Sources of Fine Particles in the 

Wapato Hills-Puyallup River Valley 

PM2.5 Nonattainment Area 

April 2010 

Publication No. 10-02-009 
Publication and Contact Information 

This report is available on the Department of Ecology’s website at 

www.ecy.wa.gov/biblio/1002009.html . 

For more information contact: 

Air Quality Program 

P.O. Box 47600 

Olympia, WA 98504-7600 

Phone: 360-407-6821 

Washington State Department of Ecology - www.ecy.wa.gov 

o Headquarters, Olympia 360-407-6000 

o Northwest Regional Office, Bellevue 425-649-7000 

o Southwest Regional Office, Olympia 360-407-6300 

o Central Regional Office, Yakima 509-575-2490 

o Eastern Regional Office, Spokane 509-329-3400 

If you need this publication in another format, please contact the Air Quality 

Program at (360) 407-6800. If you have a hearing loss, call 711 for Washington 

Relay Service. If you have a speech disability, call 877-833-6341. 
Sources of Fine Particles in the Wapato 

Hills-Puyallup River Valley PM2.5

Nonattainment Area 

by 

David Ogulei, PhD, PE 

Air Quality Program 

Washington State Department of Ecology 

Olympia, Washington 
This page is purposely left blank
i 

Table of Contents 

Page

List of Figures and Tables................................................................................................... ii

Figures........................................................................................................................... ii

Tables ........................................................................................................................... iii

Acknowledgements ............................................................................................................ iv

Executive Summary .............................................................................................................v

1.0 INTRODUCTION .............................................................................................1

2.0 EXPERIMENTAL METHODS.........................................................................3

2.1 Sampling Site and Data Description ..................................................................3

2.2 Model Description .............................................................................................8

2.2.1 Positive Matrix Factorization (PMF) .......................................................8

2.2.2 The Conditional Probability Function (CPF) .........................................11

3.0 SOURCE APPORTIONMENT RESULTS .....................................................12

3.1 Criteria for Source Identification .....................................................................12

3.2 Overall Contributions.......................................................................................13

3.3 Seasonal Contributions ....................................................................................13

3.4 Weekday and Weekend Contributions .............................................................16

3.5 Annual Contributions .......................................................................................17

3.6 Contributions on Days When the Measured Total Mass Concentration Was 

Elevated............................................................................................................17

4.0 DETAILED RESULTS AND DISCUSSION .................................................20

4.1 Wood Smoke Contributions .............................................................................20

4.2 Motor Vehicles (Gasoline and Diesel) .............................................................24

4.3 Secondary Aerosol (Secondary Nitrate and Secondary Sulfate) .....................26

4.4 Industrial Emissions .........................................................................................29

4.5 Fugitive Dust ....................................................................................................32

4.6 Oil Combustion and Ships ...............................................................................33

4.7 Other Sources ...................................................................................................35

5.0 CONCLUSION ................................................................................................38

6.0 REFERENCES ................................................................................................40

Acronyms and Abbreviations ............................................................................................42
ii 

List of Figures and Tables 

 Page

Figures 

Figure 1.1. Wapato Hills-Puyallup River Valley nonattainment area. . .............................2 

Figure 2.1. Location of the sampling site with respect to major particle sources. ..............4 

Figure 2.2. The area's wind profile during the study period. ..............................................5 

Figure 2.3. Plots of measured uncertainties against measured concentrations for elemental 

carbon (EC) and organic carbon (OC). ............................................................9 

Figure 2.4. Overall wind profile. ......................................................................................11 

Figure 3.1. Percentage contributions, averaged over the entire modeling period. ............14 

Figure 3.2. Average contributions by season. ...................................................................15 

Figure 3.3. Heating vs. non-heating contributions. ...........................................................15 

Figure 3.4. Weekday/weekend variation. .........................................................................16 

Figure 3.5. Percentage contributions on days when the measured total PM2.5 mass 

concentration was elevated. ............................................................................18 

Figure 4.1. Wood smoke source profile. ...........................................................................21 

Figure 4.2. CPF for wood smoke. .....................................................................................21 

Figure 4.3. Number of homes using wood as a primary heating source per square mile, in 

the Tacoma area ..............................................................................................22 

Figure 4.4. Wood smoke, BC, OC, and PM2.5 time series. ...............................................23 

Figure 4.5(a). PMF profile for gasoline-fired vehicles. ....................................................24 

Figure 4.5(b). PMF profile for diesel vehicles. .................................................................25 

Figure 4.6(a). CPF for gasoline vehicles. .........................................................................26 

Figure 4.6(b). CPF for diesel vehicles. .............................................................................26 

Figure 4.7(a). PMF source profile for secondary nitrate. .................................................27 
iii 

Figure 4.7(b). PMF source profile for secondary sulfate. .................................................27 

Figure 4.8(a). CPF for secondary nitrate. .........................................................................28 

Figure 4.8(b). CPF for secondary sulfate. .........................................................................28 

Figure 4.9. Seasonal profile for secondary aerosol and PM2.5 mass. ................................29 

Figure 4.10. PMF profile for industrial emissions. ...........................................................30 

Figure 4.11. CPF for industrial emissions. .......................................................................30 

Figure 4.12. Predicted extent of arsenic contamination due to the ASARCO melter ......31 

Figure 4.13. PMF profile for fugitive dust. .......................................................................33 

Figure 4.14. CPF for fugitive dust. ...................................................................................33 

Figure 4.15. PMF profile for oil combustion/ships...........................................................34 

Figure 4.16. CPF for oil combustion and ships. ...............................................................34 

Figure 4.17. PMF profiles for fireworks, freshly-emitted sea salt, and aged sea salt. ......36 

Figure 4.18. CPFs for fireworks, freshly emitted sea salt, and aged sea salt. ...................37 

Figure 4.19. Time series for fireworks. .............................................................................37 

Tables 

Table 2.1. Total PM2.5 mass concentrations and average wind direction on days when the 

FRM or STN samplers reported ambient mass concentrations exceeding 

30μg/m3

 ..............................................................................................................6 

Table 2.2. Summary of data used in this study. ..................................................................7 

Table 3.1. Average contributions resolved by the PMF model. .......................................13 

Table 3.2. Source contributions averaged over individual years. .....................................17 

Table 3.3. Contributions on days when the observed PM2.5 mass concentration was 

greater than or equal to 30 μg/m3. ....................................................................19 
iv 

Acknowledgements 

This report represents a collection of significant contributions from a number of individuals 

within the Department of Ecology and Puget Sound Clean Air Agency. The author of the report 

would like to especially thank the following people for their contribution to this study: 

 Ranil Dhammapala, Department of Ecology Air Quality Program 

 Katherine Himes, Puget Sound Clean Air Agency 

 Doug Schneider, Department of Ecology Air Quality Program 

 Sarah Rees, Department of Ecology Air Quality Program 

 Jeff Johnston, Department of Ecology Air Quality Program 
v 

Executive Summary 

Purpose 

This report provides information to policy makers about the sources of fine particle air pollution 

in Washington’s Wapato Hills-Puyallup River Valley nonattainment area. 

