewing the monitoring record, meteorological data, emissions inventories, and other 

related analyses of PM2.5 that have been conducted to date. Section 3 describes the available models that could be 

used to make an attainment demonstration in Tacoma and attempts to summarize the pros and cons of each 

approach. The intended audience for this report are the federal, state, and local officials charged with developing 

strategies to bring Tacoma back into attainment of the PM2.5 NAAQS and may also be used as a technical reference 

during stakeholder discussions. 

2. Current Understanding of PM2.5 Exceedances in Tacoma, Washington 

2.1. Monitoring Record in Tacoma 

As of 1 July 2010, the following monitors have been operating at the Tacoma L Street site for at least three years. 

Table 2.1. Ambient PM2.5 and meteorological monitors operating at the Tacoma L Street site for the last 3 years or 

longer. 

Parameter Sampling frequency Sampling started 

PM2.5 FRM 1-in-3 (daily from 7 Jan 2010) 3 October 1999 

PM2.5 Neph Continuous 23 February 2001 

PM2.5 TEOM* Continuous 2 November 2000 

Meteorological Continuous 10 November 2001 

PM2.5 speciation 1-in-6 11 January 2006 

* Replaced with a FEM TEOM on 12 January 2010. 

 

3

 Clean data for PM2.5 means that the 24-hour 98th percentile 3 year average is less than or equal to 35 ug/m3. 

4
The current NAAQS for PM2.5 are 35 μg/m3

 for the 24-hr standard and 15 μg/m3 for the annual standard. The 2007-

2009 24-hr PM2.5 design value4

, as measured by the Tacoma L St Federal Reference Monitor (FRM), is 46 μg/m3

and the annual design value is 9.8 μg/m3

. The Tacoma area is in violation of the 24-hr PM2.5 standard but is in 

attainment of the annual standard. Figure 2.1 shows how design values have changed over time since 2002 at the 

Tacoma L Street site. Inspection of Figure 2.1 for trends suggests that the 24-hr design value may be increasing 

with time while the annual design value suggests marginal declines over the same period. Analysis of the FRM data 

showed 16 monitored exceedances of the 24-hr PM2.5 standard during the 2007 – 2009 period and all occurred 

during the winter months of November – February. 

The PM2.5 Chemical Speciation Network (CSN, formally known as the Speciation Trends Network, STN) is part of 

a national network that analyzes 24-hour integrated, filter-based PM2.5 samples for several metals, nitrate (NO3

-

), 

sulfate (SO4

2-), elemental carbon (EC) and organic carbon (OC). The CSN’s main objectives are to evaluate the 

chemical makeup of PM2.5, understand the sources contributing to PM2.5 at the site using source apportionment, 

evaluate health risks and assess exposures. The FRM monitor runs at a sampling frequency of 1-in-3 days, however 

the separate but co-located CSN monitor only samples at a frequency of 1-in-6 days. Of the 16 FRM monitored 

exceedances in 2007–2009, only 6 also had chemically speciated measurements. Analysis of those 6 co-located 

chemically speciated measurements show that on average the chemical composition of PM2.5 on exceedance days is 

61% organic compounds, 9% elemental carbon, 6% nitrate, and 4% sulfate, with the remaining mass being 

ammonium associate with nitrate and sulfate, soil components, and other trace elements. A source apportionment 

study has been conducted using all the CSN data from Tacoma collected between January 2006 and May 2009 

(Ogulei, 2010)5

. Results from this source apportionment study are summarized in Section 2.5 of this report. 

Figure 2.1. PM2.5 design value trends at the Tacoma L Street site. 

 

4

 A design value is the monitored concentration that is compared to the regulatory standard. To attain the 24-hour 

PM2.5 standard, the 3-year average of the 98th percentile of 24-hour concentrations must not exceed 35 μg/m3

. 5

 Ogulei, D., WA Dept of Ecology, 2010, Sources of Fine Particles in the Wapato Hills-Puyallup River Valley PM2.5

Nonattainment Area. Available at http://www.ecy.wa.gov/pubs/1002009.pdf. 

5
2.2. Spatial Distributions of PM2.5 in the Nonattainment Area 

Mobile Monitoring 

Mobile monitoring was conducted in Tacoma for particles measured by light scattering, light absorption coefficient, 

particle-bound polyaromatic hydrocarbons (PAH), as well as other trace particle-bound chemical constituents. 

Monitoring was conducted during selected afternoon and evening periods from December 2008 to February 2009 

and again from July to August 2009. Data was collected over 10 afternoons and 16 evenings in the summer, and 10 

afternoons and 10 evenings in the winter. The time periods for afternoon and evening sampling were 4 PM – 7 PM 

and 9 PM – 1 AM, respectively. The morning commute hours were not sampled because particle concentrations 

during this period are affected by the rapidly increasing morning mixing height, in addition to the spatially varying 

emission densities. Thirty-second averages of particle light scattering coefficient (bsp, a surrogate for PM2.5), 

particle light absorption coefficient (bap, a surrogate for the elemental carbon portion of PM2.5), and particle-bound 

polycyclic aromatic hydrocarbons (PAH) were collected along with relevant location information by GPS. Details 

of the deployment of this mobile platform are described in a report to be published by PSCAA in 20106

. 

Table 2.2 shows the means of the observed values of bsp, bap, and PAH. These data were merged with relevant, 

contemporaneous fixed site data collected during this study to adjust for short-term temporal variability within each 

sample period. In addition to an analysis of the means, analysis of the distribution of the observations (see boxplots 

and mapping of the data in the appendix) indicates that higher bsp values occur in residential areas of southern 

Tacoma in the winter during evening hours. In contrast, higher bap values were observed near the major roads 

during both afternoon and evening periods in both summer and winter. 

Table 2.2. Summary of mobile measurements of particles on selected days between 12/08 and 2/09 (winter) and 

between 7/09 and 8/09 (summer). Afternoon and evening sampling occurred between 4 PM – 7 PM and 9 PM – 1 

AM, respectively. 

Measurement Winter Mean Summer Mean 

b Afternoon 78.9 21.9 sp (10-6m-1) 

Evening 83.2 20.8 

b Afternoon 21.8 9.4 ap (10-6m-1) 

Evening 24.7 10.4 

PAH (ng/m Afternoon 18.4 4.9 3) 

Evening 20.7 4.5 

A principal component analysis (PCA) was conducted on the aerosol dataset. PCA is a form of factor analysis and 

involves a mathematical procedure that transforms a number of possibly correlated variables into a smaller number 

of uncorrelated variables called principal components. The first principal component (or factor) accounts for as 

much of the variability in the data as possible, and each succeeding component accounts for as much of the 

remaining variability as possible. The PCA for the Tacoma dataset combined the relevant aerosol data and 

identified three factors contributing to the observed spatial variability. Factor 1 was high in bsp and moderately high 

in bap and is believed to be more associated with wood smoke. Factors 2, high in PAH, and Factor 3, high in bap and 

moderately high in bsp, are both believed to be more associated with mobile sources. The factor loadings are shown 

in Table 2.3, however, this analysis and its interpretation are still ongoing and should not be taken as final. 

Table 2.3. Principal component factor loadings 

Factor 1 Factor 2 Factor 3 

bsp 0.92 0.18 0.36 

PAH 0.17 0.96 0.23 

bap 0.42 0.29 0.86 

This PCA helps to understand the underlying sources of spatial variability and to clarify the location of the highly 

impacted wood smoke areas, characterized by relatively high levels of bsp, moderate levels of bap, and low levels of 

 

6

 “Seasonal and Spatial Characterization of Tacoma and Seattle Area Air Toxics”, PSCAA, 2010 in progress. 

6
PAHs. Figure 2.2 compares the relative contribution of this temporally adjusted wood smoke feature with the 

results of woodstove use from Census survey data in 2000. The lack of any apparent correlation between the left 

and right images of Figure 2.2 indicates that the 2000 census data are limited in the information they provide. 

Figure 2.2. The left-hand plot shows the spatial distribution of woodstove survey use data from the 2000 Census. 

The right-hand plot shows the spatial distribution of a wood smoke related factor (Factor 1) derived from a principal 

component analysis. 

Factor 1 Score

Temporary PM2.5 Fixed Site Monitoring in Tacoma 

A network of Radiance Research nephelometers was distributed at 4 locations around the nonattainment monitor at 

Tacoma South L Street. The temporary monitoring sites were Stewart Heights Park, Lincoln High School, Edison 

Elementary, and the USGS Field office. These four temporary monitors were sited in primarily residential 

neighborhoods. 

The PM2.5 values were estimated from a factor derived from light scattering to Federal Reference Method PM2.5

regressions at the Tacoma South L Street site. Monitoring occurred during the 2006-2007 winter (from September 

2006 through February 2007). Figure 2.3 shows a map of the 4 temporary fixed-site monitors as well the Tacoma 

South L Street, Tacoma Tideflats, and Puyallup South Hill sites that were monitoring concurrently. 

7
Figure 2.3. Map depicting the locations of the 4 temporary fixed-site monitors active from September 2006 through 

February 2007 and the Tacoma South L Street site. 

Figure 2.4 shows a stagnation event during the sampling period and the respective PM2.5 levels for each site. 

Notably, the Tacoma South L Street monitor was the highest of the group, and most other monitors were relatively 

close. These results demonstrate that maximum concentrations are occurring where the permanent fixed site is 

located. 

Figure 2.4. PM2.5 concentrations during a winter stagnation in January 2007 for the temporary sites in Tacoma as 

well as Tacoma South L Street.

Figure 2.5 shows the 24-hour averaged 98th percentile results from the temporary study. The data is not a calendar 

year and only includes winter season data. From the data, the Tacoma South L Street fixed site is the highest 

followed by the Stewart Heights site on E 56th Street. 

8
PM 2.5

(Proximity to Exceeding the 24 Hour Standard)

Sept 2006 - Feb 2007

40.5 ug/m3 39.8 ug/m3 43.7 ug/m3

50.7 ug/m3

57.1 ug/m3

38.0 ug/m3

45.7 ug/m3

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

1

Concentration

Micrograms per cubic meter

Federal Standard (35 ug/m3)

Lincoln HS USGS Edison Elem Stewart South L St Puyallup

Heights

Tacoma Port

Figure 2.5. Nephelometer PM2.5 98th percentile values using only the 24-hour results for the sampling period 

(September through February). 

Mobile and Temporary Fixed Site Monitoring Conclusions 

The results indicate that the Tacoma South L Street site is a hotspot for PM2.5 concentrations in the winter heating 

months. Although the Tacoma South L Street site is the highest, there is also a relatively even distribution of PM2.5

over the Eastside, South End, and South Tacoma neighborhoods in the winter months. Additionally, PM2.5 levels 

are elevated as far north as the Tacoma Tideflats site. 

The wood smoke factor found in the principle component analysis has a similar distribution as that found in the 

temporary fixed site monitoring and demonstrates higher levels of PM2.5 over the residential neighborhoods. 

Additionally, the mobile monitoring showed light scattering data is highest on winter nights over the residential 

neighborhoods.

2.3. Meteorological Conditions Related to PM2.5 Exceedances 

Figure 2.6 shows the distribution of one-hour PM2.5 concentrations as functions of the wind direction, wind speed, 

temperature, and time of day for the heating season October 2007 through March 2008. Figures for a longer period 

show a similar pattern. Wind speed and direction measurements at the Tacoma South L Street site were taken with a 

sonic anemometer, which has good resolution even at low wind speeds. The reader should note that when wind 

speeds drop below about 0.5 mph, conditions at the monitor are representing nearby neighborhood conditions and 

wind direction is not indicative of transport from areas further away in the nonattainment area. It is readily apparent 

from Figure 2.6 that high hourly PM2.5 concentrations are associated with wind directions from the northeast through 

southeast, wind speeds less than two miles per hour, cool to cold temperatures, and have a strong time of day 

dependence. Hourly concentrations rarely exceed 35 μg/m3

 when the wind speed is greater than two miles per hour 

or during the middle of the day. Hourly concentrations exceed 35 μg/m3

 almost 25 percent of the time when the 

wind direction is from between 80 and 130 degrees. And, although hourly concentrations can exceed 35 μg/m3

when temperatures are above 30° F, it is almost a certainty that concentrations exceed 35 μg/m3

 when the 

temperature is less than 25° F. One-hour averages have been used to provide an accurate description of the 

meteorological conditions and time of day dependence of PM2.5 concentrations. In each panel the 24-hour PM2.5

National Ambient Air Quality Standard (NAAQS) is shown by the horizontal dotted red line at 35 μg/m3

. 

