# Summary Technical Report in Support of

the PM 2.5 

# 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 UniversityTable of Contents:

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

2. CURRENT UNDERSTANDING OF PM 2.5 EXCEEDANCES IN TACOMA, WASHINGTON ...............4

2.1. M ONITORING R ECORD IN T ACOMA .............................................................................................................4

2.2. S PATIAL D ISTRIBUTIONS OF PM 2.5 IN THE N ONATTAINMENT A REA ............................................................6

2.3. M ETEOROLOGICAL C ONDITIONS R ELATED TO PM 2.5 E XCEEDANCES ..........................................................9

2.4. S OURCE A PPORTIONMENT .........................................................................................................................11

2.4.1. Overall Cont ributions.......................................................................................................... ................11

2.4.2. Seasonal Cont ributions......................................................................................................... ...............12

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

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

2.5. L EVOGLUCOSAN M ONITORING R ESULTS ...................................................................................................14

2.6. E MISSIONS D ATA AND A NALYSIS ..............................................................................................................15

3. AVAILABLE ATTAINMENT DE MONSTRATION MODELS ................................................................20

3.1. L INEAR R OLLBACK M ODELING .................................................................................................................21

3.1.1. Model Desc ription .............................................................................................................. .................21

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

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

3.2. 3-D IMENSIONAL G RID -B ASED P HOTOCHEMICAL M ODELING ....................................................................24

3.2.1. Model Desc ription .............................................................................................................. .................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 Resu lts 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 Demonstrati on with a Photochemica l Modeling System.......................33

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

A.1. B OXPLOTS AND M APPING OF M OBILE M ONITORING D ATA F ROM T ACOMA ..............................................34

A.2. M ODEL P ERFORMANCE S TATISTICS U SED .................................................................................................40

A.3. T ABULATED M ODEL P ERFORMANCE S TATISTICS ......................................................................................40

A.4. T ABLE OF A CRONYMS AND A BBREVIATIONS ............................................................................................44



### 21. 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 PM 2.5 nonattainment area. Tacoma was designated as a nonattainment area

for the 24-hour PM 2.5 standard on October 8, 2009. 1 The boundaries of the nonatta inment area are shown in Figure

1.1.

Figure 1.1. The City of Tacoma, PM 2.5 nonattainment area boundarie s, and surrounding areas.

A CAPC is an innovative approach to achieving air quality goals in an area exceedi ng or nearly exceeding the National Ambient Air Quality Standards (NAA QS). It is intended, ultimately, as an alternative to the traditional SIP process. EPA has demonstration projects underway in tw o areas; Tacoma, WA and Omaha, NE/Council Bluffs, IA. The goals of a CAPC are:

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

process,

 to create a more flexible/adaptable process for implementing controls, whil e 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 an d effectively by implementi ng targeted local control measures that complement national measures, establishi ng agreed-upon technical or accountability metrics before the process begins so that all parties are in agreemen t 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 ba sed on EPA’s Clean Data Policy. EP A’s Clean Data Policy allows EPA to suspend certain SIP elements if the vi olating monitor achieves the standard before the attainment plan is due. 2 In

1 http://www.epa.gov/pmdesignations/2006standard s/do cuments/2009-10-08/FR-11-13-2009.pdf

2 See 40 CFR 51.1004(c)



### 3this 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 inventor y for the nonattainment area, permanent and enforceable control measures that effectively return the area to attain ment 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 98 th percentile value for PM 2.5 in 2009 (47.6 μg/m 3 ), it is unlikely that the Tacoma area will achieve “clea n 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 Agreem ent finalized, signed; begin implementation, including check-ins at interim milestones and supplementation of efforts with backup m easures as necessary.

This report is being prepared as part of the second item on the timeline; Comp lete 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 ne eded on sources of emissions and the level of control needed for those sources to bring the Tacoma area into attainment with the PM 2.5 NAAQS (i.e. determine where there are gaps in our current knowledg e 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 PM 2.5 exceedances in Tacoma by reviewing the monitoring record , meteorological data, emissi ons inventories, and other related analyses of PM 2.5 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 lo cal officials charged with developing strategies to bring Tacoma back into attainment of the PM 2.5 NAAQS and may also be used as a technical reference during stakeholder discussions.

2. Current Understanding of PM 2.5 Exceedances in Ta coma, Washington

2.1. Monitoring Record in Tacoma

As of 1 July 2010, the following monitors have been opera ting at the Tacoma L Street s ite for at least three years.

Table 2.1. Ambient PM 2.5 and meteorological monitors operating at the T acoma L Street site for the last 3 years or longer.

Parameter Sampling frequency Sampling started

PM 2.5 FRM 1-in-3 (daily from 7 Jan 2010) 3 October 1999

PM 2.5 Neph Continuous 23 February 2001

PM 2.5 TEOM* Continuous 2 November 2000 Meteorological Continuous 10 November 2001

PM 2.5 speciation 1-in-6 11 January 2006 * Replaced with a FEM TEOM on 12 January 2010.

3 Clean data for PM 2.5 means that the 24-hour 98 th percentile 3 year average is le ss than or equal to 35 ug/m3.



### 4The current NAAQS for PM 2.5 are 35 μg/m 3 for the 24-hr standard and 15 μg/m 3 for the annual standard. The 2007-

2009 24-hr PM 2.5 design value 4 , as measured by the Tacoma L St Fede ral Reference Monitor (FRM), is 46 μg/m 3
and the annual design value is 9.8 μg/m 3 . The Tacoma area is in violation of the 24-hr PM 2.5 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 d eclines over the same period. Analysis of the FRM data

showed 16 monitored exceedances of the 24-hr PM 2.5 standard during the 2007 – 2009 period and all occurred during the winter months of November – February.