Background information 

States monitor air quality in different areas to find out how much pollution is in the air. When an 

area violates a federal health-based air quality standard, the federal Clean Air Act requires the 

Environmental Protection Agency (EPA) to classify the area’s air quality. EPA can make one of 

three designations for an area based on a state's recommendation: 

 Attainment (meeting a standard) 

 Nonattainment (failing to meet a standard) 

 Unclassifiable (not enough information to designate) 

Fine particle pollution in the Wapato Hills-Puyallup River Valley area 

On November 13, 2009, EPA formally designated part of Pierce County in Washington (the 

Wapato Hills-Puyallup River Valley area) as nonattainment. The designation took effect 

December 14, 2009. This area is nonattainment for the 2006 National Ambient Air Quality 

Standard (NAAQS) for 24-hour PM2.5. PM2.5 is fine particles that are less than 2.5 micrometers 

in size. This pollutant can have many serious health effects. 

Effects of a nonattainment designation 

In addition to the health effects of air pollution, nonattainment designations can have both 

environmental and economic consequences for communities in and around the nonattainment 

area. 

When an area is designated nonattainment, the state must develop a State Implementation Plan 

(SIP) and submit it to EPA within three years. The SIP is a plan for bringing the area back into 

attainment as quickly as possible. The SIP must include an attainment demonstration showing 

what actions the state will take to control air pollution, and how these actions will lead to 

attainment. 

 

Analysis of PM2.5 sources 

To determine how to control a pollutant, the Washington Department of Ecology (Ecology) 

needs to understand the pollutant’s sources. Ecology identified the main sources of PM2.5 in the 

Wapato Hills-Puyallup River Valley area by using receptor modeling techniques to analyze 
vi 

existing PM2.5 speciation data. The data were collected from January 2006 to May 2009 using 

the Speciation Trends Network (STN) sampling protocols in South Tacoma, Washington. 

The results of this analysis indicate that the most significant sources of PM2.5 in the 

nonattainment area, in order of their overall average contributions, are: 

 wood smoke (45%) 

 secondary particles (25%) 

 motor vehicles (13%) 

 industrial emissions (6%) 

 fugitive dust (4%) 

 fireworks (2%) 

 oil combustion and ships (1%) 

 fresh and processed sea salt (4%) 

Conclusions 

Wood smoke is the single most important source of PM2.5 in the Wapato Hills-Puyallup River 

Valley nonattainment area. Wood smoke contributed more than half of the PM2.5 on most days 

in the winter, as well as on days when PM2.5 levels were the highest. The total amount of wood 

smoke probably comes from a combination of wood stoves and other wood-fired home heating 

devices. Outdoor burning is not likely to contribute significantly to PM2.5 in this area, because 

the area surrounding the sampling site is classified as an Urban Growth Area (UGA), where 

outdoor burning is prohibited. 

Although wood smoke emissions were responsible for the majority of the PM2.5 pollution on 

most days, average contributions from wood smoke and local industry appear to have declined 

each year since 2006. A similar decline was not observed with other sources or with the 98th

percentile values of the 24-hour wood smoke concentrations. 
1 

1.0 INTRODUCTION 

Numerous health effect studies have shown that exposure to elevated concentrations of fine 

particles that are less than 2.5 micrometers in aerodynamic diameter (PM2.5) causes adverse 

health effects, including cardiovascular and respiratory effects in sensitive populations (e.g., 

Pope, 1996; Lewtas, 2007; Huang and Ghio, 2009). Most PM2.5 found in the ambient air results 

from direct emissions from stationary and mobile emission sources, and secondary particles 

formed through atmospheric reactions. 

On October 17, 2006, the United States Environmental Protection Agency (EPA) strengthened 

the 24-hour PM2.5 national ambient air quality standards (NAAQS) from 65 micrograms per 

cubic meter (μg/m3

) to 35 μg/m3, but retained the annual PM2.5 NAAQS at 15 μg/m3 (71 FR 

61144). The revised 24-hour PM2.5 standard became effective on December 18, 2006. To attain 

this 24-hour standard, the 3-year average of the 98th percentile of 24-hour PM2.5 concentrations at 

each population-oriented monitor within an area must not exceed 35 μg/m3. 

On November 13, 2009, EPA published final designations in the Federal Register announcing 

those areas that meet or violate the 2006 24-hour PM2.5 NAAQS (74 FR 58688). Through that 

Federal Register notice, EPA formally designated 31 areas including part of Pierce County, 

Washington, as nonattainment of the 2006 24-hour PM2.5 NAAQS, in accordance with the 

requirements of the federal Clean Air Act Section 107(d). The final designations are effective 

December 14, 2009, and are based upon 2006 through 2008 monitored PM2.5 data. 

Figure 1.1 shows an outline of Pierce County’s PM2.5 nonattainment area, as described in the 

Federal Register, and the approximate location of the ambient monitoring site used for the 

designation. The designated 24-hour PM2.5 nonattainment area lies approximately 20 miles 

south of Seattle, Washington, and covers most of metropolitan Tacoma and surrounding 

communities within Pierce County’s Comprehensive Urban Growth Area. There are currently 

no nonattainment areas for the annual PM2.5 NAAQS in Washington State. 

As a result of this designation, Washington State (like other affected states) is required to 

develop a State Implementation Plan (SIP), within three years of the effective date of the final 

designations, which provides for attainment of the NAAQS as expeditiously as practicable (74 

FR 58688). The SIP submission must include an attainment demonstration showing that the 

application of selected reasonably available control measures and control technologies to specific 

source types will lead to attainment of the 2006 24-hour PM2.5 NAAQS as soon as possible. The 

exact requirements of the SIP are unknown because EPA has not published implementation 

instructions for the 2006 PM2.5 NAAQS. 

Preparation of an effective SIP begins with proper identification and prioritization of emission 

sources to be controlled. In general, emission sources found to be directly or indirectly 

contributing to the highest mass concentrations in the nonattainment area should be given 

priority. Useful information for identifying the most important emission sources to be controlled 

can be obtained through analysis of speciated PM2.5 measurements. 
2 

Figure 1.1.  Wapato Hills‐Puyallup River Valley Nonattainment Area.  The nonattainment

boundary is shown in pink.

In response to the monitored high ambient PM2.5 concentrations measured in south Tacoma, and 

to address nonattainment issues in the region, the Washington State Department of Ecology 

initiated an effort to identify and quantify the most important sources contributing to the 

measured mass concentrations within the designated nonattainment area. Existing PM2.5

speciation data collected using the Speciation Trends Network (STN) sampling protocols in 

South Tacoma, Washington, from January, 2006 to May, 2009 were analyzed in this study. 

The source apportionment analysis was conducted using Positive Matrix Factorization (PMF; 

Paatero and Tapper, 1994; Paatero, 1997) and the Conditional Probability Function (CPF; 

Ashbaugh et al., 1985). Detailed results from this study are presented in the following sections.
3 

2.0 EXPERIMENTAL METHODS 

2.1 Sampling Site and Data Description 

The data used in this study were collected between January 11, 2006 and May 7, 2009 at 7802 

South L Street in Tacoma, Pierce County, Washington. The sampling site (Latitude 47.1863 o, 

Longitude -122.4515o

) is operated by Puget Sound Clean Air Agency (PSCAA) and is located 

approximately 4 miles south of downtown Tacoma. The area surrounding the site is a 

predominantly low income residential neighborhood. A major north-south freeway (Interstate-5) 

runs less than one mile west of the site (Figure 2.1). There are also several heavily traveled 

arterial streets that are located within one mile of the sampling site. An Air Force base 

(McChord Air Force Base) is located approximately 3 miles southwest of the site, and an Army 

base (Fort Lewis) is a farther 3 miles southwest. Multiple industries are located in the vicinity of 

the site, including several major industrial facilities that are located within 6 miles northeast of 

the sampling site. Major industries located within the Port of Tacoma include a pulp and paper 

mill; a refuse processing facility; an oil refinery; an agricultural feeds processing facility; and a 

lime processing plant. 