9
Figure 2.6. The distribution of one-hour PM2.5 concentrations as functions of the wind direction, wind speed, 

temperature, and time of day for the heating season October 2007 through March 2008 at the Tacoma L St. monitor. 

Standard boxplots are shown with the lower and upper bounds of the box being the 25th and 75th percentiles (the 

first and third quartiles), the bold bar across the box marking the median (50th percentile), and the whiskers marking 

the robust range (usually taken as 1.5 times the inter-quartile range away from the median). Although the concept of 

discarding outliers based just on value has fallen out of favor, data beyond the whiskers (shown by circles in these 

figures) are often called outliers and represent the extremes of the distribution. The width of boxes is proportional to 

the square root of number of observations in the bin. 

10
2.4. Source Apportionment 

Ecology identified the main sources of PM2.5 in the Wapato Hills-Puyallup River Valley area by using receptor 

modeling techniques to analyze existing PM2.5 speciation data (Ogulei, 2010)7

. The data were collected from 

January, 2006 to May, 2009 using the CSN sampling protocols in South Tacoma, Washington. The source 

apportionment analysis was conducted using Positive Matrix Factorization (PMF; Paatero and Tapper, 19948

; 

Paatero, 19979

) and the Conditional Probability Function (CPF; Ashbaugh et al., 198510). 

2.4.1. Overall Contributions 

The results of this analysis indicate that the sources of PM2.5 in the nonattainment area, in order of their overall 

average contributions, are: 

 wood smoke (45%); 

 secondary particles (25%); 

 motor vehicles (13%); 

 industrial emissions (6%); 

 fugitive dust (4%); 

 fresh and processed sea salt (4%); 

 fireworks (2%); and 

 oil combustion and ships (1%). 

Table 2.4 shows the average contributions in micrograms per cubic meter (μg/m3

) resolved in this analysis. The 

contributions have been averaged over the entire sampling period. Overall, wood smoke was the dominant source 

during the study period with an average contribution of 4.0 μg/m3

, or about 45% of the total apportioned PM2.5

mass. Additionally, PMF resolved contributions from secondary aerosol (25%) and motor vehicles (13%). The 

analysis using the PMF model was unable to attribute a large fraction of the aerosol, that from secondary aerosol 

formation of sulfate and nitrate, to a specific source. However, it is likely that a major portion of this secondary 

aerosol can be attributed to mobile and industrial sources, since emissions inventories indicate that these sources are 

large emitters of the aerosol precursor gases. Hence, the percent attributions listed here for motor vehicles and 

industrial emissions are likely low estimates. 

Table 2.4. Average contributions resolved by the PMF model. 

Resolved Source Average Contribution 

(μg/m3

)

Standard Deviation 

(μg/m3)

Wood Smoke 4.0 4.9 

Secondary Nitrate 1.2 1.4 

Secondary Sulfate 1.0 0.9 

Gasoline Vehicles 0.9 1.3 

Arsenic-rich Industrial Emissions 0.5 0.6 

Fugitive Dust 0.4 0.5 

Fresh Sea Salt 0.3 0.6 

Diesel Vehicles 0.3 0.3 

Fireworks 0.2 0.7 

Oil Combustion and Ships 0.1 0.2 

 

7

 Ogulei, D., WA Dept of Ecology, 2010, Sources of Fine Particles in the Wapato Hills-Puyallup River Valley PM2.5

Nonattainment Area. Available at http://www.ecy.wa.gov/pubs/1002009.pdf. 8

 Paatero, P., and Tapper, U. (1994). Positive Matrix Factorization: A Non-Negative Factor Model with Optimal 

Utilization of Error Estimates of Data Values. Environmetrics 5, 111-126. 9

 Paatero, P. (1997). Least Squares Formulation of Robust Non-Negative Factor Analysis. Chemometrics and 

Intelligent Laboratory Systems 37, 23-35. 

10 Ashbaugh, L.L., Malm, W.C., and Sadeh, W.Z. (1985). A Residence Time Probability Analysis of Sulfur 

Concentrations at Grand Canyon National Park. Atmospheric Environment 19, 1263–1270. 

11
Aged Sea Salt 0.03 0.02 

2.4.2. Seasonal Contributions 

To better understand the contributions of individual sources, the sampling period was divided into four seasons as 

follows: “Winter”, which was used to represent the calendar months of December, January and February; “Spring”, 

which represented March, April and May; “Summer” for June, July and August; and “Fall” for September, October 

and November. 

Figure 2.7 shows the resolved average contributions by season. Strong seasonality was observed in some, but not 

all, of the resolved sources. Specifically, wood smoke, fireworks, secondary nitrate, motor vehicles, industrial 

emissions, and fresh sea salt exhibited pronounced fluctuations in average contributions between two or more 

seasons. Most of the fugitive dust and fireworks contributions occurred in the summer, while most of the wood 

smoke contributions occurred in the winter. 

Average contributions from motor vehicles and industry were highest in the winter probably due to the low 

atmospheric mixing height in the winter. Also, for motor vehicles, the increased number of cold-starts in the winter 

may have contributed to increased motor vehicle emissions in the winter. 

Average Concentration (g/m3)

01234567

Aged Sea Salt

Fireworks

Oil Combustion/Ships

Fugitive Dust

Diesel Vehicles

Fresh Sea Salt

Arsenic‐rich Industrial

Sec. Sulfate

Gasoline Vehicles

Sec. Nitrate

Wood Smoke

Summer

Spring

Fall

Winter

Figure 2.7. Average PM2.5 contributions by season. The majority of wood smoke contributions originated in the 

winter and fall, while most of the fireworks contributions were observed in the summer months. 

2.4.3. Contributions on Days When the Measured Total Mass Concentration Was Elevated 

Figure 2.8 shows the range of percentage contributions for four different ambient PM2.5 mass concentrations. As 

indicated by Figure 2.8, wood smoke was responsible for at least one-half of the observed total mass on most days 

when the measured total PM2.5 mass concentration exceeded 30 μg/m3

. Wood smoke contributions also averaged 

about 52% when the total PM2.5 mass concentration exceeded 20 μg/m3

, which is a healthy air goal established by 

the Washington State Department of Ecology. 

12
Source

Wood Smoke

Sec. Nitrate

Gasoline Vehicles

Sec. Sulfate

Arsenic‐rich Industrial

Fresh Sea Salt

Diesel Vehicles

Fugitive Dust

Oil Combustion/Ships

Fireworks

Aged Sea Salt

Percent Contribution (%)

0

10

20

30

40

50

60

70

80

PM2.5 > or = 20g/m3

PM2.5 > or = 30g/m3

PM2.5 > or = 35g/m3

PM2.5 > or = 20 but < 35g/m3

Error Bar = Range

Figure 2.8. Percentage contributions on days when the measured total PM2.5 mass concentration was elevated. For 

completeness, four concentration ranges were evaluated: ≥ 20 μg/m3 (circular symbols); ≥ 30 μg/m3 (triangles); ≥ 35 

μg/m3

 (squares); and 20 ≤ PM2.5 <35 μg/m3 (diamonds). Error bars represent the data range. 

Generally, on elevated days (i.e., days with measured PM2.5 mass ≥ 30 μg/m3

) where the resolved wood smoke 

contribution exceeded 50%, daily-averaged winds blew from the southeast or southwest approximately 70% of the 

time. This suggests that wood smoke sources located to the southeast and southwest of the sampling site may have 

been responsible for the majority of the observed wood smoke concentrations on those days. 

Besides wood smoke, motor vehicles and secondary aerosol (secondary nitrate plus secondary sulfate) also 

contributed large amounts to the measured total mass on most days when the PM2.5 ambient concentration was or 

exceeded 30 μg/m3

. 

2.4.4. Conclusions 

By analyzing the measured elemental species concentrations in Tacoma, Ecology has determined that the largest 

source of ambient fine particles in the nonattainment area is wood smoke. Other sources found were gasoline 

vehicles, diesel vehicles, fugitive dust, fireworks, secondary nitrate, secondary sulfate, oil combustion and ships, 

arsenic-rich industrial emissions, freshly-emitted sea salt, and processed (aged) sea salt. On average, the 

combination of all these sources explained more than 90% of the measured average mass concentration. 

It is likely that the resolved wood smoke contributions represent combined contributions from wood stoves and 

other wood-fired home heating devices. Wood smoke contributions had the strongest relationship with the 

measured total PM2.5 mass which illustrates that wood smoke contributions were responsible for the majority of the 

observed trend in total PM2.5 mass concentrations. The source apportionment results showed that wood smoke 

accounts for nearly one-half of the total measured PM2.5 mass on most days. The wood smoke contributions were 

highest during the winter and on days when the measured daily PM2.5 mass concentration exceeded 20 or 30 μg/m3

. 

13
Wind direction and wind speed were found to be factors in the observed wood smoke concentrations. Accordingly, 

the largest sources of wood smoke based on hourly observations appeared to be located to the south and southeast of 

the sampling site. 

A strong presence of secondary aerosol was observed in the study area in this analysis. About 25% of the ambient 

PM2.5 mass concentrations were attributable to secondary aerosol. Further analyses identified the resolved 

secondary aerosol as locally-generated aerosol from multiple local sources of nitrogen oxides, sulfur dioxide and 

volatile organic compounds. This indicates that a complete emissions control strategy would also consider 

evaluating local sources of precursors for secondary aerosol, including mobile and non-mobile sources of nitrogen 

oxides, sulfur dioxide and volatile organic compounds. 

2.5. Levoglucosan Monitoring Results 

Levoglucosan, an anhydrous sugar derived from the pyrolysis of the major wood polymer cellulose, is one of the 

most abundant organic compounds associated with particles in wood smoke.11,12 This chemical marker is stable in 

the environment and has been used extensively to estimate wood smoke levels in ambient particulate matter (PM) 

samples.13,14

The time series plot in Figure 2.9 shows levoglucosan for three Tacoma sites and one Seattle site. These results 

come from a PSCAA study measuring air toxics in the Tacoma area. Sampling started in November 2008 and 

continued to October 2009. The study results are scheduled to be published in 2010. As a surrogate for wood 

smoke, the plot demonstrates different contribution of wood smoke to each site. 

Levoglucosan concentrations in μ g/m3 over time

0.0

0.5

1.0

1.5

2.0

2.5

11/2/0812/2/08

1/2/092/2/093/2/094/2/095/2/096/2/097/2/098/2/099/2/09

10/2/09

Date

Levoglucosan concentration

Tacoma Tideflats

Tacoma Portland Ave Res

Tacoma South L Street

Seattle Duwamish

Figure 2.9. Levoglucosan concentrations at sites in Tacoma and Seattle. 

 

11 Fine, P. M., G. R. Cass, et al. (2001). "Chemical characterization of fine particle emissions from fireplace 

combustion of woods grown in the Northeastern United States." Environ Sci Technol 35(13): 2665-2675. 

12 Fine, P. M., G. R. Cass, et al. (2002). "Chemical characterization of fine particle emissions from the fireplace 

combustion of woods grown in the Southern United States." Environ Sci Technol 36(7): 1442-51. 

13 Schauer, J. J. and G. R. Cass (2000). "Source apportionment of wintertime gas-phase and particle-phase air 

pollutants using organic compounds as tracers." Environ Sci Technol 34(9): 1821-1832. 

14 Larson, T., T. Gould, et al. (2004). "Source apportionment of indoor, outdoor, and personal PM2.5 in Seattle, 

Washington, using positive matrix factorization." J Air Waste Manag Assoc 54(9): 1175-87. 

14
The Tacoma South L Street nonattainment monitor demonstrates the highest levels of levoglucosan of the 4 sites. 

Additionally, there is a clear seasonal distribution that starts in the fall and ends in the spring. 

Figure 2.10 shows the levoglucosan results as averages for the heating (October to March) and non-heating (April to 

September) seasons. 