The PM 2.5 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 PM 2.5 samples for several metals, nitrate (NO 3 - ), sulfate (SO 4 2- ), elemental carbon (EC) and orga nic carbon (OC). The CS N’s main objectives are to evaluate the chemical makeup of PM 2.5 , understand the sources contributing to PM 2.5 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 2007–200 9, only 6 also had chemically speciated meas urements. Analysis of those 6 co-located chemically speciated measurem ents show that on average the chemical composition of PM 2.5 on exceedance days is 61% organic compounds, 9% elemental carbon, 6% nitr ate, and 4% sulfate, with the remaining mass being ammonium associate with nitrate and sulfate, soil com ponents, and other trace elemen ts. 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 Secti on 2.5 of this report.

Figure 2.1. PM 2.5 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

PM 2.5 standard, the 3-year average of the 98th percentile of 24-hour concentrations must not exceed 35 μg/m 3

. 5 Ogulei, D., WA Dept of Ecology, 2010, Sources of Fi ne Particles in the Wapato Hills-Puyallup River Valley PM 2.5 Nonattainment Area. Available at http://www.ecy.wa.gov/pubs/1002009.pdf

.



### 52.2. Spatial Distributions of PM 2.5 in the Nonattainment Area

Mobile Monitorin g

Mobile monitoring was conducted in Tacoma for particles measured by light scattering, light absorption coefficient, particle-bound polyaromatic hydrocarbo ns (PAH), as well as other trace pa rticle-bound chemical constituents. Monitoring was conducted during sel ected afternoon and evenin g periods from December 2008 to February 2009 and again from July to August 2009. Da ta was collected over 10 afternoons an d 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 (b sp , a surrogate for PM 2.5 ), particle light absorption coefficient (b ap , a surrogate for the elem ental carbon portion of PM 2.5 ), 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 2010 6

.

Table 2.2 shows the means of the observed values of b sp , b ap , and PAH. These data we re merged with relevant, contemporaneous fixed site data collected during this stu dy to adjust for short-term temporal variability within each sample period. In addition to an analysis of the means, anal ysis of the distribution of the observations (see boxplots and mapping of the data in the ap pendix) indicates that higher b sp values occur in residential areas of southern Tacoma in the winter during even ing hours. In contrast, higher b ap 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

Afternoon 78.9 21.9 b sp (10 -6 m -1 ) Evening 83.2 20.8

Afternoon 21.8 9.4 b ap (10 -6 m -1 ) Evening 24.7 10.4

Afternoon 18.4 4.9 PAH (ng/m 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 correl ated 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 acco unts 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 obser ved spatial variability. Factor 1 was high in b sp and moderately high

in b ap and is believed to be more associated with wood smoke. Factors 2, high in PAH, and Factor 3, high in b ap and moderately high in b sp , are both believed to be more associated with mobile sources. The f actor loadings are shown in Table 2.3, however, this analysis and its interpretatio n are still ongoing and should not be taken as final.

Table 2.3. Principal component factor loadings

Factor 1 Factor 2 Factor 3

b sp 0.92 0.18 0.36

PAH 0.17 0.96 0.23

b ap 0.42 0.29 0.86

This PCA helps to understand the underlying sources of sp atial variability and to clarify the location of the highly impacted wood smoke areas, characterized by relatively high levels of b sp , moderate levels of b ap , and low levels of

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

.



### 6PAHs. Figure 2.2 compares the relative contribution of th is 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

Tem porary PM 2.5 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 te mporary monitors were sited in primarily residential neighborhoods.

The PM 2.5 values were estimated from a factor derived from light scattering to Federal Reference Method PM 2.5 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.



### 7Figure 2.3. Map depicting the locations of the 4 temporar y 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 PM 2.5 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 concentr ations are occurring where the permanent fixed site is

located.

Figure 2.4. PM 2.5 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 98 th 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 56 th Street.



### 8PM 2.5 (Proximity to Exceeding the 24 Hour Standard) Sept 2006 - Feb 2007

40.5 ug/m 3 39.8 ug/m 3 43.7 ug/m 3 50.7 ug/m 3 57.1 ug/m 3

38.0 ug/m 3 45.7 ug/m 3

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0

1ConcentrationMicrograms per cubic meter

Federal Standard ( 35 u g /m3 )

USGS Edison Elem Lincoln HS South L St Pu y allu p Stewart Heights Tacoma Port

Figure 2.5. Nephelometer PM 2.5 98 th 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 S outh L Street site is a hotspot for PM 2.5 concentrations in the winter heating months. Although the Tacoma South L Street site is the highest, there is also a relatively even distribution of PM 2.5 over the Eastside, South End, and South Tacoma neighborhoods in the winter months. Additionally, PM 2.5 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 PM 2.5 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 PM 2.5 Exceedances

Figure 2.6 shows the distribution of one-hour PM 2.5 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 measuremen ts at the Tacoma South L Street site were taken with a sonic anemometer, which has good resolu tion even at low wind speeds. The r eader 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 transp ort from areas further away in the nonatta inment area. It is readily apparent from Figure 2.6 that high hourly PM 2.5 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 concentr ations rarely exceed 35 μg/m 3 when the wind speed is greater than two miles per hour or during the middle of the day. Hourly concentrations exceed 35 μg/m 3 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/m 3
when temperatures are above 30° F, it is almost a certainty that concentrations exceed 35 μg/m 3 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 PM 2.5 concentrations. In each panel the 24-hour PM 2.5 National Ambient Air Quality Standard (NAAQS) is shown by the horizontal dotted red line at 35 μg/m 3

.



### 9Fi gure 2.6. The distribution of one-hour PM 2.5 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 bou nds 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.