An analysis of emissions inventory data for Pierce County indicates that wood combustion 

emissions, primarily emissions from wood-fired home-heating devices, are the largest source of 

emissions in Pierce County. A recent analysis of the area’s ambient air quality data further 

found that the highest total PM2.5 mass concentrations in the area are observed exclusively in the 

winter, with carbonaceous PM2.5 being the dominant chemical component on days with the 

highest PM2.5 mass concentration (EPA, 2008b). 

Twenty-four-hour PM2.5 speciation measurements were made every sixth day according to 

Speciation Trends Network (STN) protocols. There were a total of 74 species measured, 

including total PM2.5 mass. Ionic species (i.e., ammonium, sulfate, nitrate, sodium, and 

potassium ions) were analyzed by ion chromatography of material collected on nylon filters in 

the Andersen RAAS speciation sampler. Other elemental species were measured using energy 

dispersive x-ray fluorescence of material collected on Teflon® filters in the Andersen RAAS 

speciation sampler. PSCAA also made concurrent ambient measurements of black carbon using 

an aethalometer, as well as measurements of multiple meteorological variables. 

In this study, species containing at least 75% of concentrations below detection, or missing, were 

omitted from further analysis. Under this criterion, we omitted the following 24 species (i.e., 

32% of all species measured by the STN speciation sampler): antimony (Sb), barium (Ba), 

cadmium (Cd), cesium (Cs), cerium (Ce), europium (Eu), gallium (Ga), gold (Au), hafnium (Hf), 

indium (In), iridium (Ir), lanthanum (La), molybdenum (Mo), niobium (Nb), phosphorus (P), 

samarium (Sm), scandium (Sc), silver (Ag), tantalum (Ta), terbium (Tb), tin (Sn), tungsten (W), 

yttrium (Y), and zirconium (Zr). 
4 

Sampling

Site

Port of

Tacoma

Figure 2.1.  Location of the sampling site with respect to major particle sources.

We also did not utilize sulfur (S), potassium ion (K+

) or sodium (Na) in this study because they 

were highly correlated with sulfate (SO4

2-), potassium (K) and sodium ion (Na+

), respectively, 

suggesting that their ambient concentrations were being double-counted in the data set. 

Additionally, chromium (Cr), cobalt (Co), mercury (Hg), rubidium (Rb), and selenium (Se) had 

signal-to-noise ratios of less than or equal to 1.0 (EPA, 2008a) which prevented meaningful 

statistical fitting of those species. Those species were therefore excluded from further analysis. 

The wind speed and direction data used in this study were measured at the same air quality 

monitoring site described above. The data were downloaded from PSCAA’s TrendGraphing

website: http://trendgraphing.pscleanair.org/. Figure 2.2 shows the area’s wind profile during the 

period, January 11, 2006 through May 7, 2009. It can be observed that most of the wind 

appeared to be traveling at 1–2 meters per second (m/s), and generally approached from the 

south-southwest. We observed only three occurrences between January 1, 2006 and May 7, 

2009, when the winds exceeded 4 m/s, meaning that winds were generally mild within the study 
5 

Wind Speed (m/s)

0‐1 1‐2 2‐3 3‐4 >=4

Wind Direction (Degrees from North)

0

45

90

135

180

225

270

315

360

area. On days when the measured total mass concentrations exceeded 30 μg/m3, winds blew 

from the southeast approximately two-thirds of the time (Table 2.1), at an average speed of about 

0.6 m/s. 

Figure 2.2.  The area's wind profile during the study period.  Plot shows one‐hour averaged

measurements.  Box edges represent the 25th and 75th percentiles of the wind direction.  Solid

line inside the box is the median wind direction.  Dotted line inside the box is the average wind

direction.  Lower and upper whisker caps are the 10th and 90th percentiles, respectively.  Any

data outside the 10th/90th percentiles are plotted as circular points.

On 5/12/07, STN began using revised thermal optical transmittance and reflectance protocols for 

organic carbon (OC) and elemental carbon (EC), while the previous protocols were continued up 

to 7/5/07. In this work, we utilized total EC and total OC data measured using the old thermal 

optical transmittance (TOT) protocol prior to and including 5/6/07. After 5/6/07, we used total 

EC and OC data measured by the revised TOT protocol. We did not use thermal optical 

reflectance (TOR) data because there were no total EC (TOR) data prior to 7/5/07. Also, we did 

not use data for carbon fractions (i.e., EC1, EC2, EC3, OC1, OC2, OC3, OC4 and OP) because 

EC fractions were not measured prior to 5/12/07. 
6 

Table 2.1.  Total PM2.5 mass concentrations and average wind direction on days when the

FRM or STN samplers reported ambient mass concentrations exceeding 30 μg/m3

Sample Date FRM Mass

Concentration (μg/m3)

STN Mass

Concentration (μg/m3)

Average Wind

Direction (Degrees)

2/19/2006 33.7 No data 107

11/1/2006 32.3 33.0 119

12/7/2006 No data 32.6 209

12/16/2006 68.0 No data 79

12/19/2006 32.6 32. 7 189

12/28/2006 42.7 No data 67

12/31/2006 50.2 54.3 160

1/12/2007 44.7 44.9 81

1/15/2007 58.6 No data 159

1/27/2007 30.8 No data 148

1/30/2007 38.2 39.1 115

2/2/2007 46.7 No data 75

10/27/2007 31.7 34.5 93

11/8/2007 31.5 33.2 151

11/23/2007 45.3 No data 96

1/24/2008 44.2 No data 103

1/25/2008 49.7 No data 95

2/18/2008 31.7 32.8 92

2/21/2008 31.1 No data 48

12/5/2008 39.2 No data 156

12/23/2008 49.2 No data 103

1/19/2009 39.3 38.3 52

2/3/2009 36.8 No data 117

2/18/2009 27.0 31.1 21

    

The average total PM2.5 mass concentration measured by the STN speciation sampler was 9.66 

μg/m3

 (N = 191 samples) while the average total mass concentration measured by the federal 

reference method (FRM) during this period was 9.62 μg/m3

 (N = 394 samples). 
7 

Table 2.2.  Summary of data used in this study.