Figures 2.10. Box plots of levoglucosan concentrations averaged in heating and non-heating seasons. Standard 

boxplots are shown with the lower and upper bounds of the box being the 25th and 75th percentiles (the first and 

third quartiles), the bold bar across the box marking the median (50th percentile). 

The median values in all the Tacoma sites appear to be elevated compared to the Seattle Duwamish site during the 

heating season. During the non-heating season, levels are much lower, with Tacoma sites remaining higher than the 

Seattle Duwamish site. The mean, median, and 75th and 95th percentiles are all highest at the Tacoma South L 

Street monitor for heating and non-heating seasons. 

The measurement and analysis of levoglucosan confirms our conceptual understanding that, of the 4 sites monitored, 

Tacoma South L St. is the most impacted by woodsmoke. This analysis is also in rough agreement with the seasonal 

proportions of woodsmoke impacting Tacoma South L St. determined by the PMF source apportionment modeling 

discussed above. Continued measurement and analysis of levoglucosan in Tacoma could provide a direct means of 

tracking progress with woodsmoke emissions curtailment efforts. 

2.6. Emissions Data and Analysis 

PM2.5 emission sources for Pierce County are listed in Table 2.5. The table includes two different emissions 

inventory estimates; the Washington State Department of Ecology Emissions Inventory (2005)15 and the 9-Factor 

Report found in the August 18, 2008 letter from the EPA to Washington State with EPA’s proposed nonattainment 

designation for the Tacoma area16. Analysis and methodology can be found in their respective reports. 

 

15 http://www.ecy.wa.gov/programs/air/EmissionInventory/EmInvSummary_2005.xls 16 http://www.epa.gov/pmdesignations/2006standards/rec/letters/10_WA_EPAMOD.pdf 

15
Table 2.5. Pierce County Seasonal PM2.5 Emission Estimates in tons per year from the 9-Factor Report and WA 

Ecology’s 2005 Emissions Inventory. 

9-Factor Report Ecology 2005 Emissions Inventory 

Category Annual Summer Fall Winter Spring Annual Summer Fall Winter Spring 

Ecology 

notes 

Outdoor Open 

Burning 

Land clearing 

burning 941 282 282 151 226 

didn't 

inventory 

this category 

Yard waste 

burning 185 56 56 30 44 73 21 16 20 16 

Forest 

wildfires and 

managed 

burns 117 35 35 19 28 104 57 46 1 

Structural fires 11 3 3 3 3 7 2 2 2 2 

Agricultural 

waste burning 4 1 1 1 1 0 0 0 0 0 

1258 377 377 204 302 184 23 75 68 19 

Mobile 

Sources 

On-road 

gasoline* 94 25 24 22 23 94 25 24 22 23 

Preliminary 

MOVES 

results show 

this category 

could be 3-4 

times the 

Ecology 

estimates 

shown here 

Non-road 

diesel 280 78 67 62 73 308 95 74 63 75 

On-road diesel 201 52 50 48 50 200 54 50 47 50 

Non-road 

gasoline 80 22 19 18 21 85 31 19 16 19 

Ocean-going 

vessels 54 14 14 14 14 54 14 14 14 14 

Harbor vessels 35 9 9 9 9 35 9 9 9 9 

Railroad diesel 

engines 27 7 7 7 7 26 7 7 7 7 

On-road CNG 

and LPG 4 1 1 1 1 

Aircraft 

ground 

support 3 1 1 1 1 0 0 0 0 0 

Aircraft 2 1 1 1 1 

Non-road LPG 2 1 1 1 1 2 1 0 0 0 

Recreational 

boats 52 22 17 0 13 39 22 7 2 7 

834 233 211 184 214 843 257 203 179 204 

Fireplaces and 

Woodstoves 

Woodstoves 

wood burning 430 22 60 206 142 1573 97 473 629 374 

includes 

firelogs 

Fireplace 

wood burning 165 8 23 79 54 273 11 89 116 58 

includes 

firelogs 

Firelog 

burning 78 4 11 37 26 

Pellet stove 

burning 6 0 1 3 2 37 2 12 15 9 

679 34 95 325 224 1884 110 574 760 440 

Other Source 

Categories 

Industrial 

point sources 181 45 45 45 45 177 45 44 43 45 

Natural gas 

burning 67 3 9 32 22 31 1 8 14 8 

residential 

only 

16
Boiler and 

furnace 

distillate oil 

burning 11 1 2 5 4 5 0 1 2 1 

residential 

only 

Propane 

furnaces and 

boilers 2 0 0 1 1 2 0 0 1 0 

residential 

only 

261 49 56 83 72 215 46 54 60 54 

Other Ecology 

Sources 

vehicle fires 8 2 2 2 2 

residential 

trash 357 102 80 89 86 

paved road 

dust 10 3 2 2 2 

unpaved road 

dust 28 10 7 6 6 

agricultural 

harvesting 0 0 0 0 0 

agricultural 

tilling 25 0 1 1 22 

 429 117 92 101 118 

 

Total All 

Sources 3032 693 739 796 812 3,556 553 998 1,170 835 

*On-road gasoline inventory data for the 9-Factor Report were incorrect in the original Report and are corrected here. The data should be 

identical to the Ecology data because Ecology was the source. 

Fireplaces and Woodstoves 

As the table above demonstrates, the Ecology winter fireplace and woodstove PM2.5 estimate is more than two times 

the 9-Factor Report estimate. The differences originate from the different surveys used to estimate the emissions 

inventory. 

Ecology’s 2005 woodstove/fireplace inventory was based on a 2001 Washington State University survey17 of 

residential wood burning in Washington, Oregon and Idaho. They completed 754 surveys in Washington. The state 

was broken into 4 groups for the survey: incorporated areas, unincorporated Western Washington, unincorporated 

Eastern Washington – counties with predominantly range/farm lands, and unincorporated Eastern Washington – 

counties with some forested lands. The activity estimates for Pierce County were drawn from wood burning 

characteristics of the first two groups. The emission factors were taken from AP-42 and the 2002 EPA NEI 

Documentation. 

The fireplace/woodstove estimate for the 9-Factor Report was based on results of the Pacific Rim Research (PRR) 

survey in 2005. The data are representative of wood-burning behavior in the Puget Sound Clean Air Agency 4-

county area. The Agency contracted with PRR to perform a behavioral survey. The survey covered 300 households 

in each of the 4 counties and the Agency assumed the results of the survey are representative of the Puget Sound 

area. Emissions factors were taken from AP-42.18

Fireplace and woodstove source estimates could be improved with data from a recent survey wood burning emission 

survey. A full report of wood burned was analyzed in 2009 by OMNI Environmental Services, Inc. and PM2.5

estimates could be calculated from these results. The work to make these calculations involves translating the 

survey data into actual emissions estimates (i.e., examining the appropriateness of the weighting used in the original 

survey, determining how to use the survey subgroups, and choosing emission factors). Specific improvements from 

this 2009 analysis that could be incorporated into future fireplace and woodstove source estimates are improvements 

in estimating; duration of a burn session, pounds of wood burned per hour, cords of wood burned per year, number 

 

17 Tarni, J; Allen, T. Wood Burning Stove Survey for Idaho, Oregon and Washington State. Social and Economic 

Sciences Research Center. Washington State University. Prepared for Gary Reinbold, Idaho Department of 

Environmental Quality. Data report and database of survey responses. August 2001. 

18 http://www.pscleanair.org/news/library/reports/Air_Emission_Inventory_2005.pdf 

17
of logs burned per year, frequency of fireplace and woodstove use, the total number of woodstoves and fireplaces, 

and the seasonal distribution of burning. However, the 2009 study lacks information that would improve our 

understanding of the spatial distribution of fireplaces and woodstoves, both within and outside the nonattainment 

area. Without these improvements, existing methods such as using population density as a surrogate to map 

fireplaces and woodstoves emissions will need to be employed. 

Figure 2.11 maps concentrations of Pierce County households that use wood as a primary source of heat. Data were 

gathered in the 2000 U.S. census and are likely out of date. Additionally, the 2000 census provided no data on 

households that use wood as a secondary source of heat or use of fireplaces for esthetic purposes, which are also 

thought to be important sources. Analysis of the map indicates that the highest concentrations of homes using wood 

as a primary heat in 2000 source lies within the nonattainment area boundaries (Figure 1.1). Ecology and PSCAA 

have no reason to believe that this has changed at present, however, the current spatial distribution of emissions is 

uncertain. 

Outdoor Open Burning 

As noted in the 9-Factor Report, outdoor open burning is likely overestimated within the nonattainment boundary 

because the data used is countywide emissions and open burning is prohibited in urban areas and where the violating 

monitor is located. The Ecology estimate did not consider land clearing burning in their estimate. 

Mobile Sources 

MOVES (MOtor Vehicle Emissions Simulator) is EPA’s new on-road mobile model and is replacing Mobile6.2 as 

the SIP modeling standard. As such, the Ecology mobile inventory needs to be updated using MOVES. Preliminary 

modeling by Ecology with MOVES results in on-road PM2.5 emissions that are 3-4 times higher than those estimated 

with the old model. MOVES is an advancement over Mobile6.2 because is uses updated emissions rates and updated 

fleet and activity data. Some of the additional differences between MOVES and Mobile6.2 include refinements in 

temporal scale (instantaneous rates vs. trip-averaged), refinements in spatial scale (macro/meso/microscales vs. 

regional only), the basis of activity (time vs. distance), and the kinds of emissions modeled (energy consumption and 

greenhouse gases vs. CO2 and air toxics). 

18
Figure 2.11. Pierce County households that use wood as a primary source of heat (data: 2000 U.S. Census). 

Source Contributions to Total PM2.5

In the 9-Factor Report, the data show that the highest sources for PM2.5 emissions in Pierce County in the winter are 

fireplaces and woodstoves (41%). The next highest contributors in winter are mobile sources (23%). The smallest 

contributing group in winter is other source categories (10%). 

While Ecology’s inventory has different proportions for source contributions, the ranking of source contribution is 

similar. Ecology’s winter time contributions allocate woodstoves and fireplaces as the primary source for PM2.5

(65%). The next highest ranked are mobile (15%) and other sources (14%). As outdoor land clearing was not 

included, outdoor burning is only 6% of the winter time source for PM2.5. 

As listed for the 9-Factor Report in Table 2.5, summer and annual emissions largest contributors are outdoor burning 

and mobile sources. However, as described above, considering the limitations of having only countywide data, the 

nonattainment area may be more impacted by mobile sources than outdoor open burning. In the Ecology inventory, 

fireplace and woodstove emissions are the largest contributor to annual emissions. 

19
EPA Contributing Emissions Score Analysis 

EPA considered the Contributing Emissions Score (CES) for each county in their nonattainment decisions. The 

CES is an EPA methodology that identifies counties that may be contributing to violations at the monitor. While 

EPA evaluated the CES in its nonattainment boundary determination, the CES results were not relevant to the 

specifics of the Tacoma area nonattainment. Specifically, the CES must be interpreted carefully in areas with sharp 

spatial variance in emissions and topography. Additionally, the CES does not account for large diurnal variations in 

hourly particulate emissions which routinely occur during events where PM2.5 exceeds the 24-hour standard. EPA 

accounted for these limitations of the CES in their assessment of the nonattainment boundary, which remained 

exclusively within Pierce County. 

Conclusions 

The discrepancies in the emissions inventory results demonstrate the need for updated estimates based on newer 

surveys and newer emissions inventories. Additionally, the SIP process requires a comprehensive 2008 base year 

inventory. Currently, Ecology does not have a 2008 comprehensive inventory, except for point sources, non-road 

model sources, and managed silvicultural burning. Ecology is currently working on being able run MOVES for 

on-road sources. Additional work includes analyzing and utilizing the new PSCAA woodstove survey. An 

inventory projection to the attainment year will also need to be done for a SIP. 

Regardless of the choice of attainment demonstration method, Ecology and PSCAA need to develop a 

comprehensive, accurate, current inventory of actual emissions for the selected base year and refine the current 

geographic extent of the emissions from the county level to that of the specific nonattainment area. Once the base 

year inventory is completed, Ecology and PSCAA will also need to develop a future year planning inventory that 

incorporates any expected emissions changes for the area, the targeted PM2.5 control measures, and allowable 

emissions (as opposed to actual) for point sources. These will be needed regardless of the choice of attainment 

demonstration method. However, the method chosen does affect the amount of further spatial and temporal 

refinement that will be needed. A photochemical modeling inventory requires hourly, gridded emissions for specific 

meteorological conditions, which means more extensive data collection and formatting needs over that required for 

the rollback attainment demonstration method. 