### 102.4. Source Apportionment

Ecology identified the main sources of PM 2.5 in the Wapato Hills-Puyallup River Valley area by using receptor modeling techniques to analyze existing PM 2.5 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 Ma trix Factorization (PMF; Paatero and Tapper, 1994 8 ; Paatero, 1997 9 ) and the Conditional Probability Function (CPF; Ashbaugh et al., 1985 10 ).

2.4.1. Overall Contributions

The results of this analysis indicate that the sources of PM 2.5 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/m 3 ) resolved in this analysis. The contributions have been averaged over the entire sampli ng period. Overall, wood smoke was the dominant source during the study period with an average contribution of 4.0 μg/m 3 , or about 45% of the total apportioned PM 2.5 mass. Additionally, PMF resolved contributions from secondary aerosol (25%) and motor vehicles (13%). The analysis using the PMF model was unable to attribute a la rge fraction of the aerosol, th at 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.

Resol ved Source Average Contribution (μg/m 3 ) Standard Deviation (μg/m 3 )

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 Fi ne Particles in the Wapato Hills-Puyallup River Valley PM 2.5 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 Ro bust Non-Negative Factor An alysis. 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.



### 11Aged 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 cale ndar 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 se ason. 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 fluctu ations in average contributions between two or more seasons. Most of the fugitive dust and fireworks contri butions 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 ve hicles, the increased number of cold-starts in the winter may have contributed to increased motor vehicle emissions in the winter.



### Average Concentration (  g/m 3 

### ) 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 PM 2.5 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 PM 2.5 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 PM 2.5 mass concentration exceeded 30 μg/m 3 . Wood smoke contributions also averaged

about 52% when the total PM 2.5 mass concentration exceeded 20 μg/m 3 , which is a healthy air goal established by the Washington State Department of Ecology.



### 12

## Source W

o od Sm

oke
Sec. Ni

tra

te

Gas o lin

e

Ve

hicle s Sec. S

u lfate

Ars en

ic

‐ r ich I n dus tr

ial

F

re

sh

S

ea

Sal

t Diesel Vehi c les Fugitive Dust

Oil Combusti o n/Ships Fireworks Aged Sea SaltPercentContribution(%)



### 0 10 20 30 40 50 60 70 80

PM 2.5 

### > or = 20  g/m 3



### PM 2.5 

### > or = 30  g/m 3



### PM 2.5 

### > or = 35  g/m 3



### PM 2.5 

### > or = 20 but < 35  g/m 3



### Error Bar = Range

Figure 2.8. Percentage contributions on days when the measured total PM 2.5 mass concentration was elevated. For completeness, four concentration ranges were evaluated: ≥ 20 μg/m 3 (circular symbols); ≥ 30 μg/m 3 (triangles); ≥ 35 μg/m 3 (squares); and 20 ≤ PM 2.5 <35 μg/m 3 (diamonds). Error bars represent the data range.

Generally, on elevated days (i.e., days with measured PM 2.5 mass ≥ 30 μg/m 3 ) where the resolved wood smoke contribution exceeded 50%, daily-average d 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 aeros ol (secondary nitrate plus secondary sulfate) also contributed large amounts to the measured total mass on most days when the PM 2.5 ambient concentration was or exceeded 30 μg/m 3

.

2.4.4. Conclusions

By analyzing the measured elemental species concentrati ons 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, second ary nitrate, secondary sulfate, oil combustion and ships, arsenic-rich industrial emissions, fresh ly-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 PM 2.5 mass which illustrates that wood smoke contributio ns were responsible for the majority of the

observed trend in total PM 2.5 mass concentrations. The source apportio nment results showed that wood smoke accounts for nearly one-half of the total measured PM 2.5 mass on most days. The wood smoke contributions were highest during the winter and on days when the measured daily PM 2.5 mass concentration exceeded 20 or 30 μg/m 3

.



### 13Wind direction and wind speed were found to be factors in the observed wood smoke c oncentrations. 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

PM 2.5 mass concentrations were attributable to secondary aerosol. Further analyses identified the resolved secondary aerosol as locally-generated aerosol from multip le 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/m 3 over time

0.0 0.5 1.0 1.5 2.0 2.511/2/0812/2/081/2/092/2/093/2/094/2/095/2/096/2/097/2/098/2/099/2/0910/2/09

DateLevoglucosan 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 ch aracterization of fine partic le 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 char acterization 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 PM 2.5 in Seattle, Washington, using positive matrix factorization." J Air Waste Manag Assoc 54(9): 1175-87.



### 14The 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, w ith Tacoma sites remaining higher than the Seattle Duwamish site. The mean, median, and 75 th and 95 th 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. Th is analysis is also in roug h 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

PM 2.5 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 EP A to Washington State with EPA’s proposed nonattainment designation for the Tacoma area 16 . Analysis and methodology can be found in their respective reports.

15 http://www.ecy.wa.gov/programs/air/Emi ssi onInventory/EmInvSummary_2005.xls

16 http://www.epa.gov/pmdesignations/2006s t andards/rec/letters/10_WA_EPAMOD.pdf



### 15Table 2.5. Pierce County Seasonal PM 2.5 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



### 16Boiler 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 inco rrect in the original Report a nd are corrected here. The data should be identical to the Ecology data b ecause Ecology was the source.

Fireplaces and Woodstoves

As the table above demonstrates, the Ecology winter fireplace and woodstove PM 2.5 estimate is more than two times the 9-Factor Report estimate. The di fferences originate from th e different surveys used to estimate the emissions inventory.

Ecology’s 2005 woodst ove/fireplace inventory was based on a 20 01 Washington Stat e Univers ity survey 17 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 improve d 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 PM 2.5 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 coul d be incorporated into future fireplace and woodstove so urce 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 Idah o, Oregon and Washington State . Social and Economic Sciences Research Center. Washingt on 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/r eports/Air_Emission_Inventory_2005.pdf



### 17of logs burned per year, frequency of fireplace and woodstove use, the tota l 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 distribu tion 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 esthe tic 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 nonattain ment 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 burni ng is prohibited in urban ar eas 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 PM 2.5 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. CO 2 and air toxics).