Species

Total

Number

of Valid

Samples,

N

Percentage

of Missing

and Below‐

Detection

Values (%)a

Statistical Measure (μg/m3)

b

Average

Standard

Deviation Minimum Maximum

PM2.5 Mass 191 2.6 9.66 8.71 0.10 54.29

FRM Mass 394 0 9.62 9.66 0.90 68.00

Nitrate 190 3.6 0.75 0.65 0.00 3.22

Sulfate 190 3.1 0.97 0.63 0.04 3.64

Elemental Carbon (EC) 190 1.6 0.79 1.09 0.07 7.60

Organic Carbon (OC) 190 2.1 3.97 4.13 0.08 28.25

Black Carbon (BC) 190 10.5 0.95 1.04 0.12 4.77

Aluminum (Al) 191 37.8 0.0199 0.0280 0.0001 0.2390

Ammonium ion (NH4

+

) 190 9.2 0.38 0.34 0.01 2.24

Arsenic (As) 191 34.7 0.0015 0.0018 0.00001 0.0199

Bromine (Br) 191 11.7 0.0024 0.0020 0.00005 0.0101

Calcium (Ca) 191 5.1 0.0268 0.0250 0.0006 0.1922

Chloride Ion (Cl‐) 191 10.2 0.14 0.25 0.001 2.34

Chromium (Cr) 191 45.9 0.00115 0.00126 0.00004 0.01194

Cobalt (Co) 191 63.3 0.00055 0.00034 0.00006 0.00231

Copper (Cu) 191 13.3 0.004 0.007 0.00001 0.056

Iron (Fe) 191 2.6 0.056 0.052 0.0002 0.336

Lead (Pb) 191 35.2 0.00385 0.00449 0.00005 0.03050

Magnesium (Mg) 191 51.0 0.015 0.022 0.0001 0.148

Manganese (Mn) 191 21.9 0.00215 0.00249 0.00002 0.01567

Mercury (Hg) 178 74.0 0.00180 0.00117 0.00014 0.00912

Nickel (Ni) 191 27.0 0.0014 0.0016 0.00001 0.0105

Potassium (K) 191 2.6 0.10733 0.23063 0.00142 1.69983

Rubidium (Rb) 191 67.3 0.00069 0.00045 0.00001 0.00344

Selenium (Se) 191 66.3 0.00077 0.00045 0.000002 0.00360

Silicon (Si) 191 4.6 0.061 0.082 0.0003 0.715

Sodium Ion (Na+) 190 3.1 0.219 0.207 0.003 1.550

Strontium (Sr) 191 57.7 0.00160 0.00393 0.00005 0.03968

Titanium (Ti) 191 58.7 0.0026 0.0031 0.0001 0.0343

Vanadium (V) 191 36.2 0.0029 0.0038 0.0001 0.0306

Zinc (Zn) 191 7.7 0.0100 0.0109 0.0002 0.0581 a

In the original data set after blank correction.

b

After treatment for missing and below‐detection values.
8 

A comparison of the measured concentrations by these two methods for the days when the FRM 

monitor predicted ambient concentrations greater than 30 μg/m3

 (Table 2.1) indicates that the 

STN speciation sampler closely replicated the FRM’s values. In fact, the mass concentrations 

obtained by the STN sampler tended to be slightly higher than the corresponding FRM values. 

Note that STN data were obtained every sixth day, compared to an every-third-day frequency for 

the FRM mass measurements. Overall, on those days when both the STN and FRM samplers 

recorded a PM2.5 mass measurement, the two measurements were strongly correlated, with an 

overall correlation coefficient (r2

) of 0.95 (N = 186 samples). 

 

Except for organic carbon, all of the speciation data used in this study were blank-corrected 

using the provided field blanks. For organic carbon (OC), we corrected for OC sampling 

artifacts by plotting the uncorrected total OC against the measured total PM2.5 mass and using the 

y-intercept as the correction. No OC field blanks were subtracted from the raw concentrations as 

it was assumed that the artifacts encompassed the field blank. 

We did not exclude data associated with fireworks primarily because, as shown in Table 2.1, 

both the STN and FRM monitors reported unusually high concentrations on one of the suspected 

fireworks days. Suspected fireworks days were 7/4/2006, 12/31/2006, 7/5/2007, 7/5/2008 and 

1/1/2009. Since the high total mass concentration on 12/31/2006 may have affected the PM2.5

design value for that year, we therefore found it important to ascertain the exact contribution of 

fireworks to the measured ambient concentrations in the area. 

Table 2.2 provides a statistical summary of the final species used in the source apportionment 

analysis. For completeness and data comparison, Table 2.2 also shows the statistics for total 

PM2.5 mass as measured by the FRM sampler. Because the average measured mass 

concentrations determined by STN and FRM methods are in very good agreement (9.66 and 9.62 

μg/m3, respectively), it is safe to conclude that, the average source contributions resolved in this 

study would not be expected to substantially change if the analysis relied exclusively on FRM 

measurements. 

2.2 Model Description 

We utilized two primary data analysis methods in this analysis: Positive Matrix Factorization 

(PMF) and the Conditional Probability Function (CPF). Both of these methods have been 

extensively utilized and discussed in the air quality management literature. For completeness, 

the two methods are summarized below. 

2.2.1 Positive Matrix Factorization (PMF) 

PMF is a mathematical (statistical) technique that decomposes a matrix of speciated ambient 

sample data into a source contributions matrix and a source profile matrix. The PMF method has 

been discussed in detail by EPA (2008a). The latest version of EPA’s formulation of the PMF 

model (EPA PMF 3.0) was used in this study. The PMF algorithm attempts to find a 

mathematical solution that satisfies several mass apportionment conditions (Hopke et al., 1980; 

Paatero, 1997). In general, the model attempts to estimate the optimum number of independent 

sources (or “factors”) that adequately explain the observations at the sampling site. The resolved 
9 

sources are identified and validated through information known a priori about the airshed being 

studied. The modeling procedures recommended by EPA (2008a) were followed in this study. 

2.2.1.1 Data Treatment for PMF Analysis 

A PMF analysis requires extensive pre-treatment of data before the data can be used with the 

model. The data set must not contain missing, zero or non-positive concentrations. Negative or 

zero concentrations that typically result from blank corrections must be appropriately substituted 

with non-negative values. Any missing concentrations in a valid sample must be estimated prior 

to application of the model. Finally, each reported concentration must be accompanied by an 

uncertainty value that represents the sum of all uncertainties associated with that measurement. 

Total EC

Total EC (TOT) (g/m3)

012345

Total EC (TOT) Uncertainty (g/m3

) 0.1

0.2

0.3

0.4

0.5

0.6

Y = 0.058X + 0.225

r

2 = 0.97

Total OC

Total OC (TOT) (g/m3)

0 5 10 15 20 25 30

Total OC (TOT) Uncertainty (g/m3

) 0.0

0.5

1.0

1.5

2.0

2.5

Y = 0.069X + 0.199

r

2 = 1.00

Figure 2.3.  Plots of measured uncertainties (y‐axis) against measured concentrations (x‐axis)

for elemental carbon (EC) and organic carbon (OC).
10 

In this study, missing data were replaced with interpolated concentrations from the closest given 

values or estimated non-negative values. For such data points, the uncertainty was calculated as 

four times the estimated concentration. Except for aluminum (Al), values reported to be below 

detection were replaced with one-half of the sample detection limit for that species. We then 

calculated the associated uncertainties for such data points as twice the estimated concentration. 

In accordance with the air quality monitoring advisory for Al 

(<http://vista.cira.colostate.edu/improve/Data/QA_QC/Advisory/da0010/da0010_Almdl.pdf>), if 

Al was reported below the detection limit, we calculated the associated concentration data point 

as one-third of the silicon (Si) concentration value for that sample, based on the approximate 

ratio of Al to Si on days when valid Al and Si values were reported. The corresponding 

uncertainties were calculated as four times the estimated concentrations. 

For OC and EC, since no measurement uncertainties were reported under the revised TOT or 

TOR protocols, the required measurement uncertainties were estimated by regressing the 

reported uncertainties against the ambient concentrations determined under the old TOT and 

TOR protocols. Based on the regression analysis, the required uncertainties were calculated 

using the following equations: 

 

For EC: EC Uncij = (0.058)×(EC Concij) + 0.225 (1) 

For OC: OC Uncij = (0.069)×(OC Concij) + 0.199 (2) 

where Uncij is the calculated (estimated) measurement error for species j and sample i; Concij is 

the observed species concentration; and the constants are regression coefficients (Figure 2.3). 