In summary, considering that monitor violations in Tacoma are exclusively in the late fall through early spring 

seasons, the available seasonal emission data demonstrate that woodstoves and fireplaces have the largest impact to 

attainment status. In contrast, mobile sources are a large non-seasonal source of PM2.5 to the county. There are also 

less understood sources, such as outdoor burning and other county contributions, but have presumably less impact 

than woodstoves, fireplaces, and mobile sources. Updating the woodstove/fireplace estimates with more recent 

surveys and the mobile emission estimates using MOVES would also refine the PM2.5 estimates and give 

stakeholders a better understanding of the pollution proportions. 

3. Available Attainment Demonstration Models 

Federal regulations require that areas in nonattainment of the NAAQS must develop implementation plans 

describing how the area will come back into attainment. Attainment demonstration models project the impact of 

planned changes in emissions on air quality. Often these models are used to explore multiple emission control 

scenarios in the air quality planning process. EPA Region 10, Ecology, and PSCAA currently have access to two 

types of models capable of performing an attainment demonstration; linear rollback modeling and 3-dimensional 

grid-based photochemical modeling. These two models, either in their basic form or adapted versions, are the two 

models that have been historically used for NAAQS attainment demonstrations. The technical team that developed 

this report is unaware of any other attainment demonstration models. 

20
3.1. Linear Rollback Modeling 

3.1.1. Model Description 

Linear rollback modeling of PM2.5 concentrations is a relatively simple method to calculate future PM2.5 based on 

current observed concentrations with a linear scaling of the observed PM2.5 data based on the ratio of future 

projected emissions to current emissions. The 1973 paper by de Nevers and Morris19 still serves as an excellent 

reference to rollback modeling. The authors begin with the simple rollback equation: 

(ci-b)future = ∑[kij ej]future (3.1) 

 (ci-b)base ∑[kij ej]base 

where ci is the pollutant concentration at point I, b is the background concentration (the assumed PM2.5

concentration unrelated to sources in the nonattainment area), ej is the emission rate of category j, and kij is the 

contribution of a unit emission rate from category j to concentration at point i. 

Equation 3.1 can be used to calculate the percent emissions reductions (R) required in an attainment demonstration 

by defining the future concentration ((ci )future) to be the attainment goal (cmax). Additionally, one can adapt the 

equation to include emissions growth factors (gf) for each sub-population that emits PM2.5 to account for any 

expected increase in source activity. Modifying Equation 3.1 with these changes and solving for R yields Equation 

3.2: 

R = 100% (gf*cmax-std+b(1-gf)) (3.2) 

 gf*cmax-gf*b 

where std is the attainment concentration goal. 

If emissions growth is ignored (gf = 1), Equation 3.2 is the simple rollback model for an attainment demonstration. 

Figure 3.1 (replotted Figure 2 from de Nevers and Morris) shows the uncertainty associated with choosing various 

growth rates as a function of background levels. For example, vertical and horizontal dotted lines in Figure 3.1 

show only a difference of 5% between assuming no growth and growth of 1.2 (20%) when background levels are 

0.25 of the design value. Additionally, as a simplification, the secondary production of PM2.5 could be treated as 

part of the background. Hence, for the Tacoma area, Figure 3.1 could be used to estimate the uncertainty around 

assuming different growth projections for those primary PM2.5 emissions sectors not targeted for controls (e.g., if 

woodsmoke is targeted for controls and primary PM2.5 from mobile sources is not). To account for any non-linearity 

in secondary production of PM2.5 as a result of reducing primary emissions, emissions could be reduced beyond that 

required by Equation 3.2 to add a margin of safety. The reader should note that because rollback does not consider 

secondary aerosols in the attainment calculation, this likely makes it less accurate than the photochemical modeling 

methodology. However, while it is likely less accurate, the rollback methodology might be more conservative in 

practice because secondary PM2.5 gaseous precursor reductions would not be counted and the rollback methodology 

is often employed with additional reductions beyond those needed to reach the design value as a safety factor. 

de Nevers and Morris continue by developing three additional equations (not shown) based on Equation 3.2 which 

include the effects of release height, atmospheric dispersion and wind direction. 

 

19 de Nevers, Noel and Roger Morris, “Rollback Modeling—Basic and Modified”. Paper No. 73-139, presented at 

the Annual Meeting of the Air Pollution control Association, June 24-28, 1973, Chicago, IL. 

21
Figure 3.1. The effects of using various assumed emissions growth factors (a growth factor of 1.2 would be a 

growth of 20%) as a function of background to design value ratio (replotted Figure 2 from de Nevers and Morris). 

Rollback assumes that the sources of the contributing emissions are well defined, hence the transport of emissions 

from outside the area will increase the uncertainty of the result because the transported material will be subject to 

variability in loss due to wet or dry deposition and uncertainty in the dispersion that occurs during transport. The 

meteorological analysis in Section 2.3 shows that the periods of high PM2.5 concentrations in Tacoma are 

characterized as occurring with wind speeds of less than two miles per hour, effectively limiting transport from 

outside the nonattainment area. The surface wind directions associated with frequent 1-hour average concentrations 

greater than 35 μg/m3

, 80 through 130 degrees, will also limit the source mix expected to contribute to the design 

value at Tacoma South L St. 

3.1.2. Pros and Cons of Using Linear Rollback for Attainment Demonstration in Tacoma 

Using a linear rollback model as an attainment demonstration tool has advantages and disadvantages. The chief 

advantages are that it is simple to understand, communicate, use, and requires relatively few additional resources. 

Rollback can directly employ the available meteorological analysis, emissions inventory, and source apportionment 

analysis to estimate the emissions reductions needed to reach attainment. 

The disadvantages are that it is a highly simplified analysis that does not account for the transport, dispersion, 

deposition, or chemical transformation of emissions after release to the atmosphere. Hence, to the extent that 

secondary PM2.5 contributes an increasing portion of the total PM2.5 mass, the rollback methodology can be expected 

to have in increasing uncertainty. 

Although each of the forms of the rollback equation from de Nevers and Morris addresses another factor important 

for modeling the ambient concentrations from emissions, even the most complete version is not likely to be as 

accurate as a chemical transport dispersion model. Having said that, the extent to which the accuracy of the two 

methods diverge can be expected to be a function of the quality of the refinements used in the photochemical 

dispersion model (e.g., the quality of spatial and temporal placement of emissions, the quality of the chemical 

mechanism, the quality of the meteorological inputs). The effort to create high quality inputs for photochemical 

dispersion modeling is considerable and is discussed more fully in the section on 3-Dimensional Grid-Based 

Photochemical Modeling. 

Table 3.1 summarizes some areas that have used rollback and their experiences. Of the areas considered here, 

rollback has been associated with six areas that were brought back into attainment out of a total of ten areas. It 

22
should also be noted that none of the rollback examples listed in Table 3.1 are for PM2.5, and the authors were 

unable to find an example of rollbacks’ use in a PM2.5 attainment demonstration. This is likely because most areas 

in nonattainment for PM2.5 are dominated by secondary aerosols, the formation of which involves complex 

chemistry requiring the use of a photochemical model to predict. Having said that, of the examples listed in Table 

3.1, only one case was linear rollback used in a SIP process that did not result in attainment, and one other case was 

indeterminate (the facility shut down before the recommended reductions were implemented). Only one of the ten 

areas (Fresno County) explicitly accounted for secondary production as a component of PM10, and it has not yet 

reached attainment of the standard. In Fresno County, secondary production was considered in developing interpollutant offset ratios20 for new sources in the PM10 nonattainment area. The background PM10 concentration 

exceeded the standard and Fresno County was forced to develop the inter-pollutant offset ratios so that new sources 

could be permitted. Fresno County used a combination of Chemical Mass Balance source apportionment modeling 

and modified rollback to calculate the effects on design values resulting from reducing emissions of various 

chemical species (a speciated rollback). Assigning species to source categories allowed them to evaluate the effects 

of reducing emissions from those source categories. 

Table 3.1. Examples of areas where rollback has been used in an attainment demonstration, the pollutant targeted, 

the date of the analyses, and whether there was success. 

Agency Pollutant Date Was the Area Brought Into 

Attainment?* 

Delaware NO2 1972 Yes 

Chugiak-Eagle River, AK PM10 1987 Yes 

San Joaquin Valley, CA PM10 1997 No 

Maricopa Co, AZ PM10 1999 TBD 

Clark county, NV PM10 2000 Yes 

Douglas Co, AZ SO2 2001 Yes (facility dismantled) 

South Coast Air Basin, CA PM10 2003 Yes 

Denver-Boulder, CO21 CO 2003 Yes 

Pocatello, ID PM10 2006 Yes 

Fresno County, CA** PM10 2006 TBD 

*As of June 15, 2010 

**Secondary production considered 

3.1.3. Steps Required to Make an Attainment Demonstration with Linear Rollback 

The following is a list of steps that provide more detail related to how an attainment demonstration would be 

achieved using rollback. Some of these steps could be done concurrently. 

a) The first step in a rollback attainment demonstration is the development of a detailed rollback modeling 

protocol that, in part, details each of the steps below. This protocol is developed in consultation with all parties 

that will be involved in the technical work as well as EPA. 

b) Determine the geographic domain the rollback model will consider. By default for rollback this is the 

boundaries of the NAA (see Figure 1.1). 

c) Select the ‘base year’ for which emissions inventories (EIs) will be developed and from which future year 

emissions will be projected. 

d) Develop a comprehensive, accurate, current inventory of actual emissions for the selected base year that would 

include resolving the outstanding discrepancies in emissions between PSCAA and Ecology as described in 

section 2 above. 

e) Refine emissions inventories from county-wide emissions to those within the NAA. 

f) Determine what background concentration will be assumed for the rollback modeling. 

g) Identify the sources of primary and secondary PM2.5 in the inventory. 

h) Estimate the amounts of primary and secondary PM2.5 impacting the FRM monitor during times of high PM2.5

 

20 www.energy.ca.gov/sitingcases/bullard/documents/applicant/DA-response-1/appendix-A/Attachment_5-1.pdf 21 Colorado Department of Public Health and Environment, June 2003. Technical Support Document, Carbon 

Monoxide Maintenance Plan Revision For the Denver-Boulder Attainment Area. 

23
concentration. 

i) Determine the amount of total PM2.5 to be reduced at the monitor to achieve attainment including additional 

reductions to be used as a safety factor. 

j) Apportion primary emissions controls among emissions categories within the NAA to achieve the necessary 

PM2.5 reductions at the monitor. 

3.2. 3-Dimensional Grid-Based Photochemical Modeling 

The 3-dimensional grid-based photochemical model with a history of use over the Tacoma airshed is the 

Community Multi-scale Air Quality (CMAQ) model22 employed with the Weather Research Forecast (WRF) 

meteorological model (and previously with the MM5 meteorological model). 

3.2.1. Model Description 

The CMAQ model creates a 3-dimentional representation of the atmosphere, where the horizontal plane is divided 

into a grid of uniform spacing and the vertical plane is divided into layers of non-uniform spacing. The CMAQ 

model is best described as a modeling system that requires three types of modeling components: a meteorological 

model for the description of atmospheric states and motions, an emissions model for man-made and natural 

emissions that are injected into the atmosphere, and a chemical transport model for simulating chemical transport, 

transformations, and deposition. The CMAQ model is run for a defined period of time and generates predictions 

(usually set to hourly) for each grid box over a defined geographic area. The model can be used in retrospective 

cases to simulate historical events or as a forecast system to predict future conditions. In the Tacoma area, the 

CMAQ model currently (July 2010) runs on a daily basis via the AIRPACT air quality forecast system. 