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

Source Contributions to Total PM 2.5

In the 9-Factor Report, the data sh ow that the highest sources for PM 2.5 emissions in Pierce Coun ty in the winter are fireplaces and woodstoves (41%). The ne xt 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 co ntributions allocate wood stoves and fireplaces as the primary source for PM 2.5 (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 PM 2.5

.

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.



### 19EPA 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 nonattainme nt. 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 ro utinely occur during events where PM 2.5 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 analyzi ng 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 emissi ons for the selected base y ear 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 PM 2.5 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 Taco ma are exclusively in the late fall through early spring seasons, the available seasonal emission data demonstrate th at woodstoves and fireplaces have the largest impact to attainment status. In contrast, mobile so urces are a large non-seasonal source of PM 2.5 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 PM 2.5 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 thes e models are used to explore multiple emission control scenarios in the air quality planning process. EPA Regi on 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.



### 203.1. Linear Rollback Modeling

3.1.1. Model Description

Linear rollback modeling of PM 2.5 concentrations is a relatively simple method to calculate future PM 2.5 based on

current observed concentrations with a linear scaling of the observed PM 2.5 data based on the ratio of future projected emissions to current emissions. The 1973 paper by de Nevers and Morris 19 still serves as an excellent reference to rollback modeling. The authors begin with the simple rollback equation:

(c i -b) future = ∑ [k ij e j ] future (3.1) (c i -b) base ∑ [k ij e j ] base

where c i is the pollutant concentration at point I, b is the background concentration (the assumed PM 2.5 concentration unrelated to sources in the nonattainment area), e j is the emission rate of category j, and k ij 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 ((c i ) future ) to be the attainment goal (c

max ). Additionally, one can adapt the equation to include emissions growth factors (gf) for each sub-population that emits PM 2.5 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*c

max -std+b(1-gf)) (3.2)

gf*c

max -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 PM 2.5 could be treated as part of the background. Hence, for the Tacoma area, Figure 3.1 could be us ed to estimate the uncertainty around assuming different growth projections for those primary PM 2.5 emissions sectors not targeted for controls (e.g., if woodsmoke is targeted fo r controls and primary PM 2.5 from mobile sources is not). To account for any non-linearity in secondary production of PM 2.5 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 becaus e rollback does not consider secondary aerosols in the attainment calculation, this lik ely 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 PM 2.5 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 add itional 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.



### 21Figure 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 va lue ratio (replotted Figure 2 from de Nevers and Morris).

Rollback assumes that the sources of th e contributing emissions are well defined, hence the transport of emissions from outside the area will increase the uncertainty of the re sult 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 PM 2.5 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 a ssociated with frequent 1-ho ur average co ncentrations greater than 35 μ g/m 3 , 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 availa ble 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 analys is 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 PM 2.5 contributes an increasing portion of the total PM 2.5 mass, the rollback methodology can be expected to have in increasing uncertainty.

Although each of the forms of the rollb ack equation from de Nevers and Morri s 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 th e 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 mo re 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 ar eas that were brought back into attainment out of a total of ten areas. It



### 22should also be noted that none of the rollback examples listed in Table 3.1 are for PM 2.5 , and the authors were unable to find an example of rollbacks’ use in a PM 2.5 attainment demonstration. Th is is likely because most areas

in nonattainment for PM 2.5 are dominated by secondary aerosols, th e formation of which involves complex chemistry requiring the use of a photochemical model to pred ict. 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 PM 10 , and it has not yet

reached attainment of the standard. In Fresno County, secondary production was considered in developing interpollutant offset ratios 20 for new sources in the PM 10 nonattainment area. The background PM 10 concentration exceeded the standard and Fresno County was forced to deve lop 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 categor ies 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 NO 2 1972 Yes Chugiak-Eagle River, AK PM 10 1987 Yes San Joaquin Valley, CA PM 10 1997 No Maricopa Co, AZ PM 10 1999 TBD Clark county, NV PM 10 2000 Yes Douglas Co, AZ SO 2 2001 Yes (facility dismantled) South Coast Air Basin, CA PM 10 2003 Yes Denver-Boulder, CO 21 CO 2003 Yes Pocatello, ID PM 10 2006 Yes Fresno County, CA** PM 10 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 ac tual 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 PM 2.5 in the inventory. h) Estimate the amounts of primary and secondary PM 2.5 impacting the FRM monitor during times of high PM 2.5

20 www.energy.ca.gov/sitingcases/bu llard /documents/applicant/DA-respon se-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.



### 23concentration. i) Determine the amount of total PM 2.5 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

PM 2.5 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) model 22 employed with the Weathe r 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 ch emical transport model for si mulating 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 syst em to predict future conditions. In th e Tacoma area, the CMAQ model currently (July 2010) runs on a daily basis via the AIRPAC T air quality forecast system.

To use the CMAQ model to make an attainment demonstra tion, 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. A ttainment of the air quality standard is demonstrated if the ratio of future

modeled PM 2.5 to base case modeled PM 2.5 , multiplied by the observed PM 2.5 , 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 forecas ts for the Pacific Northwest using the CMAQ modeling system. The WSU forecast system , referred to as AIRPACT-3 24 , 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 be ing 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 PM 2.5 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/



### 24Figure 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 Weathe r Research and Forecast (WRF) meteorol ogical model, the Sparse Matrix Operator Kernel Emissions (SMOKE) processing syst em, and the Community Multi-Scale Air Quality Model (CMAQ). WRF output fields are obtai ned from the University of Washingt on 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 ge ographic 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 surf ace to the tropopause; highways and a few major cities are shown. Five large urban areas are included in the do main: Seattle-Tacoma, Portland , Vancouver (BC, Canada), Boise, and Salt Lake City. The SMOKE model is used to process anthropogenic and bi ogenic emission categories

for each forecast simulation. Area an d 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 da ta and are adjusted for WR F-forecast temperature. Anthropogenic emissions over the provinces of British Colu mbia 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 BlueSky 26 system, which utilized ICS-209 re ports 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 fo r accessing SMARTFIRE w ildfire emissions.