Because no measurement uncertainties were provided for black carbon (BC), the required 

uncertainties were calculated using the equation reported by Ogulei et al. (2006), with the 

arbitrary constant set to 0.1. An empirical extra modeling uncertainty of 20% was added to the 

estimated uncertainties of all species to account for uncorrected random and systematic errors. 

In addition, PM2.5 mass concentrations were down-weighted by a factor of 3 in the PMF analysis 

in order to minimize their effect on the apportionment of other species. 

2.2.1.2 Optimum Number of Sources 

As recommended by EPA (2008a), the optimum number of sources was based primarily upon 

the quality of the least-squares fit and the interpretability of the model results. The experimental 

residual sum of squares, Q, should be approximately equal to its theoretical value (EPA, 2008a). 

The theoretical value of Q was approximated by the total number of data points minus the total 

number of degrees of freedom, that is, 

 

Qtheoretical = (number of species × number of samples) (3) 

- (number of species × number of sources) 

- (number of samples × number of species) 
11 

m CPF

n















2.2.2 The Conditional Probability Function (CPF) 

The CPF is a probabilistic tool that estimates the probable direction associated with ambient 

concentrations that exceed an operationally-defined threshold. In this study, the threshold was 

defined as the highest 25% (i.e., 75th percentile) of the resolved contributions. We calculated the 

CPF for each wind sector using the following equation: 

(4) 

 

where Δθ is the wind sector (20o each); mΔθ is the number of contribution occurrences from wind 

sector Δθ in which the resolved contributions fell in the upper 25% of all contributions; and nΔθ

is the total number of contribution occurrences from wind sector Δθ. 

For the purpose of calculating the CPF, data points corresponding to wind speeds below 1 m/s 

(2.2 miles per hour) were initially excluded in the 

preliminary analysis. This is because winds slower than 

1 m/s tend to lead to wind meandering around the 

sampling site, which typically makes it difficult to 

identify potential point source locations. However, 

because previous studies found that exceedances of the 

24-hour PM2.5 national ambient standard typically 

occurred at very low wind speeds, the final analysis was 

refined to include all wind speeds in the CPF 

calculations. The CPFs presented in this report 

therefore include all measured 24-hour averaged wind 

speeds in the calculations. 

A plot of the overall wind profile is shown in Figure 2.4. 

A comparison between Figures 2.2 and 2.4 indicates that 

the two profiles both show that the predominant wind 

direction was south-southwest. This is particularly 

relevant if there are significant sources located to the 

south-southwest direction relative to the sampling site. 

We also calculated CPFs for individual species to determine the directions associated with high 

measured concentrations for those species (CPFs not shown here). Those CPFs showed similar 

potential source locations associated with the measured organic carbon (OC) and black carbon 

(BC) concentrations. There were also similarities between potential source locations associated 

with the measured nickel (Ni) and vanadium (V) concentrations. Total PM2.5 mass emissions 

appeared to come from multiple directions as did emissions of most species.

Figure 2.4.  Overall wind profile.  

Numbers in the plot indicate number of

occurrences from a given wind sector.

0 4 8 121620242832364044

N

NW

W

SW

S

SE

E

NE
12 

3.0 SOURCE APPORTIONMENT RESULTS 

3.1 Criteria for Source Identification 

In this study, we defined the “correct” solution as the optimum number of sources that 

corresponded to acceptable least squares and regression statistics, and physically interpretable 

source profiles. The solution was checked for stability through a bootstrapping technique. 

Bootstrapping is a re-sampling technique in which a new data set having the same dimensions as 

the original data set is randomly generated (sampled) from the original data set and analyzed. 

The resulting source profiles and contributions are compared with the original (base) solution 

(EPA, 2008a). 

We successfully resolved and identified eleven sources based upon the following criteria: 

1) The elemental composition of the source profile. The presence of specific source 

signatures in a resolved source profile generally permitted the identification of that source 

profile. The resolved source profiles were compared with profiles available through the 

EPA’s SPECIATE database. 

2) The seasonal behavior of the source contributions. This enabled distinction between 

sources that are known to peak during the summer from those that typically peak in the 

winter. For example, secondary nitrate and wood smoke could be distinguished from 

secondary sulfate based on this criterion. 

3) The wind direction(s) corresponding to the highest source contributions. Through the 

CPF analysis, we were able to identify the potential locations of point sources because 

resolved concentrations almost always peaked when winds blew from the direction of 

those sources. 

4) The resolved source contributions versus the emissions inventory. Knowledge of the 

area’s emissions inventory and published information on source contributions enabled 

identification of certain sources, such as wood smoke. 

We identified the eleven emission sources as: wood smoke, gasoline vehicles, diesel vehicles, 

fugitive dust, fireworks, secondary nitrate, secondary sulfate, oil combustion and ships, arsenicrich industrial emissions, freshly-emitted sea salt, and processed (“aged”) sea salt. On average, 

the eleven sources accounted for 93% of the measured PM2.5 mass. The remaining 7% may 

represent the effect of noise in the measured data and the apportionment itself, other relatively 

insignificant sources or a combination of these factors. 
13 

3.2 Overall Contributions 

Table 3.1 shows the average contributions in micrograms per cubic meter (μg/m3) resolved in 

this analysis. The contributions have been averaged over the entire sampling period. The 

percentage contributions are shown in Figure 3.1. Overall, wood smoke was the dominant 

source during the study period with an average contribution of 4.0 μg/m3, or about 45% of the 

total apportioned PM2.5 mass. Additionally, PMF resolved significant contributions from 

secondary aerosol (25%) and motor vehicles (13%). A break-down of the source contributions 

among the various seasons is provided later in the report. 

Table 3.1.  Average contributions resolved by the PMF model.  

Resolved Source

Average Contribution

(μg/m3

)

Standard Deviation

(μg/m3)

Wood Smoke 4.0 4.9

Secondary Nitrate 1.2 1.4

Secondary Sulfate 1.0 0.9

Gasoline Vehicles 0.9 1.3

Arsenic‐rich Industrial Emissions 0.5 0.6

Fugitive Dust 0.4 0.5

Fresh Sea Salt 0.3 0.6

Diesel Vehicles 0.3 0.3

Fireworks 0.2 0.7

Oil Combustion and Ships 0.1 0.2

Aged Sea Salt 0.03 0.02

3.3 Seasonal Contributions 

To better understand the contributions of individual sources, we divided the sampling period into 

four seasons as follows: “Winter”, which was used to represent the calendar months of 

December, January and February; “Spring”, which represented March, April and May; 

“Summer” for June, July and August; and “Fall” for September, October and November. To 

better resolve the contribution of wood smoke in the study area, we also defined a “heating” 

season (October through March) and a “non-heating” season (April through September). 

Boundaries for the heating and non-heating seasons were based on existing data on wood stove 

use within the Puget Sound area. The heating season was defined to account for those months 

when ambient temperatures were low enough that home heating was expected to be prevalent. 
14 

Fugitive Dust

4%

Wood Smoke

45%

Gasoline

Vehicles

10%

Oil

Combustion/Ships

1%

Fireworks

2%

Aged Sea Salt

<1%

Sec. Sulfate

11%

Fresh Sea Salt

4%

Sec. Nitrate

14%

Diesel Vehicles

3%

Arsenic‐rich

Industrial

6%

Figure 3.1.  Percentage contributions, averaged over the entire modeling period.