To use the CMAQ model to make an attainment demonstration, a ‘base case’ emissions inventory (EI) is developed 

for an historical period and the CMAQ model performance is evaluated against the monitoring record. If the model 

performance is deemed adequate, then the base case EI is projected to a future year and modified to include the 

proposed emissions changes that are believed needed to achieve attainment. A second CMAQ model run is then 

performed using the future year EI. Attainment of the air quality standard is demonstrated if the ratio of future 

modeled PM2.5 to base case modeled PM2.5, multiplied by the observed PM2.5, is less than the standard.23

3.2.2. Current Modeling Capability 

EPA Region 10, Ecology, and PSCAA currently work with Washington State University (WSU) and the University 

of Washington (UW) to produce daily air quality forecasts for the Pacific Northwest using the CMAQ modeling 

system. The WSU forecast system, referred to as AIRPACT-324, is run daily on computers at WSU and UW on a 

12-km grid covering a regional domain as seen in Figure 3.2. EPA Region 10 runs a mirror operation of that system 

on computers at its Seattle office. 

AIRPACT-3, in addition to being an air quality forecast system, has the capability to be run retrospectively for 

historical episodes. Hence, it could be used as the basis for attainment demonstration modeling for PM2.5 in 

Tacoma. 

 

22 http://www.cmaq-model.org/ 23 A more detailed description of the model attainment test can be found in the EPA modeling guidance document at 

the following web link http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rh-guidance.pdf. 24 http://lar.wsu.edu/airpact-3/ 

24
Figure 3.2. The AIRPACT-3 domain. 

AIRPACT-3 is an automated air quality forecast system running nightly for the Pacific Northwest and reporting to 

the public daily via the web. The AIRPACT-3 system combines air chemistry and meteorology using community 

modeling software including the Weather Research and Forecast (WRF) meteorological model, the Sparse Matrix 

Operator Kernel Emissions (SMOKE) processing system, and the Community Multi-Scale Air Quality Model 

(CMAQ). WRF output fields are obtained from the University of Washington mesoscale meteorological forecast 

operations on a daily basis.25 The WRF meteorological fields are processed using the MCIP program prior to use in 

CMAQ. The AIRPACT-3 geographic domain is shown in Figure 3.2 and uses a 95 by 95 grid of cells, each cell 

being 12km by 12km, with 21 vertical layers from the surface to the tropopause; highways and a few major cities are 

shown. Five large urban areas are included in the domain: Seattle-Tacoma, Portland, Vancouver (BC, Canada), 

Boise, and Salt Lake City. The SMOKE model is used to process anthropogenic and biogenic emission categories 

for each forecast simulation. Area and non-road emissions are based on the 2002 EPA NEI as adjusted using the 

EPA’s Economic Growth Analysis System software. On-road emissions are generated using emission factors from 

the EPA MOBILE v6.2 model and state specific activity data and are adjusted for WRF-forecast temperature. 

Anthropogenic emissions over the provinces of British Columbia and Alberta, Canada are included from the 2000 

Greater Vancouver Regional District inventory. Woodstove emissions inventories for WA, OR and ID were 

updated in 2008 and implemented in AIRPACT-3 in early 2009. Forest fire (wildfire) emissions had originally been 

obtained from the US Forest Service BlueSky26 system, which utilized ICS-209 reports to provide necessary inputs 

to SMOKE; that data stream for wildfire emissions became unavailable in August 2008. Currently AIRPACT-3 is 

implementing use of the BlueSky Framework for accessing SMARTFIRE wildfire emissions. 

 

25 http://www.atmos.washington.edu/mm5rt/ 26 http://www.airfire.org/bluesky

25
3.2.3. Current Model Performance 

Analysis of model performance is a fundamental element of attainment demonstration modeling. A model that 

cannot adequately reproduce historical episodes of poor air quality cannot be relied upon to produce credible 

predictions of air quality under emissions control scenarios. Both EPA Region 10 and WSU have performed 

independent analysis of AIRPACT-3 model performance for PM2.5. However, it should be noted that this is an 

analysis of AIRPACT-3 model performance as a forecast system. Typically, when the CMAQ model is run for 

historical episodes in an attainment demonstration effort, there is considerable effort made to improve 

meteorological and emissions inputs over those used in forecast mode. Hence, were the AIRPACT-3 system to be 

used for attainment demonstration modeling, it is likely that the model performance would be improved over 

forecast performance as described below. This approach was recently used in a SIP analysis for ozone in the 

Portland/Vancouver area27. 

The EPA modeling guidance document referenced above lists several statistical metrics commonly used to evaluate 

model performance. The statistical metrics used here conform with the EPA guidance and are described in more 

detail in the Appendix. Boylan and Russell (2006)28 proposed model performance goals and criteria for the mean 

fractional bias (FB) and mean fractional error (FE) statistics that are now widely used in the modeling community. 

Boylan and Russell defined model performance ‘goals’ as the highest level of accuracy the model can be expected 

to achieve while model performance ‘criteria’ were defined as an acceptable level of performance. Tabulated model 

performance statistics are listed in the Appendix. Model performance for FB and FE are also shown graphically 

below. A more detailed explanation of the model performance statistics, goals, and criteria can be found in the 

Appendix. 

3.2.3.1. EPA Region 10 Model Performance Analysis 

EPA Region 10 conducted a multi-year retrospective run of the current AIRPACT-3 system producing hourly 

predictions from January 1, 2003 through December 31, 2006. This retrospective air quality modeling used hourly 

emissions based on the AIRPACT-3 2005 emissions inventory used in the forecast system and based on datespecific hourly meteorological model data from an archive of forecast model output at UW. In general it was found 

that, under conditions of elevated PM2.5, the AIRPACT-3 system over-predicts PM2.5 concentrations in major urban 

areas of the Pacific Northwest and under-predicts PM2.5 concentrations in smaller population centers. The initial 

interpretation of these results suggests that the 12 km grid resolution does not adequately resolve processes in 

smaller population (source) areas, but that emission inventories for PM may be overestimated (particularly in large 

urban areas). 

Model performance for 24-hr average PM2.5 was analyzed for the winter months of October through March, for the 

years 2003 – 2006, and for the major urban centers of Portland, Tacoma, and Seattle. In this analysis the 

AIRPACT-3 output was post-processed with the PMx utility (Jiang et al., 2006)29. The PMx utility truncates the 

aerosol size distributions in CMAQ to match the 2.5 μm cut point in PM2.5 monitors. CMAQ simulates the aerosol 

size distribution as a series of overlapping log-normal distributions. A portion of the accumulation mode aerosol 

size distribution can range larger than PM2.5 and a portion of the course mode aerosol distribution can be smaller 

than PM2.5. The PMx utility accounts for this by removing accumulation mode mass in CMAQ beyond PM2.5 and 

including the portion of course mode mass below PM2.5. The performance analysis was broadened to include 

Portland and Seattle, rather than just Tacoma, to attempt to show the range of model performance obtained for 

several of the major urban areas of the northwest. Additionally, as discussed in more detail below, analysis of 

chemically speciated model performance was not available for Tacoma because of limited data but was possible for 

Portland and Seattle. Model performance statistics for PM2.5 FB and FE are summarized in Figures 3.3 and 3.4; 

 

27 http://www.deq.state.or.us/aq/planning/docs/wsuPortland_sip_report.pdf 28 Boylan, J. W., and A. G. Russell (2006), PM and light extinction model performance metrics, goals, and criteria 

for three-dimensional air quality models, Atmos. Environ., 40(26), 4946– 4959. 

29 Jiang, W. M., S. Smyth, E. Giroux, H. Roth, and D. Z. Yin (2006), Differences between CMAQ fine mode 

particle and PM2.5 concentrations and their impact on model performance evaluation in the Lower Fraser Valley, 

Atmos. Environ., 40, 4973– 4985. 

26
tables showing the full suite of model performance statistics are located in the Appendix. Model performance is 

discussed more fully below. 

Unlike other pollutants listed under the NAAQS, PM2.5 is an accumulation of multiple chemical species. The major 

categories of chemical species in PM2.5 are sulfates (SO4), nitrates (NO3), organic mass (OM), elemental carbon 

(EC), and soil elements (SOIL). Each of these chemical species has its own emissions, chemical formation, 

transformation, and removal processes in the atmosphere. As such, it is important to investigate not only model 

performance for bulk PM2.5, but also model performance for each chemical component of PM2.5. 

In recognition of this, a subset of PM2.5 monitoring sites in the Pacific Northwest have co-located PM2.5 Chemical 

Speciation Samplers. These samplers allow separation of the bulk PM2.5 into the major categories of chemical 

species. Because the PM2.5 chemical speciation sampler at the Tacoma site did not begin operation until mid 2006, 

it was not possible to calculate model performance statistics for the speciated components of PM2.5 in Tacoma for 

this EPA analysis. For the Seattle and Portland monitoring sites, chemically speciated model performance was 

analyzed for the winter months of Oct – Mar and for the years 2003 – 2005 (2006 speciated data was unavailable at 

the time this analysis was performed). FB and FE model performance statistics for OM and EC are summarized in 

Figures 3.5 through 3.8. OM and EC were on average the chemical species with the highest percent contribution to 

PM2.5 on days that exceeded the NAAQS standard, so FB and FE for these species are plotted along with that for 

bulk PM2.5 below. Tables showing the full suite of model performance statistics, including that for sulfate and 

nitrate, are located in the Appendix. Model performance is discussed more fully in section 3.2.3.3 below. 

3.2.3.2. WSU and Ecology Model Performance Analysis 

The AIRPACT-3 air-quality forecasting system was evaluated for PM2.5 for the period of August through November 

2004, with the results as shown in Table 3.2 (adapted from Chen et al., 2008)30 and for this analysis results were not 

plotted. In this analysis the AIRPACT-3 output was also post-processed with the PMx utility. Chen et al. evaluated 

two monitoring networks separately. Data from the EPA-AQS system is mostly from urban and suburban areas, 

while data from the IMPROVE network is rural. This evaluation encompassed the entirety of the AIRPACT-3 

domain (WA, OR, ID, southern BC, and boundary areas). Several differences exist between the system as evaluated 

by Chen et al. and the current (July 2010) AIRPACT-3 system: 1) Meteorology was from the MM5 model but is 

now from the WRF model; 2) CMAQ results were always generated using initial conditions (IC) from the previous 

day but until March, 2010 these IC failed to pass along the semi-volatile aerosol precursors; 3) wild-fire emissions 

were obtained from the BlueSky system, but this arrangement failed in August 24, 2008 and has not yet been 

replaced, leaving wild-fire emissions out from late 2008 until the present (July 2010). As shown in Table 3.2, 

fractional bias and error statistics for PM2.5 were within the accepted model performance criteria for the 2004 

analyses. 

Table 3.2. Summary of AIRPACT-3 forecast performance statistics* for 24-hr PM2.5 by the EPA-AQS and 

IMPROVE measurement networks for August – November 2004. 

Monitoring 

Network 

N Observed 

Mean 

(μg/m3

) 

Model 

Mean 

(μg/m3

) 

MB 

(μg/m3

) 

ME 

(μg/m3) 

FB 

(%) 

FE 

(%) 

NMB 

(%) 

NME 

(%) 

EPA-AQS 1014 11 13 2.1 8.0 3 59 17 70 

IMPROVE 234 6.8 9.0 2.2 5.5 2 54 32 81 

* For a description of model performance statistics used in this report, see the Appendix. The color shading of the 

FB and FE statistics denotes whether the statistic meets typical model performance goals (green), meets model 

performance criteria (orange), or falls outside of model performance standards (red). 

More recently, WSU in collaboration with Ecology analyzed model performance for the AIRPACT-3 forecast 

system for the FRM and CSN monitors at Tacoma South L St and for the winter months of Oct – Mar, 2006 – 2010. 

However, unlike the EPA model performance analysis, and that described above in Chen et al., this WSU/Ecology 

 

30 Chen, J., J. Vaughan, J. Avise, S. O’Neill, and B. Lamb (2008), Enhancement and evaluation of the AIRPACT 

ozone and PM2.5 forecast system for the Pacific Northwest, J. Geophys. Res., 113, D14305, 

doi:10.1029/2007JD009554. 

27
analysis was not post-processed with the PMx utility. In the absence of running the PMx utility, it is likely that 

CMAQ model results will have an additional positive bias. This should be considered when viewing the model 

results. 