25 http://www.atmos.washington.edu/mm5rt/

26 http://www.airfire.org/bluesky



### 253.2.3. 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 scen arios. Both EPA Region 10 and WSU have performed independent analysis of AIRPACT-3 model performance for PM 2.5 . However, it should be noted that this is an analysis of AIRPACT-3 model perfor mance as a forecast system. Typically, when the CMAQ model is run for historical episodes in an attainment demonstration ef fort, 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 area 27

.

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) statis tics 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 define d 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 perfor mance 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, 20 06. 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 for ecast model output at UW. In general it was found that, under conditions of elevated PM 2.5 , the AIRPACT-3 system over-predicts PM 2.5 concentrations in major urban areas of the Pacific Northwest and under-predicts PM 2.5 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 PM 2.5 was analyzed for the winter months of October through March, for the years 2003 – 2006, and for the major urban centers of Po rtland, 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 PM 2.5 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 PM 2.5 and a portion of the course mode aerosol distribution can be smaller

than PM 2.5 . The PMx utility accounts for this by rem oving accumulation mode mass in CMAQ beyond PM 2.5 and including the portion of course mode mass below PM 2.5 . 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 th e 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 PM 2.5 FB and FE are summarized in Figures 3.3 and 3.4;

27 http://www.deq.state.or.us/aq/planni n g/docs/wsuPortlan d_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 PM 2.5 concentrations and their impact on model perfor mance evaluation in the Lower Fraser Valley, Atmos. Environ., 40, 4973– 4985.



### 26tables showing the full suite of model performance statis tics are located in the Appendi x. Model performance is discussed more fully below.

Unlike other pollutants listed under the NAAQS, PM 2.5 is an accumulation of multiple chemical species. The major categories of chemical species in PM 2.5 are sulfates (SO 4 ), nitrates (NO 3 ), organic mass (OM), elemental carbon (EC), and soil elements (SOIL). Each of these chemi cal species has its own emi ssions, chemical formation, transformation, and removal processes in the atmosphere. As such, it is important to investigate not only model performance for bulk PM 2.5 , but also model performance fo r each chemical component of PM 2.5

.

In recognition of this, a subset of PM 2.5 monitoring sites in the Pacifi c Northwest have co-located PM 2.5 Chemical Speciation Samplers. These samplers allow separation of the bulk PM 2.5 into the major categories of chemical species. Because the PM 2.5 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 PM 2.5 in Tacoma for this EPA analysis. For the Seattle and Portland monito ring 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

PM 2.5 on days that exceeded the NAAQS standard, so FB an d FE for these species are pl otted along with that for

bulk PM 2.5 below. Tables showing the full suite of model pe rformance statistics, includi ng 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 forecas ting system was evaluated for PM 2.5 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 PM 2.5 were within the accepted model pe rformance criteria for the 2004 analyses.

Table 3.2. Summary of AIRPACT-3 forecast performance statistics* for 24-hr PM 2.5 by the EPA-AQS and IMPROVE measurement networks for August – November 2004. Monitoring

Network N Observed

Mean (μg/m 3 ) Model

Mean (μg/m 3 ) MB (μg/m 3 ) ME (μg/m 3 ) 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 perf ormance 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 PM 2.5 forecast system for the Pacific Northwest, J. Geophys. Res., 113, D14305,

doi:10.1029/2007JD009554.



### 27analysis 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 PM 2.5 mass, where samples were grouped into four bins (PM 2.5 mass measured from 0-10 μg/m 3 , 10-20 μg/m 3 , 20-30 μg/m 3 , and >30 μg/m 3 ) 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 PM 2.5 . In order to show the model performance for the OM and EC

fractions of PM 2.5 over the same total PM 2.5 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 Fi gure 3.5 closest to the y ax is represent the average OM concentration in Seattle, Portland, and Tacoma when total PM 2.5 was between 0-10 μg/m 3 . Tables showing the full suite of model performance statistics are located in the Appe ndix. 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 5CMAQ Mean Fractional Bias, %

Average PM 2.5 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 PM 2.5 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 PM 2.5 mass from 0-10 μg/m 3 , 10-20 μg/m 3 , 2 0-30 μg/m 3 , and >30 μg/m 3

.

0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 5CMAQ Mean Fractional Error, %

Average PM 2.5 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 PM 2.5 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 PM 2.5 mass from 0-10 μg/m 3 , 10-20 μg/m 3 , 2 0-30 μg/m 3 , and >30 μg/m 3

.



### 29-100 -80 -60 -40 -20

0 20 40 60 80 100

0 5 10 15 20 25 30CMAQ 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 PM 2.5 mass from 0-10 μg/m 3 , 10-20 μg/m 3 , 2 0-30 μg/m 3 , and >30 μg/m 3

.

0 10 20 30 40 50 60 70 80 90 100

0 5 10 15 20 25 30CMAQ 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 PM 2.5 mass from 0-10 μg/m 3 , 10-20 μg/m 3 , 2 0-30 μg/m 3 , and >30 μg/m 3

.



### 30-100 -80 -60 -40 -20

0 20 40 60 80 100

012345CMAQ 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 PM 2.5 mass from 0-10 μg/m 3 , 10-20 μg/m 3 , 2 0-30 μg/m 3 , and >30 μg/m 3

.