Figure 3.2 shows the resolved average contributions by season. It can be observed that strong 

seasonality was observed in some, but not all, of the resolved sources. Specifically, wood 

smoke, fireworks, secondary nitrate, motor vehicles, industrial emissions, and fresh sea salt 

exhibited pronounced fluctuations in average contributions between two or more seasons. Most 

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

smoke contributions occurred in the winter. 

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

to the low atmospheric mixing height in the winter. Also, for motor vehicles, the increased 

number of cold-starts in the winter may have contributed to increased motor vehicle emissions in 

the winter. 

The differences in contributions between heating and non-heating months can be seen in Figure 

3.3. The most noticeable change in average contributions between heating and non-heating 

seasons was observed with wood smoke. As discussed later in the report, this observation is not 

surprising given the reported heavy reliance of the study area’s local residents on wood heating 

as the primary source of heat in the winter. 
15 

Average Concentration (g/m3)

01234567

Aged Sea Salt

Fireworks

Oil Combustion/Ships

Fugitive Dust

Diesel Vehicles

Fresh Sea Salt

Arsenic‐rich Industrial

Sec. Sulfate

Gasoline Vehicles

Sec. Nitrate

Wood Smoke

Summer

Spring

Fall

Winter

Figure 3.2.  Average 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.

Average Concentration (g/m3)

01234567

Aged Sea Salt

Fireworks

Oil Combustion/Ships

Fugitive Dust

Diesel Vehicles

Fresh Sea Salt

Arsenic‐rich Industrial

Sec. Sulfate

Gasoline Vehicles

Sec. Nitrate

Wood Smoke

Non‐heating Season

Heating Season

Figure 3.3.  Heating vs. non‐heating contributions.  The most significant change in average

contributions was observed with wood smoke.
16 

3.4 Weekday and Weekend Contributions 

Differences between weekday and weekend average concentrations were generally less 

pronounced than seasonal average contributions. As shown in Figure 3.4, the observed 

differences between weekday and weekend concentrations for some of the sources were within 

experimental error suggesting that daily human activities were not the exclusive explanation for 

the observed contributions from those sources. In fact, this analysis found that weather 

conditions also played a significant role in the observed PM2.5 concentrations on certain days. 

Source Type

Wood Smoke

Sec. Nitrate

Gasoline Vehicles

Sec. Sulfate

Arsenic‐rich Industrial

Fresh Sea Salt

Diesel Vehicles

Fugitive Dust

Oil Combustion/Ships

Fireworks

Aged Sea Salt

Average Conc. (g/m3

)

0.2

0.4

0.6

0.8

1.2

1.4

1.6

1.8

2.2

2.4

2.6

2.8

3.2

3.4

3.6

3.8

4.2

4.4

4.6

4.8

0.0

1.0

2.0

3.0

4.0

5.0

0.2

0.4

0.6

0.8

1.2

1.4

1.6

1.8

2.2

2.4

2.6

2.8

3.2

3.4

3.6

3.8

4.2

4.4

4.6

4.8

0.0

1.0

2.0

3.0

4.0

5.0

Weekday

Weekend

Figure 3.4.  Weekday/weekend variation.  Weekday refers to Monday through Friday;

Weekend is Saturday and Sunday.  Error bars are standard deviations of the mean.  

Weekday contributions were noticeably higher than weekend average contributions for motor 

vehicles, secondary nitrate and fugitive dust. Weekend average contributions were higher than 

weekday values for secondary sulfate, industrial emissions and oil combustion/ships. The 

weekday-weekend difference was within experimental error for the other sources, including 

wood smoke. 
17 

3.5 Annual Contributions 

Table 3.2 shows annual average contributions for the years used in this study. Although the data 

available for 2009 represented less than half of the year (January 11, 2006 through May 7, 2009), 

it is possible to notice that average contributions for wood smoke and industry appeared to 

annually decrease from the 2006 levels. This annual decline appeared to exist in absolute terms 

(concentrations) and relative terms (percentage). There was no indication of a decline in annual 

average contributions from other source categories. 

Note that while there is a noticeable decline in the annual averaged wood smoke emissions, a 

longer time period is needed to establish a trend in contributions. Moreover, the 98th percentile 

values of the 24-hour concentrations did not show a similar decline. The 98th percentile 

concentrations for wood smoke were 23, 24 and 13 μg/m3 for 2006, 2007 and 2008, respectively, 

while the 98th percentile concentrations for motor vehicles were 2.3, 1.9 and 1.2 μg/m3 for 2006, 

2007 and 2008, respectively. 

Table 3.2.  Source contributions averaged over individual years.  

Sample Year   2006 2007 2008 2009*

Measured PM2.5 Mass (μg/m3)   9.4 10.8 8.3 10.4

Average Concentrations (μg/m3) (Percentage of Measured Mass)

Wood Smoke   4.8 (51%) 4.4 (41%) 3.0 (37%) 3.7 (35%)

Secondary Aerosol   2.0 (21%) 2.5 (23%) 2.1 (25%) 2.4 (23%)

Motor Vehicles   0.9 (9%) 1.2 (11%) 1.0 (11%) 2.7 (26%)

Industry   0.6 (6%) 0.6 (5%) 0.3 (4%) 0.5 (5%)

Other Identified Sources   1.1 (12%) 0.8 (8%) 1.1 (13%) 0.7 (7%)

* Only measurements made until May 7, 2009 were available for analysis.  Therefore, caution

should be exercised in interpreting the 2009 figures.

3.6 Contributions on Days When the Measured Total 

Mass Concentration Was Elevated 

Figure 3.5 shows the range of percentage contributions for four different ambient PM2.5 mass 

concentrations. The resolved average contributions on days when the measured total PM2.5 mass 

concentration was or exceeded 30 μg/m3

 are shown in Table 3.3. As indicated by Figure 3.5 and 

Table 3.3, wood smoke was responsible for at least one-half of the observed total mass on most 

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

. On those days 

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

, the wood smoke 

proportion reached 72% at one time, on 11/1/2006. The corresponding total mass concentration 
18 

was 33 μg/m3. Table 2.1 (Section 2.1) indicates that the average wind direction on that day 

(11/1/2006) was southeast. 

Wood smoke contributions also averaged about 52% when the total PM2.5 mass concentration 

exceeded 20 μg/m3

, which is a healthy air goal established by the Washington State Department 

of Ecology (Figure 3.5). On two occasions when the total PM2.5 mass concentration exceeded 20 

μg/m3 during the study period, the wood smoke contribution was 72% of the daily total mass. 

Source

Wood Smoke

Sec. Nitrate

Gasoline Vehicles

Sec. Sulfate

Arsenic‐rich Industrial

Fresh Sea Salt

Diesel Vehicles

Fugitive Dust

Oil Combustion/Ships

Fireworks

Aged Sea Salt

Percent Contribution (%)

0

10

20

30

40

50

60

70

80

PM2.5 > or = 20g/m3

PM2.5 > or = 30g/m3

PM2.5 > or = 35g/m3

PM2.5 > or = 20 but < 35g/m3

Error Bar = Range

Figure 3.5.  Percentage contributions on days when the measured total PM2.5 mass

concentration was elevated.  For completeness, four concentration ranges were evaluated: ≥ 

20 μg/m3 (circular symbols); ≥ 30 μg/m3 (triangles); ≥ 35 μg/m3 (squares); and 20 ≤ PM2.5 <35

μg/m3 (diamonds).  Error bars represent the data range.