The FB and FE model performance statistics for Tacoma from WSU/Ecology’s analysis are plotted alongside the 

EPA results in Figures 3.3 though 3.8. Figures 3.3 and 3.4 are the performance results for total PM2.5 mass, where 

samples were grouped into four bins (PM2.5 mass measured from 0-10 μg/m3

, 10-20 μg/m3, 20-30 μg/m3, and >30 

μg/m3

) to show performance at various ranges of overall mass. Figures 3.5 through 3.8 depict the model 

performance for the OM and EC portions of PM2.5. In order to show the model performance for the OM and EC 

fractions of PM2.5 over the same total PM2.5 mass ranges as in Figures 3.3 and 3.4, the same binning of samples was 

retained. So, for example, the three data points in Figure 3.5 closest to the y axis represent the average OM 

concentration in Seattle, Portland, and Tacoma when total PM2.5 was between 0-10 μg/m3

. Tables showing the full 

suite of model performance statistics are located in the Appendix. Model performance is discussed more fully in the 

following section. 

28
-100

-80

-60

-40

-20

0

20

40

60

80

100

0 10 20 30 40 5

CMAQ Mean Fractional Bias, %

Average PM2.5 concentration in October- March, μg/m3

0

Model performance goals Seattle '03-'06:EPA

Model performance criteria Tacoma '03-'06:EPA

Portland '03-'06:EPA Tacoma '06-'10:WSU/ Ecology

Figure 3.3. AIRPACT-3 PM2.5 mean fractional bias for Portland, Tacoma, and Seattle for the winter months of 

October–March and for several multi-year periods. Data for each city were divided into four bins based on 

measured PM2.5 mass from 0-10 μg/m3

, 10-20 μg/m3, 20-30 μg/m3, and >30 μg/m3.

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 5

CMAQ Mean Fractional Error, %

Average PM2.5 concentration in October- March, μg/m3

0

Model performance goals Seattle '03-'06:EPA

Model performance criteria Tacoma '03-'06:EPA

Portland '03-'06:EPA Tacoma '06-'10:WSU/ Ecology

Figure 3.4. AIRPACT-3 PM2.5 mean fractional error for Portland, Tacoma, and Seattle for the winter months of 

October–March and for several multi-year periods. Data for each city were divided into four bins based on 

measured PM2.5 mass from 0-10 μg/m3

, 10-20 μg/m3, 20-30 μg/m3, and >30 μg/m3.

29
-100

-80

-60

-40

-20

0

20

40

60

80

100

0 5 10 15 20 25 30

CMAQ Mean Fractional Bias, %

Average OM concentration in October- March, μg/m3

Model performance goals

Model performance criteria

Seattle '03-'06:EPA

Portland '03-'06:EPA

Tacoma '06-'10:WSU/ Ecology

Figure 3.5. AIRPACT-3 OM mean fractional bias for Portland, Tacoma, and Seattle for the winter months of 

October–March and for several multi-year periods. Data for each city were divided into four bins based on 

measured PM2.5 mass from 0-10 μg/m3

, 10-20 μg/m3, 20-30 μg/m3, and >30 μg/m3.

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30

CMAQ Mean Fractional Error, %

Average OM concentration in October- March, μg/m3

Model performance goals

Model performance criteria

Seattle '03-'06:EPA

Portland '03-'06:EPA

Tacoma '06-'10:WSU/ Ecology

Figure 3.6. AIRPACT-3 OM mean fractional error for Portland, Tacoma, and Seattle for the winter months of 

October–March and for several multi-year periods. Data for each city were divided into four bins based on 

measured PM2.5 mass from 0-10 μg/m3

, 10-20 μg/m3, 20-30 μg/m3, and >30 μg/m3.

30
-100

-80

-60

-40

-20

0

20

40

60

80

100

012345

CMAQ Mean Fractional Bias, %

Average EC concentration in October- March, μg/m3

Model performance goals

Model performance criteria

Seattle '03-'06:EPA

Portland '03-'06:EPA

Tacoma '06-'10:WSU/ Ecology

Figure 3.7. AIRPACT-3 EC mean fractional bias for Portland, Tacoma, and Seattle for the winter months of 

October–March and for several multi-year periods. Data for each city were divided into four bins based on 

measured PM2.5 mass from 0-10 μg/m3

, 10-20 μg/m3, 20-30 μg/m3, and >30 μg/m3.

0

10

20

30

40

50

60

70

80

90

100

012345

CMAQ Mean Fractional Error, %

Average EC concentration in October- March, μg/m3

Model performance goals

Model performance criteria

Seattle '03-'06:EPA

Portland '03-'06:EPA

Tacoma '06-'10:WSU/ Ecology

Figure 3.8. AIRPACT-3 EC mean fractional error for Portland, Tacoma, and Seattle for the winter months of 

October–March and for several multi-year periods. Data for each city were divided into four bins based on 

measured PM2.5 mass from 0-10 μg/m3

, 10-20 μg/m3, 20-30 μg/m3, and >30 μg/m3.

31
3.2.3.3. Model Performance Results and Discussion 

Figures 3.3 – 3.8 depict FB and FE for PM2.5, OM and EC, respectively. Model performance data for each city has 

been binned in to four PM2.5 mass ranges, that from 0-10 μg/m3

, 10-20 μg/m3, 20-30 μg/m3, and >30 μg/m3. For 

example, the EC data plotted that lies closest to the y axis on the FB and FE plots is the average EC concentration 

for each city when measured PM2.5 was between 0-10 μg/m3

. Similar FB and FE statistics for sulfate and nitrate are 

not plotted, but the tabulated statistics can be found in the Appendix. 

The FB and FE model performance statistics for total PM2.5 fall within the accepted goals or criteria for all four 

PM2.5 mass bins. As can be seen in Figure 3.3, in most cases AIRPACT-3 overestimates PM2.5 in the major cities of 

the Pacific Northwest (PNW), with the exception of EPA’s analysis of PM2.5 in Tacoma for the highest mass bin. 

WSU/Ecology’s analysis for Tacoma shows systematically higher FB than EPA’s, which is consistent with the lack 

of PMx post-processing in WSU/Ecology’s analysis. In most cases post-processing with PMx would cause model 

predicted concentrations to go down and therefore a reduction in the fractional bias. In the cases where fractional 

bias is negative, this would make it more negative and increase the corresponding fractional error. 

As depicted in Figures 3.5 and 3.6, for the OM portion of PM2.5 FB and FE fell outside of the model performance 

criteria for Portland when PM2.5 was less than 10 μg/m3

 and for Seattle when PM2.5 was less than 20 μg/m3. 

However, for PM2.5 greater than 20 μg/m3

 OM was within FB and FE goals or criteria for all three cities. It is 

interesting to note that WSU/Ecology’s analysis of FB in Tacoma is less than that for EPA’s analysis of Seattle and 

Portland, despite the lack of PMx being run for the WSU/Ecology’s Tacoma data. This may be due to the different 

years that were analyzed or perhaps differences in the quality of the emissions inventories. A more definitive 

explanation would require further inquiry. 

It was also found that the FB and FE statistics for the EC (Figures 3.7 and 3.8) and nitrate (not plotted) portions of 

PM2.5 fell within the goals or criteria for all three cities and for all PM2.5 mass ranges. For the sulfate portion of 

PM2.5, the three cities were within model performance goals or criteria under most conditions except in Portland 

when observed PM2.5 was between 20-30 μg/m3

. 

Overall, for these simulations, the AIRPACT-3 model is meeting or exceeding the accepted model performance 

criteria for observed PM2.5 concentrations in Tacoma, for observed PM2.5 concentrations above 20 μg/m3

 in Seattle, 

and for observed PM2.5 concentrations in Portland except for sulfate. Options for improving model performance are 

discussed in the following section. 

3.2.4. Options for Improving Model Performance 

 

The model performance summarized above is for the AIRPACT-3 system run as a forecast model. As previously 

noted, retrospective case studies using a modeling system for attainment demonstrations generally involve the 

tailoring of meteorological and emissions inputs to match the historical record. By its very nature, a forecast system 

does not permit this tailoring. Hence, it is likely that model performance could be improved by: 

 improving meteorological inputs through initializing the meteorological model with more extensive 

observational datasets, selecting sub-grid scale parameterization schemes that yield results best matching the 

historical record, and using four-dimensional data assimilation to ‘nudge’ the simulated meteorology toward the 

historical record. 

 improving emissions inventories by matching point source emissions to actual facility operations. 

Other efforts that might lead to improved model performance are: 

 

 increasing the model horizontal resolution from 12 km to 4 km or better 

 emission inventory improvements such as using Ecology’s 2009 EI and making improvements to the wood 

smoke emissions temporal allocation and emissions factors. 

32
3.2.5. Pros and Cons of Using Photochemical Models for Attainment Demonstration in Tacoma 

The advantages of using a photochemical modeling system as a PM2.5 attainment demonstration tool are that it: 

 accounts for the chemical transformations of pollutants after they are emitted and can simulate the secondary 

production of PM2.5 in the atmosphere from gaseous precursors; 

 accounts for the effects of weather and topography on pollutants; 

 appropriately places emissions geographically and temporally relative to the monitor and simulates the 

transport, diffusion and dilution of emissions and their relative impact on the monitor; 

 provides an estimate of PM2.5 concentrations in unmonitored areas near the monitor; 

 predicts the impact of emissions controls on unmonitored areas near the monitor (i.e., can predict if emissions 

controls are having variable effectiveness leaving hot spots in unmonitored areas); 

 can estimate the effects of emissions uncertainties through sensitivity experiments where emissions inputs are 

perturbed, the model is rerun, and the model response is quantified; 

 predicts any non-linear effects of emissions controls throughout the geographic region modeled; 

 allows the exploration of PM2.5 sensitivity to a variety of control scenarios to determine which emissions 

controls are most effective and which are ineffective or counterproductive due to non-linear effects; 

 can account for expected emissions growth or other changes outside the nonattainment area and the impact of 

that on PM2.5 within the nonattainment area; and 

 is the tool capable of giving the most accurate results for this task. 

The primary disadvantage of using a photochemical modeling system as a PM2.5 attainment demonstration tool is 

that it is highly resource intensive to conduct this kind of analysis. Specifically, the following tasks are highly 

resources intensive: 

 

 refining the regional modeling capability, currently at 12 km, to a finer resolution of 4 km or 1.3 km, 

 developing meteorological model data for a series of historical episodes, 

 developing emissions model data for both the historical episodes and future year emission control scenarios, 

 running the modeling system for the historical episodes, 

 evaluating the modeling system’s performance, and 

 running the modeling system for the future year scenarios. 

3.2.6. Steps to Make an Attainment Demonstration with a Photochemical Modeling System 

Detailed EPA guidance on using photochemical modeling systems to perform a model attainment demonstration can 

be found in the document titled “Guidance on the Use of Models and Other Analyses for Demonstrating Attainment 

of Air Quality Goals for Ozone, PM2.5, and Regional Haze.”31 Portions of the text below are taken wholly or in part 

from that document. 

In general, a modeled attainment demonstration consists of (a) analyses which estimate whether selected emissions 

reductions will result in ambient concentrations that meet the NAAQS and (b) an identified set of control measures 

which will result in the required emissions reductions. EPA guidance suggests several strategies when using 

photochemical modeling for an attainment demonstration. First, the sensitivity of the model to emissions changes 

should be used in a relative sense rather than an absolute sense and second, the focus should be on the composite 

responses of the model averaged over a number of days to help circumvent the problem of not being able to simulate 

precisely all of the details involved in an exceedance on an individual day. 

The following is a list of steps that provide more detail related to how an attainment demonstration would be 

achieved using a photochemical modeling system for the Tacoma NAA. Some of these steps could be done 

concurrently. 