0 10 20 30 40 50 60 70 80 90 100

012345CMAQ 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 Po rtland, 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 PM 2.5 mass from 0-10 μg/m 3 , 10-20 μg/m 3 , 2 0-30 μg/m 3 , and >30 μg/m 3

.



### 313.2.3.3. Model Performance Results and Discussion

Figures 3.3 – 3.8 depict FB and FE for PM 2.5 , OM and EC, respectively. Model performance data for each city has

been binned in to four PM 2.5 mass ranges, that from 0-10 μg/m 3 , 10-20 μg/m 3 , 20-30 μg/m 3 , and >30 μg/m 3 . For example, the EC data plotted that lies closest to the y ax is on the FB and FE plots is the average EC concentration for each city when measured PM 2.5 was between 0-10 μg/m 3 . Similar FB and FE statistic s 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 PM 2.5 fall within the accepted goals or criteria for all four

PM 2.5 mass bins. As can be seen in Figure 3.3, in most cases AIRPACT-3 overestimates PM 2.5 in the major cities of the Pacific Northwest (PNW), with th e exception of EPA’s analysis of PM 2.5 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 reduc tion 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 PM 2.5 FB and FE fell outside of the model performance

criteria for Portland when PM 2.5 was less than 10 μg/m 3 and for Seattle when PM 2.5 was less than 20 μg/m 3

. However, for PM 2.5 greater than 20 μg/m 3 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

PM 2.5 fell within the goals or criteria for all three cities and for all PM 2.5 mass ranges. For the sulfate portion of

PM 2.5 , the three cities were within model performance goals or criteria under most conditions except in Portland

when observed PM 2.5 was between 20-30 μg/m 3

.

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

criteria for observed PM 2.5 concentrations in Tacoma, for observed PM 2.5 concentrations above 20 μg/m 3 in Seattle,

and for observed PM 2.5 concentrations in Portland except for sulfate. Options for improving model performance are discussed in the following section.

3.2.4. 

### Options for Improving Model Performanc e

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 an d emissions inputs to match the historical record. By its ve ry 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 alloca tion and emissions factors.



### 323.2.5. Pros and Cons of Using Photochemical Models for Attainment Demonstration in Tacoma

The advantages of using a photochemical modeling system as a PM 2.5 attainment demonstration tool are that it:

 accounts for the chemical tr ansformations of pollutants after they are emitted and can simulate the secondary production of PM 2.5 in the atmosphere from gaseous precursors;

 accounts for the effects of weathe r and topography on pollutants;

 appropriately places emissi ons geographically and temporally rela tive to the monitor and simulates the transport, diffusion and dilutio n of emissions and their rela tive impact on the monitor;

 provides an estimate of PM 2.5 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 thro ugh 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 PM 2.5 sensitivity to a variety of control scenarios to determine which emissions

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

 can account for expected emissions growth or other ch anges outside the nonattainme nt area and the impact of

that on PM 2.5 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 PM 2.5 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 capa bility, 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 Attainmen t Demonstration with a Phot ochemical Modeling System

Detailed EPA guidance on using photochemical modeling syst ems 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, PM 2.5 , 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 he lp 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



### 33a) 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 PM 2.5 monitoring record is reviewed for the Tacoma L St . site to select a number of historical episodes

where the PM 2.5 24-hr NAAQS was exceeded, that are representative of the range of meteorological conditions where exceedances occur, and that have suffic ient meteorological and chemically speciated PM 2.5 data to be able to adequately conduct a model performance evaluation. Ideally, episodes will be selected where 24-hr

PM 2.5 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 ac tual 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 th e 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 epis odes chosen. The meteorological model performance will need to be evaluated against the historical reco rd and should meet or exceed accept ed 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 mete orological 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 PM 2.5 at the Tacoma L St. monitoring site will need to be evaluated agains t accepted model performance standards similar to the performance analysis of the AIRPACT-3 system discu ssed above. Model performance should meet or exceed accepted model performance standards. Methods for improving model perf ormance, 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 m odel 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 da ys that exceeded the NAAQS in the hi storical scenarios to determine if attainment is achievable under this future year emissi ons 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 concentra tion 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 3 rd

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



### 34Box plots

bsp bap PAH

-50

0 50 100 150 200 250

Figure A.1. Summer Afternoons

Box plots

PAH bap bsp
-50

0 50 100 150 200 250

Figure A.2. Summer Evenings



### 35Box plots

PAH bap bsp

-50

0 50 100 150 200 250

Figure A.3. Winter Afternoons

Box plots

PAH bap bsp -50

0 50 100 150 200 250

Figure A.4. Winter Evenings



### 36Figure A.5. Spatial distribution of temporally adjusted part icle light scattering coeffici ent. 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.



### 37Figure A.6. Spatial distribution of temporally adjusted particle light absorption coefficient. Measurements were taken in the late afternoon and evening on selected wint er days between December, 2 008 and February, 2009 and on selected summer days between July and August, 2009.



### 38Figure A.7. Spatial distribution of temporally adjusted particle-bound PAHs. Measuremen ts were taken in the late afternoon and evening on se lected winter days between December, 20 08 and February, 2009 and on selected summer days between July and August, 2009.



### 39A.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. PM 2.5 Model Performance Statistics Statistic Acronym Statistic Units Statistic Name Statistic Formula

MB μg/m 3 Mean Bias (1/N) Σ (Model – Obs)

ME μg/m 3 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 PM 2.5 concentrations above a few μg/m 3 , model performance goals are FB ≤ ±30% and FE ≤ 50% and model performance criteria are FB ≤ ±60% and FE ≤ 75%.

For observed PM 2.5 concentrations below a few μg/m 3 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 be tween model and observed concentrations.