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

) where the 

resolved wood smoke contribution exceeded 50%, daily-averaged winds blew from the southeast 

or southwest approximately 70% of the time. This suggests that wood smoke sources located to 

the southeast and southwest of the sampling site may have been responsible for the majority of 

the observed wood smoke concentrations on those days. 
19 

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

sulfate) also contributed significant amounts to the measured total mass on most days when the 

PM2.5 ambient concentration was or exceeded 30 μg/m3

. Motor vehicles contributed about onequarter of the total measured mass on Martin Luther King Jr. Day (1/19/2009). An analysis of 

the measured PM2.5 data showed that there was a marked increase in ambient concentrations of 

multiple species on that day. The total PM2.5 mass was 38 and 39 μg/m3 according to the STN 

and FRM samplers, respectively. The average wind speed on 1/19/2009 was relatively mild at 

0.5 m/s (1.1 miles per hour) approaching from the east. Motor vehicle contributions were also 

significant on 12/31/2006 when the measured total mass concentration was the highest (54 

μg/m3

). 

Secondary aerosol (secondary nitrate plus secondary sulfate) was responsible for about one-third 

of the total mass on 11/8/2007, and about one-fifth of the total mass on several other days, 

including 1/30/2007, 10/27/2007, 2/18/2008 and Martin Luther King Jr. Day (1/19/2009). These 

contributions are significant, and may suggest a relationship between the resolved secondary 

aerosol, wood smoke and motor vehicles on the days with elevated ambient PM2.5 mass 

concentrations. However, further analysis is needed to better characterize the sources of 

secondary aerosol within the study area. 

Table 3.3.  Contributions on days when the observed PM2.5 mass concentration was greater

than or equal to 30 μg/m3.

Sample Date

Measured PM2.5

Mass (μg/m3)

Wood

Smoke

Secondary

Aerosol

Motor

Vehicles Industry

Other

Identified

Sources

11/1/2006 33.0 72% 8% 10% 7% 4% 

12/7/2006 32.6 51% 13% 12% 7% 5% 

12/19/2006 32.7 63% 12% 9% 5% 3% 

12/31/2006 54.3 53% 5% 16% 12% 4% 

1/12/2007 44.9 59% 17% 6% 4% 2% 

1/30/2007 39.1 61% 20% 12% 6% 4% 

10/27/2007 34.5 52% 21% 9% 6% 3% 

11/8/2007 33.2 35% 37% 11% 3% 6% 

2/18/2008 32.8 37% 23% 12% 3% 9% 

1/19/2009 38.3 40% 18% 26% 5% 3% 

2/18/2009 31.1 46% 15% 16% 3% 5% 
20 

4.0 DETAILED RESULTS AND DISCUSSION 

   

The PMF results presented in Section 3.0 indicate that the airshed in south Tacoma is dominated 

by wood smoke and a select number of sources. The effects of individual sources on the 

measured ambient mass concentrations were typically exacerbated by air stagnation events. For 

example, there appeared to be an air stagnation event on 12/31/2006, corresponding to an 

average wind speed of about 1 mile per hour (0.4 m/s) blowing from the south-southeast. The 

observed 24-hour ambient PM2.5 concentration on this day was 54 μg/m3. Because a significant 

number of wood smoke sources were located to the southeast of the sampling site, the effect of 

those sources may have been exacerbated by stagnation and inversion events on 12/31/2006. 

Similarly, the effect of fireworks on 12/31/2006 may have been more significant than had there 

been no stagnation or meteorological inversion on that day. 

In a previous analysis, the fastest winds from the south-southwest quadrant were found to 

correspond to the lowest PM2.5 mass concentrations while the slowest winds from the east to 

southeast corresponded to the highest PM2.5 concentrations (EPA, 2008b). This illustrates that 

stagnations and inversions, such as those typically prevalent in Tacoma in the winter and fall, are 

additionally responsible for the area’s elevated PM2.5 mass concentrations in the winter and fall. 

4.1 Wood Smoke Contributions 

As presented in Section 3.0, wood smoke accounted for about 45% of the overall average mass. 

The maximum wood smoke contribution on any signal day was 72% of the daily PM2.5 mass 

concentration. The maximum wood smoke contribution (72%) was observed on two occasions: 

11/1/2006 and 12/8/2007, when the measured daily PM2.5 mass concentrations were 33 and 21 

μg/m3

, respectively. This analysis was not able to distinguish between the various types of wood 

smoke sources. However, based on emissions inventory and census information collected by 

Ecology and PSCAA, we believe that the wood smoke contributions resolved in this analysis 

represent combined contributions from wood stoves and other wood-fired home heating devices. 

Significant contributions from outdoor burning are not expected because the area surrounding the 

sampling site is classified as an Urban Growth Area (UGA) where outdoor burning is prohibited. 

Figure 4.1 shows a plot of the predicted source profile for wood smoke emissions. Wood smoke 

emissions were characterized by high abundances of organic carbon (OC), elemental carbon 

(EC), black carbon (BC) and potassium (K). About 80% of OC, 60% of BC, 50% of EC and 

40% of K were apportioned to wood smoke. All of these species are important markers for wood 

smoke and their relative abundances are consistent with abundances reported in the source 

apportionment literature (e.g., Kim and Hopke, 2008a). The wood smoke concentrations 

correlated very well with BC (r2

 = 0.89), total OC (r2 = 0.97), total EC (r2 = 0.79) and total PM2.5

mass concentrations (r2

 = 0.92). 
21 

Species

PM2.5_

Mass

Nitrate

Sulfate

EC

OC

BC

Al NH4+

As

Br

Ca

Cl

Cu

Fe

Pb

Mg

Mn

Ni

K

Si Na+

Sr

Ti

V

Zn

Concentration of Species (g/m3

) 0.0001

0.001

0.01

0.1

1

10

Percentage of Species

0

20

40

60

80

100

Concentration

Percentage

Figure 4.1.  Wood smoke source profile.  “Percentage” refers to the percentage of that species

which was apportioned to a specific source.  Error bars are standard deviations of the

bootstrapping factors.  Signature species are shown with blue bars.

The wood smoke contributions resolved by this analysis differ somewhat with contributions 

observed in neighboring Seattle, Washington. Seattle area studies have typically reported lower 

wood smoke contributions. For example, Maykut et al. (2003), Wu et al. (2007), and Kim and 

Hopke (2008a) reported contributions of 7-37% in various areas near Seattle. Kim and Hopke 

(2008a) reported a significant difference in wood smoke contributions between the residential 

and downtown areas of Seattle. In their study, wood smoke accounted for about 31% of the 

apportioned mass in Lake Forest Park (residential) and 

7-10% in the commercial sites (Olive St., Beacon Hill, 

Duwamish). 

Conversely, comparable average wood smoke 

contributions to this study have been reported from 

Spokane, WA (44%) and Pullman, WA (38%) by Kim 

et al. (2003) and Jimenez et al. (2006), respectively. A 

previous source apportionment study, designed for use 

in defining the extent of the Tacoma area 

nonattainment boundary, estimated that wood smoke 

accounted for as much as 60-90% of the measured 

PM2.5 mass when the measured total mass exceeded 30 

μg/m3

 (EPA, 2008b). 