 

31 http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rh-guidance.pdf 

33
a) The first step in a modeling attainment demonstration is the development of a detailed modeling protocol that, 

in part, details each of the steps below. This modeling protocol is developed in consultation with all parties that 

will be involved in the technical work as well as EPA. 

b) The PM2.5 monitoring record is reviewed for the Tacoma L St. site to select a number of historical episodes 

where the PM2.5 24-hr NAAQS was exceeded, that are representative of the range of meteorological conditions 

where exceedances occur, and that have sufficient meteorological and chemically speciated PM2.5 data to be 

able to adequately conduct a model performance evaluation. Ideally, episodes will be selected where 24-hr 

PM2.5 was close to the design value. 

c) Determine the geographic domain over which modeling will be conducted. For photochemical modeling, this is 

the boundaries of the NAA plus a surrounding buffer. 

d) Select the ‘base year’ for which emissions inventories (EIs) will be developed and from which future year 

emissions will be projected. 

e) Develop a comprehensive, accurate, current inventory of actual emissions for the selected base year that would 

include resolving the outstanding discrepancies in emissions between PSCAA and Ecology as described in 

section 2 above. 

f) The completed base year EI will need to be spatially and temporally allocated using an appropriate emissions 

model (e.g., SMOKE). This step takes coarser level EI data (e.g., county level emissions, yearly total 

emissions) and uses spatial and temporal surrogates to place emissions on the geographic scale of the model 

resolution (e.g.; 4 km, 1.3 km) and at the temporal resolution of the model (e.g., hourly). Additional work is 

likely needed to improve the temporal allocation of wood smoke emissions. The base inventory will also need 

to be adjusted based on meteorological inputs from a meteorological model as well as activity data from 

industrial sources during the episodes selected above. 

g) A meteorological model (e.g., WRF) will need to be run at the same resolution as the other modeling 

components and for the historical episodes chosen. The meteorological model performance will need to be 

evaluated against the historical record and should meet or exceed accepted model performance standards. 

Typically a meteorological model will be run multiple times using a variety of options in order to optimize the 

models performance for the scenarios chosen. The meteorological model output is then run through a preprocessor (e.g., MCIP) so that it is appropriately formatted for input into the photochemical model. 

h) The photochemical model (e.g., CMAQ) is run at the desired resolution using as inputs the meteorological and 

emissions model results for the historical episodes chosen. Inputs to the geographic boundaries of the model 

(i.e., boundary conditions) and startup initial conditions could be obtained from the coarser resolution 

AIRPACT-3 modeling system described above. The model performance results for PM2.5 at the Tacoma L St. 

monitoring site will need to be evaluated against accepted model performance standards similar to the 

performance analysis of the AIRPACT-3 system discussed above. Model performance should meet or exceed 

accepted model performance standards. Methods for improving model performance, if required, are also 

discussed above. 

i) The ‘base case’ EI for the historical episodes is then projected to the desired future year using the emissions 

model and including the emissions modifications to be tested. Photochemical modeling is conducted with the 

future year emissions scenario while keeping the other model inputs the same as the historical episodes. The 

species by species ratio of model results, future scenario to historical scenario, is applied to the chemically 

speciated data from the monitor on days that exceeded the NAAQS in the historical scenarios to determine if 

attainment is achievable under this future year emissions scenario. Often, multiple future year emissions 

scenarios are explored. 

A. Appendices 

A.1. Boxplots and Mapping of Mobile Monitoring Data From Tacoma 

Below are box plots and maps of mobile measurements of particles on selected days between 12/08 and 2/09 

(winter) and between 7/09 and 8/09 (summer). Afternoon and evening sampling occurred between 4 PM – 7 PM 

and 9 PM – 1 AM, respectively. The left axis in the box plots represents the concentration of each parameter in the 

figure. Mean is in red and median is the black center line in the box. The upper and lower ends of the box are 3rd

and 1st quartiles respectively. The upper and lower bracket ends denote the 95th and 5th percentiles. 

34
Box plots

bsp bap PAH

-50

0

50

100

150

200

250

Figure A.1. Summer Afternoons 

Box plots

bsp bap PAH

-50

0

50

100

150

200

250

Figure A.2. Summer Evenings 

35
Box plots

bsp bap PAH

-50

0

50

100

150

200

250

Figure A.3. Winter Afternoons 

Box plots

-50 bsp bap PAH

0

50

100

150

200

250

Figure A.4. Winter Evenings 

36
Figure A.5. Spatial distribution of temporally adjusted particle light scattering coefficient. Measurements were taken 

in the late afternoon and evening on selected winter days between December, 2008 and February, 2009 and on 

selected summer days between July and August, 2009. 

37
Figure A.6. Spatial distribution of temporally adjusted particle light absorption coefficient. Measurements were 

taken in the late afternoon and evening on selected winter days between December, 2008 and February, 2009 and on 

selected summer days between July and August, 2009. 

38
Figure A.7. Spatial distribution of temporally adjusted particle-bound PAHs. Measurements were taken in the late 

afternoon and evening on selected winter days between December, 2008 and February, 2009 and on selected 

summer days between July and August, 2009.

39
A.2. Model Performance Statistics Used 

Table A.1 lists the model performance metrics used in this report, their acronyms, and the formulas used to calculate 

them. 

Table A.1. PM2.5 Model Performance Statistics 

Statistic Acronym Statistic Units Statistic Name Statistic Formula 

MB μg/m3 Mean Bias (1/N) Σ (Model – Obs) 

ME μg/m3 Mean Error (1/N) Σ (|Model – Obs|) 

FB % Mean Fractional Bias 100 * (2/N) Σ ((Model – Obs)/(Model + Obs)) 

FE % Mean Fractional Error 100 * (2/N) Σ ((|Model – Obs|)/(Model + Obs)) 

NMB % Normalized Mean Bias 100 * Σ (Model – Obs) / Σ (Obs) 

NME % Normalized Mean Error 100 * Σ (|Model – Obs|) / Σ (Obs) 

Boylan and Russell (2006) proposed model performance goals and criteria for the fractional bias and fractional error 

statistics that are now widely used in the modeling community. Boylan and Russell defined model performance 

‘goals’ as the highest level of accuracy the model can be expected to achieve while model performance ‘criteria’ 

were defined as an acceptable level of performance. For PM2.5 concentrations above a few μg/m3

, model 

performance goals are FB ≤ ±30% and FE ≤ 50% and model performance criteria are FB ≤ ±60% and FE ≤ 75%. 

For observed PM2.5 concentrations below a few μg/m3

 these goals and criteria are relaxed based on formulas 

provided in Boylan and Russell. 

The following is a description of the model performance statistics as described in EPA’s modeling guidance. 

Mean Bias (MB): Mean bias is the average difference between model and observed concentrations. 

Mean Error (ME): Mean error is similar to mean bias except the absolute value of the difference is used so that the 

error is always positive. 

Mean Fractional Bias (FB): Normalized bias can become very large when a minimum threshold is not used. 

Fractional bias is used as a substitute. The fractional bias for cases with factors of 2 under- and over-prediction are -

67 and + 67 percent, respectively (as opposed to -50 and +100 percent, when using normalized bias). Fractional bias 

is a useful indicator because it has the advantage of equally weighting positive and negative bias estimates. The 

single largest disadvantage is that the predicted concentration is found in both the numerator and denominator. 

Mean Fractional Error (FE): Normalized error can become very large when a minimum threshold is not used. 

Therefore fractional error is used as a substitute. It is similar to the fractional bias except the absolute value of the 

difference is used so that the error is always positive. 

Normalized Mean Bias (NMB): This performance statistic is used as a normalization to facilitate a range of 

concentration magnitudes. This statistic averages the difference (model - observed) over the sum of observed values. 

Normalized mean bias is a useful model performance indicator because it avoids over inflating the observed range of 

values. 

Normalized Mean Error (NME): This performance statistic is used as a normalization of the mean error to facilitate 

a range of concentration magnitudes. This statistic averages the difference (model - observed) over the sum of 

observed values. Normalized mean error is a useful model performance indicator because it avoid over inflating the 

observed range of values. 

A.3. Tabulated Model Performance Statistics 

Table A.2. EPA’s model performance statistics* for 24-hr average PM2.5 in Portland, Tacoma, and Seattle during 

the winter months of Oct – Mar, 2003 – 2006. 

Monitoring 

Site** 

Data 

Segregation, 

N Observed 

PM2.5 

Model 

PM2.5

MB 

(μg/m3) 

ME 

(μg/m3) 

FB 

(%) 

FE 

(%) 

NMB 

(%) 

NME 

(%) 

40
monitored 

24-hr average 

PM2.5

Mean 

(μg/m3

) 

Mean 

(μg/m3) 

Portland, OR <10 μg/m3 138 6.2 10.4 4.2 4.9 37 52 67 79 

10-20 μg/m3 80 14.3 20.8 6.5 8.3 29 43 46 59 

20-30 μg/m3 17 23.4 23.0 -0.4 5.3 -8 26 -2 23 

>30 μg/m3 4 38.1 45.9 7.9 7.9 19 19 21 21 

Tacoma, WA <10 μg/m3 117 5.5 7.9 2.4 3.3 25 42 43 60 

10-20 μg/m3 52 14.1 17.3 3.3 6.5 9 39 23 46 

20-30 μg/m3 27 25.4 27.9 2.5 8.1 5 31 10 32 

>30 μg/m3 30 39.1 31.3 -7.8 12.4 -28 39 -20 32 

Seattle, WA <10 μg/m3 426 5.6 11.7 6.1 6.5 54 66 109 117 

10-20 μg/m3 186 13.6 24.2 10.6 11.5 48 57 78 84 

20-30 μg/m3 36 23.5 30.3 6.8 9.2 21 33 29 39 

>30 μg/m3 1 32.6 53.6 21.0 21.0 49 49 65 65 

* The color shading of the FB and FE statistics denotes whether the statistic meets typical model performance goals 

(green), meets model performance criteria (orange), or falls outside of model performance standards (red). 

** The following PM2.5 FRM monitoring sites were used in this analysis; Portland Roselawn site AQS 410510246, 

Tacoma South L St. site AQS 530530029, Seattle Beacon Hill site AQS 530330080. 

Table A.3. EPA’s model performance statistics* for elemental carbon (EC) from 24-hr samples of PM2.5 taken in 

Portland and Seattle during the winter months of Oct – Mar, 2003 - 2005. 

Monitoring 

Site** 

Data 

Segregation, 

monitored 

24-hr average 

PM2.5

N Observed 

EC 

Mean 

(μg/m3

) 

Model 

EC 

Mean 

(μg/m3) 

MB 

(μg/m3) 

ME 

(μg/m3) 

FB 

(%) 

FE 

(%) 

NMB 

(%) 

NME 

(%) 

Portland, OR <10 μg/m3 95 0.7 1.0 0.3 0.5 24 50 44 65 

10-20 μg/m3 54 1.2 1.9 0.6 0.8 34 47 52 64 

20-30 μg/m3 14 1.9 2.0 0.2 0.7 0 40 9 40 

>30 μg/m3 4 2.3 3.9 1.6 1.6 54 54 71 71 

 

Seattle, WA <10 μg/m3 109 0.5 0.9 0.4 0.5 47 61 74 89 

10-20 μg/m3 46 1.2 2.0 0.8 1.0 45 64 67 85 

20-30 μg/m3 8 1.7 2.4 0.7 1.0 30 50 40 58 

>30 μg/m3 0 

* The color shading of the FB and FE statistics denotes whether the statistic meets typical model performance goals 

(green), meets model performance criteria (orange), or falls outside of model performance standards (red). 

** The following PM2.5 CSN monitoring sites were used in this analysis; Portland Roselawn site AQS 410510246, 

Seattle Beacon Hill site AQS 530330080. 

Table A.4. EPA’s model performance statistics* for organic mass (OM) from 24-hr samples of PM2.5 taken in 

Portland and Seattle during the winter months of Oct – Mar, 2003 - 2005. 

Monitoring 

Site** 

Data 

Segregation, 

monitored 

24-hr average 

PM2.5

N Observed 

OM 

Mean 

(μg/m3

) 

Model 

OM 

Mean 

(μg/m3) 

MB 

(μg/m3

) 

ME 

(μg/m3) 

FB 

(%) 

FE 

(%) 

NMB 

(%) 

NME 

(%) 

Portland, OR <10 μg/m3 95 4.0 8.4 4.4 5.0 65 76 108 123 

10-20 μg/m3 54 9.4 15.7 6.3 7.6 41 54 67 81 

20-30 μg/m3 14 15.1 16.2 1.1 4.7 0 34 7 31 

>30 μg/m3 4 24.1 31.6 7.5 7.5 28 28 31 31 

 

Seattle, WA <10 μg/m3 109 2.9 8.2 5.3 5.4 90 93 185 188 

41
10-20 μg/m3 46 7.6 18.9 11.3 11.7 76 84 149 154 

20-30 μg/m3 8 12.4 22.6 10.3 11.1 52 60 83 89 

>30 μg/m3 0 

* The color shading of the FB and FE statistics denotes whether the statistic meets typical model performance goals 

(green), meets model performance criteria (orange), or falls outside of model performance standards (red). 