Mean Error (ME): Mean error is similar to mean bias except the ab solute 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 simi lar 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 mode l 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 differenc e (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 PM 2.5 in Portland, Tacoma, and Seattle during the winter months of Oct – Mar, 2003 – 2006.
Monitoring

Site** Data Segregation, N Observed

PM 2.5 Model

PM 2.5 MB (μg/m 3 ) ME (μg/m 3 ) FB (%) FE (%) NMB (%) NME (%)



### 40monitored 24-hr average

PM 2.5 Mean (μg/m 3 ) Mean (μg/m 3 )

Portland, OR <10 μg/m 3 138 6.2 10.4 4.2 4.9 37 52 67 79

10-20 μg/m 3 80 14.3 20.8 6.5 8.3 29 43 46 59

20-30 μg/m 3 17 23.4 23.0 -0.4 5.3 -8 26 -2 23

>30 μg/m 3 4 38.1 45.9 7.9 7.9 19 19 21 21

Tacoma, WA <10 μg/m 3 117 5.5 7.9 2.4 3.3 25 42 43 60

10-20 μg/m 3 52 14.1 17.3 3.3 6.5 9 39 23 46

20-30 μg/m 3 27 25.4 27.9 2.5 8.1 5 31 10 32

>30 μg/m 3 30 39.1 31.3 -7.8 12.4 -28 39 -20 32

Seattle, WA <10 μg/m 3 426 5.6 11.7 6.1 6.5 54 66 109 117

10-20 μg/m 3 186 13.6 24.2 10.6 11.5 48 57 78 84

20-30 μg/m 3 36 23.5 30.3 6.8 9.2 21 33 29 39

>30 μg/m 3 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 PM 2.5 FRM monitoring sites were used in this anal ysis; 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 PM 2.5 taken in Portland and Seattle during the winter months of Oct – Mar, 2003 - 2005.
Monitoring

Site** Data Segregation,

monitored 24-hr average

PM 2.5 N Observed

EC

Mean (μg/m 3 ) Model

EC

Mean (μg/m 3 ) MB (μg/m 3 ) ME (μg/m 3 ) FB (%) FE (%) NMB (%) NME (%)

Portland, OR <10 μg/m 3 95 0.7 1.0 0.3 0.5 24 50 44 65

10-20 μg/m 3 54 1.2 1.9 0.6 0.8 34 47 52 64

20-30 μg/m 3 14 1.9 2.0 0.2 0.7 0 40 9 40

>30 μg/m 3 4 2.3 3.9 1.6 1.6 54 54 71 71

Seattle, WA <10 μg/m 3 109 0.5 0.9 0.4 0.5 47 61 74 89

10-20 μg/m 3 46 1.2 2.0 0.8 1.0 45 64 67 85

20-30 μg/m 3 8 1.7 2.4 0.7 1.0 30 50 40 58

>30 μg/m 3 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 PM 2.5 CSN monitoring sites were used in this analysis; Portland Roselawn site AQS 410510246,

Seattle Beacon Hill s ite AQS 530330080.

Table A.4. EPA’s model performance statistics* for organic mass (OM) from 24-hr samples of PM 2.5 taken in Portland and Seattle during the winter months of Oct – Mar, 2003 - 2005.
Monitoring

Site** Data Segregation,

monitored 24-hr average

PM 2.5 N Observed

OM

Mean (μg/m 3 ) Model

OM

Mean (μg/m 3 ) MB (μg/m 3 ) ME (μg/m 3 ) FB (%) FE (%) NMB (%) NME (%)

Portland, OR <10 μg/m 3 95 4.0 8.4 4.4 5.0 65 76 108 123

10-20 μg/m 3 54 9.4 15.7 6.3 7.6 41 54 67 81

20-30 μg/m 3 14 15.1 16.2 1.1 4.7 0 34 7 31

>30 μg/m 3 4 24.1 31.6 7.5 7.5 28 28 31 31

Seattle, WA <10 μg/m 3 109 2.9 8.2 5.3 5.4 90 93 185 188



### 4110-20 μg/m 3 46 7.6 18.9 11.3 11.7 76 84 149 154

20-30 μg/m 3 8 12.4 22.6 10.3 11.1 52 60 83 89

>30 μg/m 3 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 PM 2.5 CSN monitoring sites were used in this analysis; Portland Roselawn site AQS 410510246,

Seattle Beacon Hill s ite AQS 530330080.

Table A.5. EPA’s model performance statistics* for sulfate (SO 4 ) from 24-hr samples of PM 2.5 taken in Portland and Seattle during the winter months of Oct – Mar, 2003 - 2005.
Monitoring

Site** Data Segregation,

monitored 24-hr average

PM 2.5 N Observed

SO 4 Mean (μg/m 3 ) Model

SO 4 Mean (μg/m 3 ) MB (μg/m 3 ) ME (μg/m 3 ) FB (%) FE (%) NMB (%) NME (%)

Portland, OR <10 μg/m 3 95 0.8 0.7 -0.2 0.3 -20 37 -20 35

10-20 μg/m 3 53 1.8 1.1 -0.8 0.8 -50 53 -41 44

20-30 μg/m 3 14 2.5 1.2 -1.3 1.3 -71 71 -53 53

>30 μg/m 3 4 3.8 1.7 -2.1 2.1 -65 65 -55 55

Seattle, WA <10 μg/m 3 109 0.6 0.9 0.2 0.3 33 46 39 54

10-20 μg/m 3 45 1.4 1.5 0.2 0.6 7 37 12 41

20-30 μg/m 3 8 3.1 1.8 -1.3 1.7 -45 63 -41 53

>30 μg/m 3 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 PM 2.5 CSN monitoring sites were used in this analysis; Portland Roselawn site AQS 410510246,

Seattle Beacon Hill s ite AQS 530330080.

Table A.6. EPA’s model performance statistics* for nitrate (NO 3 ) from 24-hr samples of PM 2.5 taken in Portland and Seattle during the winter months of Oct – Mar, 2003 - 2005.
Monitoring

Site** Data Segregation,

monitored 24-hr average

PM 2.5 N Observed

NO 3 Mean (μg/m 3 ) Model

NO 3 Mean (μg/m 3 ) MB (μg/m 3 ) ME (μg/m 3 ) FB (%) FE (%) NMB (%) NME (%)

Portland, OR <10 μg/m 3 95 0.8 0.5 -0.3 0.4 -54 66 -36 51

10-20 μg/m 3 53 1.9 1.7 -0.2 0.7 -23 45 -11 38

20-30 μg/m 3 14 2.5 1.4 -1.1 1.2 -58 61 -44 46

>30 μg/m 3 4 5.9 6.6 0.7 3.8 14 56 12 64

Seattle, WA <10 μg/m 3 109 0.5 0.7 0.2 0.3 1 49 30 64

10-20 μg/m 3 45 1.3 2.2 0.8 1.1 32 55 65 81

20-30 μg/m 3 8 2.3 3.7 1.4 1.6 29 40 63 69

>30 μg/m 3 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 PM 2.5 CSN monitoring sites were used in this analysis; Portland Roselawn site AQS 410510246,

Seattle Beacon Hill s ite AQS 530330080.

Table A.8. WSU/Ecology’s model performance statistics* for 24-hr average PM 2.5 in Tacoma during the winter months of Oct – Mar, 2006 – 2010.
Monitoring

Site** Data Segregation,

monitored 24-hr average N Observed

PM 2.5 Mean (μg/m 3 ) Model

PM 2.5 Mean (μg/m 3 ) MB (μg/m 3 ) ME (μg/m 3 ) FB (%) FE (%) NMB (%) NME (%)



### 42PM 2.5

Tacoma, WA <10 μg/m 3 167 5.2 10.0 4.9 5.4 52 62 95 105

10-20 μg/m 3 78 14.0 25.6 11.6 13.0 48 60 83 93

20-30 μg/m 3 20 23.7 41.1 17.4 20.2 42 58 73 85

>30 μg/m 3 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 PM 2.5 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 PM 2.5 taken in Tacoma during the winter months of Oct – Mar, 2006 – 2010.
Monitoring

Site** Data Segregation,

monitored 24-hr average

PM 2.5 N Observed

EC

Mean (μg/m 3 ) Model

EC

Mean (μg/m 3 ) MB (μg/m 3 ) ME (μg/m 3 ) FB (%) FE (%) NMB (%) NME (%)

Tacoma, WA <10 μg/m 3 65 0.5 0.8 0.2 0.5 26 69 45 87

10-20 μg/m 3 22 1.4 2.2 0.8 1.0 33 48 57 73

20-30 μg/m 3 8 2.6 2.2 -0.4 0.6 -23 29 -16 23

>30 μg/m 3 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 PM 2.5 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 PM 2.5 taken in Tacoma during the winter months of Oct – Mar, 2006 – 2010.
Monitoring

Site** Data Segregation,

monitored 24-hr average

PM 2.5 N Observed

OM

Mean (μg/m 3 ) Model

OM

Mean (μg/m 3 ) MB (μg/m 3 ) ME (μg/m 3 ) FB (%) FE (%) NMB (%) NME (%)

Tacoma, WA <10 μg/m 3 65 3.5 4.8 1.2 2.3 16 52 35 66

10-20 μg/m 3 22 8.5 12.9 4.4 5.4 31 43 52 64

20-30 μg/m 3 8 15.2 13.1 -2.1 6.0 -22 47 -14 39

>30 μg/m 3 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 PM 2.5 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 (SO 4 ) from 24-hr samples of PM 2.5 taken in Tacoma during the winter months of Oct – Mar, 2006 – 2010.
Monitoring

Site** Data Segregation,

monitored 24-hr average

PM 2.5 N Observed

SO 4 Mean (μg/m 3 ) Model

SO 4 Mean (μg/m 3 ) MB (μg/m 3 ) ME (μg/m 3 ) FB (%) FE (%) NMB (%) NME (%)

Tacoma, WA <10 μg/m 3 65 0.5 0.7 0.2 0.4 31 58 47 82

10-20 μg/m 3 22 1.0 1.5 0.5 0.6 33 49 55 68

20-30 μg/m 3 8 1.1 1.1 0.0 0.3 -2 30 0 27

>30 μg/m 3 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 PM 2.5 CSN monitoring site used in this analysis was the Tacoma South L St. site AQS 530530029.



### 43Table A.12. WSU/Ecology’s model performance statistics* for nitrate (NO 3 ) from 24-hr samples of PM 2.5 taken in Tacoma during the winter months of Oct – Mar, 2006 – 2010.
Monitoring

Site** Data Segregation,

monitored 24-hr average

PM 2.5 N Observed

NO 3 Mean (μg/m 3 ) Model

NO 3 Mean (μg/m 3 ) MB (μg/m 3 ) ME (μg/m 3 ) FB (%) FE (%) NMB (%) NME (%)

Tacoma, WA <10 μg/m 3 65 0.5 0.4 -0.1 0.3 -70 93 -18 70

10-20 μg/m 3 22 1.3 2.1 0.8 1.2 25 72 63 97

20-30 μg/m 3 8 1.6 1.9 0.3 1.2 -8 74 17 79

>30 μg/m 3 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 PM 2.5 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

b ap Particle Light Absorption Coefficient

b sp 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

NO 3 - Nitrate

Obs Observation

OM Organic Mass

OR Oregon

PAH Polyaromatic Hydrocarbon

PCA Principal Component Analysis

PM Particulate Matter

PM 2.5 Particulate Matter less than 2.5 ug/m 3

PM 10 Particulate Matter less than 10 ug/m 3

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

SO 4 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