** The following PM2.5 CSN monitoring sites were used in this analysis; Portland Roselawn site AQS 410510246, 

Seattle Beacon Hill site AQS 530330080. 

Table A.5. EPA’s model performance statistics* for sulfate (SO4) from 24-hr samples of PM2.5 taken in Portland 

and Seattle during the winter months of Oct – Mar, 2003 - 2005. 

Monitoring 

Site** 

Data 

Segregation, 

monitored 

24-hr average 

PM2.5

N Observed 

SO4 

Mean 

(μg/m3) 

Model 

SO4 

Mean 

(μg/m3

) 

MB 

(μg/m3) 

ME 

(μg/m3) 

FB 

(%) 

FE 

(%) 

NMB 

(%) 

NME 

(%) 

Portland, OR <10 μg/m3 95 0.8 0.7 -0.2 0.3 -20 37 -20 35 

10-20 μg/m3 53 1.8 1.1 -0.8 0.8 -50 53 -41 44 

20-30 μg/m3 14 2.5 1.2 -1.3 1.3 -71 71 -53 53 

>30 μg/m3 4 3.8 1.7 -2.1 2.1 -65 65 -55 55 

 

Seattle, WA <10 μg/m3 109 0.6 0.9 0.2 0.3 33 46 39 54 

10-20 μg/m3 45 1.4 1.5 0.2 0.6 7 37 12 41 

20-30 μg/m3 8 3.1 1.8 -1.3 1.7 -45 63 -41 53 

>30 μg/m3 0 

* The color shading of the FB and FE statistics denotes whether the statistic meets typical model performance goals 

(green), meets model performance criteria (orange), or falls outside of model performance standards (red). 

** The following PM2.5 CSN monitoring sites were used in this analysis; Portland Roselawn site AQS 410510246, 

Seattle Beacon Hill site AQS 530330080. 

Table A.6. EPA’s model performance statistics* for nitrate (NO3) from 24-hr samples of PM2.5 taken in Portland 

and Seattle during the winter months of Oct – Mar, 2003 - 2005. 

Monitoring 

Site** 

Data 

Segregation, 

monitored 

24-hr average 

PM2.5

N Observed 

NO3 

Mean 

(μg/m3) 

Model 

NO3 

Mean 

(μg/m3

) 

MB 

(μg/m3) 

ME 

(μg/m3) 

FB 

(%) 

FE 

(%) 

NMB 

(%) 

NME 

(%) 

Portland, OR <10 μg/m3 95 0.8 0.5 -0.3 0.4 -54 66 -36 51 

10-20 μg/m3 53 1.9 1.7 -0.2 0.7 -23 45 -11 38 

20-30 μg/m3 14 2.5 1.4 -1.1 1.2 -58 61 -44 46 

>30 μg/m3 4 5.9 6.6 0.7 3.8 14 56 12 64 

 

Seattle, WA <10 μg/m3 109 0.5 0.7 0.2 0.3 1 49 30 64 

10-20 μg/m3 45 1.3 2.2 0.8 1.1 32 55 65 81 

20-30 μg/m3 8 2.3 3.7 1.4 1.6 29 40 63 69 

>30 μg/m3 0 

* The color shading of the FB and FE statistics denotes whether the statistic meets typical model performance goals 

(green), meets model performance criteria (orange), or falls outside of model performance standards (red). 

** The following PM2.5 CSN monitoring sites were used in this analysis; Portland Roselawn site AQS 410510246, 

Seattle Beacon Hill site AQS 530330080. 

Table A.8. WSU/Ecology’s model performance statistics* for 24-hr average PM2.5 in Tacoma during the winter 

months of Oct – Mar, 2006 – 2010. 

Monitoring 

Site** 

Data 

Segregation, 

monitored 

24-hr average 

N Observed 

PM2.5 

Mean 

(μg/m3

) 

Model 

PM2.5

Mean 

(μg/m3

) 

MB 

(μg/m3

) 

ME 

(μg/m3) 

FB 

(%) 

FE 

(%) 

NMB 

(%) 

NME 

(%) 

42
PM2.5

Tacoma, WA <10 μg/m3 167 5.2 10.0 4.9 5.4 52 62 95 105 

10-20 μg/m3 78 14.0 25.6 11.6 13.0 48 60 83 93 

20-30 μg/m3 20 23.7 41.1 17.4 20.2 42 58 73 85 

>30 μg/m3 26 42.1 48.7 6.7 17.1 10 37 16 41 

* The color shading of the FB and FE statistics denotes whether the statistic meets typical model performance goals 

(green), meets model performance criteria (orange), or falls outside of model performance standards (red). 

** The PM2.5 FRM monitoring site used in this analysis was the Tacoma South L St. site AQS 530530029. 

Table A.9. WSU/Ecology’s model performance statistics* for elemental carbon (EC) from 24-hr samples of PM2.5

taken in Tacoma during the winter months of Oct – Mar, 2006 – 2010. 

Monitoring 

Site** 

Data 

Segregation, 

monitored 

24-hr average 

PM2.5

N Observed 

EC

Mean 

(μg/m3

) 

Model 

EC 

Mean 

(μg/m3) 

MB 

(μg/m3) 

ME 

(μg/m3) 

FB 

(%) 

FE 

(%) 

NMB 

(%) 

NME 

(%) 

Tacoma, WA <10 μg/m3 65 0.5 0.8 0.2 0.5 26 69 45 87 

10-20 μg/m3 22 1.4 2.2 0.8 1.0 33 48 57 73 

20-30 μg/m3 8 2.6 2.2 -0.4 0.6 -23 29 -16 23 

>30 μg/m3 12 3.9 3.7 -0.2 1.8 -11 48 -5 45 

* The color shading of the FB and FE statistics denotes whether the statistic meets typical model performance goals 

(green), meets model performance criteria (orange), or falls outside of model performance standards (red). 

** The PM2.5 CSN monitoring site used in this analysis was the Tacoma South L St. site AQS 530530029. 

Table A.10. WSU/Ecology’s model performance statistics* for organic mass (OM) from 24-hr samples of PM2.5

taken in Tacoma during the winter months of Oct – Mar, 2006 – 2010. 

Monitoring 

Site** 

Data 

Segregation, 

monitored 

24-hr average 

PM2.5

N Observed 

OM

Mean 

(μg/m3

) 

Model 

OM 

Mean 

(μg/m3) 

MB 

(μg/m3

) 

ME 

(μg/m3) 

FB 

(%) 

FE 

(%) 

NMB 

(%) 

NME 

(%) 

Tacoma, WA <10 μg/m3 65 3.5 4.8 1.2 2.3 16 52 35 66 

10-20 μg/m3 22 8.5 12.9 4.4 5.4 31 43 52 64 

20-30 μg/m3 8 15.2 13.1 -2.1 6.0 -22 47 -14 39 

>30 μg/m3 12 23.4 21.9 -1.5 6.9 -12 34 -7 29 

* The color shading of the FB and FE statistics denotes whether the statistic meets typical model performance goals 

(green), meets model performance criteria (orange), or falls outside of model performance standards (red). 

** The PM2.5 CSN monitoring site used in this analysis was the Tacoma South L St. site AQS 530530029. 

Table A.11. WSU/Ecology’s model performance statistics* for sulfate (SO4) from 24-hr samples of PM2.5 taken in 

Tacoma during the winter months of Oct – Mar, 2006 – 2010. 

Monitoring 

Site** 

Data 

Segregation, 

monitored 

24-hr average 

PM2.5

N Observed 

SO4 

Mean 

(μg/m3

) 

Model 

SO4

Mean 

(μg/m3

) 

MB 

(μg/m3

) 

ME 

(μg/m3) 

FB 

(%) 

FE 

(%) 

NMB 

(%) 

NME 

(%) 

Tacoma, WA <10 μg/m3 65 0.5 0.7 0.2 0.4 31 58 47 82 

10-20 μg/m3 22 1.0 1.5 0.5 0.6 33 49 55 68 

20-30 μg/m3 8 1.1 1.1 0.0 0.3 -2 30 0 27 

>30 μg/m3 12 1.6 1.3 -0.2 0.3 -14 22 -13 21 

* The color shading of the FB and FE statistics denotes whether the statistic meets typical model performance goals 

(green), meets model performance criteria (orange), or falls outside of model performance standards (red). 

** The PM2.5 CSN monitoring site used in this analysis was the Tacoma South L St. site AQS 530530029. 

43
Table A.12. WSU/Ecology’s model performance statistics* for nitrate (NO3) from 24-hr samples of PM2.5 taken in 

Tacoma during the winter months of Oct – Mar, 2006 – 2010. 

Monitoring 

Site** 

Data 

Segregation, 

monitored 

24-hr average 

PM2.5

N Observed 

NO3 

Mean 

(μg/m3

) 

Model 

NO3 

Mean 

(μg/m3

) 

MB 

(μg/m3) 

ME 

(μg/m3) 

FB 

(%) 

FE 

(%) 

NMB 

(%) 

NME 

(%) 

Tacoma, WA <10 μg/m3 65 0.5 0.4 -0.1 0.3 -70 93 -18 70 

10-20 μg/m3 22 1.3 2.1 0.8 1.2 25 72 63 97 

20-30 μg/m3 8 1.6 1.9 0.3 1.2 -8 74 17 79 

>30 μg/m3 12 2.4 3.5 1.2 1.5 24 53 49 65 

* The color shading of the FB and FE statistics denotes whether the statistic meets typical model performance goals 

(green), meets model performance criteria (orange), or falls outside of model performance standards (red). 

** The PM2.5 CSN monitoring site used in this analysis was the Tacoma South L St. site AQS 530530029. 

A.4. Table of Acronyms and Abbreviations 

Table A.13. Acronyms and abbreviations and their meanings. 

Acronym or 

Abbreviation 

Meaning 

AIRPACT-3 Air Indicator Report for Public Awareness and Community Tracking - 3rd Version 

AQS Air Quality System 

bap Particle Light Absorption Coefficient 

bsp Particle Light Scattering Coefficient 

CAPC Clean Air Performance Commitment 

CES Contributing Emissions Score 

CFR Code of Federal Regulations 

CMAQ Community Multiscale Air Quality (a modeling system) 

CPF Conditional Probability Function 

CSN Chemical Speciation Network 

Dept Department 

EC Elemental Carbon 

Ecology Washington State Department of Ecology 

EI Emissions Inventory 

EPA Environmental Protection Agency 

FB Mean Fractional Bias 

FE Mean Fractional Error 

FRM Federal Reference Method 

ICS Incident Status Summary 

GPS Geographic Positioning System 

ID Idaho 

IMPROVE Interagency Monitoring of Protected Visual Environments 

km kilometer 

MB Mean Bias 

MCIP Meteorology-Chemistry Interface Processor 

ME Mean Error 

MM5 Mesoscale Model 5th Version 

MOU Memorandum of Understanding 

MOVES Motor Vehicle Emission Simulator 

NAA Nonattainment Area 

NAAQS National Ambient Air Quality Standards 

44
45

NEI National Emissions Inventory 

Neph Nephelometer 

NMB Normalized Mean Bias 

NME Normalized Mean Error 

NO3

- Nitrate 

Obs Observation 

OM Organic Mass 

OR Oregon 

PAH Polyaromatic Hydrocarbon 

PCA Principal Component Analysis 

PM Particulate Matter 

PM2.5 Particulate Matter less than 2.5 ug/m3

PM10 Particulate Matter less than 10 ug/m3

PMF Positive Matrix Factorization 

PNW Pacific Northwest 

PRR Pacific Rim Research 

PSCAA Puget Sound Clean Air Agency 

SIP State Implementation Plan 

SMARTFIRE Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation 

SMOKE Sparse Matrix Operator Kernel Emissions 

SO4

2- Sulfate 

STN Speciation Trends Network 

TBD To Be Determined 

TEOM Tapered Element Oscillating Microbalance 

US United States 

UW University of Washington 

WA Washington 

WRF Weather Research and Forecast 

WSU Washington State University