The CPF for wood smoke is shown in Figure 4.2. The 

CPF shows that the predominant sources of wood 

smoke are located approximately to the south and 

Figure 4.2.  CPF for wood smoke.

0.0 0.4 0.8

N

NW

W

SW

S

SE

E

NE
22 

southeast of the sampling site. This is consistent with suspected locations of residences that rely 

on wood stoves for residential heating (Figure 4.3). 

 

The CPF also indicates contributions from the southwest, which is also the direction of the 

overall wind profile (Figure 2.4). This may suggest that the wind direction was also a factor in 

the observed contributions at the receptor meaning that any wood smoke sources located to the 

southwest of the sampling site had an influence on the PM2.5 mass concentrations recorded at the 

sampling site. However, as stated above, wood smoke sources located to the south and southeast 

of the receptor were the most significant. 

Figure 4.4 shows the time series of wood smoke alongside the time series for total OC 

determined by the STN sampling protocol, BC determined by the aethalometer, and total mass 

measured by the federal reference method. In most cases, total PM2.5 mass peaked if wood 

smoke peaked suggesting that wood smoke likely caused the observed exceedances of the 

ambient standard on certain days. 

Figure 4.3.  Number of homes using wood as a primary heating source per square mile, in the

Tacoma area (Onstad and Simpson, 2008). Note that this map reports 2000 census

information, which may no longer be representative of current practice.  The map also doesn't

include the many people who use wood for secondary heat and ambience, which may lead to

an underestimate of actual wood stove use in the area.   

South L St.

PM2.5 Monitor
23 

BC (Aethalometer/PSCAA)

Mass Concentration (g/m3

) 0

1

2

3

4

5

6

Wood Smoke (PMF)

Sample Date

1/1/06    7/1/06   1/1/07   7/1/07   1/1/08   7/1/08   1/1/09   7/1/09  

0

5

10

15

20

25

30

35

Total OC (STN)

0

5

10

15

20

25

30

Total PM2.5 (FRM)

Sample Date

1/1/06   7/1/06   1/1/07   7/1/07   1/1/08   7/1/08   1/1/09   7/1/09  

0

20

40

60

80

Figure 4.4.  Wood smoke, BC, OC, and PM2.5 time series.  Only the FRM mass series is shown

because the FRM and STN mass series were identical.  Wood smoke was highly correlated with

OC, BC and total mass.  The correlation with elemental carbon (EC) was also strong.  Note the

difference in vertical scales.
24 

4.2 Motor Vehicles (Gasoline and Diesel) 

We resolved two profiles for motor vehicles, accounting for about 13% of the total apportioned 

mass, averaged over the entire sampling period. The two profiles represented emissions from 

gasoline-fired and diesel-fired vehicles. While this analysis did not utilize carbon fractions to aid 

in the separation of gasoline and diesel contributions, it was possible to recognize the difference 

between the two profiles based upon their total OC and EC relative abundances as well as the 

existence or non-existence of certain brake-wear metals and fuel oil additives.

Figures 4.5(a) and 4.5(b) show profiles for gasoline and diesel vehicles, respectively. The 

observed pollutant loadings are similar to loadings reported in gasoline and diesel vehicle 

emission inventories and other source apportionment studies. For example, the high proportions 

of manganese (Mn) and iron (Fe) apportioned to the diesel profile are consistent with reported 

findings from the Seattle area (Maykut et al., 2003) and Phoenix (Ramadan et al., 2000; Lewis et 

al., 2003). 

Species

PM2.5_

Mass

Nitrate

Sulfate

EC

OC

BC

Al NH4+

As

Br

Ca

Cl

Cu

Fe

Pb

Mg

MnNi

K

Si

Na+

Sr

Ti

V

Zn

Concentration of Species(g/m3

) 0.0001

0.001

0.01

0.1

1

Percentage of Species

0

20

40

60

80

100

Concentration

Percentage

Figure 4.5(a).  PMF profile for gasoline‐fired vehicles.  Signature species are shown with blue

bars.  About 60% of copper (Cu) was apportioned to gasoline vehicles.

In many urban areas, EC is primarily emitted by diesel engines and thus is often used as a tracer 

for motor vehicle diesel emissions in receptor modeling studies (Solomon et al., 2008). 

Generally, the concentration of total OC is expected to be higher than the EC concentration in 

emissions from gasoline-fired vehicles. Conversely, except for diesel vehicles operating at very 

slow speeds and in stop-and-go traffic, the concentration of total EC is expected to be higher 

than the OC concentration in emissions from diesel-fired vehicles (Shah et al., 2004; Kim and 

Hopke, 2008b). Thus, it is possible that emissions from diesel vehicles operating at very slow 

speeds and in stop-and-go traffic have been included in the gasoline profile because such 
25 

emissions have been shown to contain OC/EC concentrations that are comparable to those 

observed in typical gasoline emissions. 

Species

PM2.5_

Mass

Nitrate

Sulfate

EC

OC

BC

Al NH4+

As

Br

Ca

Cl

Cu

Fe

Pb

Mg

Mn

Ni

K

Si

Na+

Sr

Ti

V

Zn

Concentration of Species(g/m3

) 0.0001

0.001

0.01

0.1

1

Percentage of Species

0

20

40

60

80

100

Concentration

Percentage

Figure 4.5(b).  PMF profile for diesel vehicles.  About 90% of the measured manganese (Mn)

was apportioned to diesel vehicles, along with significant amounts of zinc (Zn), iron (Fe),

elemental carbon and black carbon.  Signature species are shown with blue bars.

The CPFs for gasoline and diesel vehicles (Figures 4.6(a) and 4.6(b)) are nearly identical 

suggesting similar potential source locations. The CPFs are observed to point to the directions of 

the freeway (Interstate-5) as well as major arterial streets and intersections (see Figure 2.1). 

Although the south-southwestern direction indicated by the motor vehicle CPFs may suggest that 

mobile source emissions originating from the McChord Air Force Base may have been included 

in the motor vehicle contributions, the actual impact of the Air Force Base’s emissions is 

expected to be insignificant (EPA, 2008b). 

It should be noted that if the analysis were based upon particle number concentrations, it is 

possible that the resolved relative contributions of motor vehicles would be different. This is 

because motor vehicles have been reported to emit significant amounts of ultrafine particles (less 

than 100 nanometers in aerodynamic diameter), which do not weigh as much as the heavier super 

micron particles (Ogulei et al., 2007). Additionally, because motor vehicles are significant 

contributors of precursors of secondary aerosol, it is likely that some of the motor vehicle 

emissions are included in the secondary aerosol contributions. 

The total motor vehicle contribution reported in this study (13%) is comparable to the 11% value 

reported by Kim et al. (2003) in Spokane, WA. Also, Maykut et al. (2003), Wu et al. (2007) and 

Kim and Hopke (2008a) reported motor vehicle contributions of 10-44% within the Seattle area. 

But, Jimenez et al. (2006) reported a lower contribution of 2% in the eastern Washington city of 
26 

0.0 0.4 0.8

N

NW

W

SW

S

SE

E

NE

0.0 0.4 0.8

N

NW

W

SW

S

SE

E

NE

Pullman, WA. A smaller contribution from motor vehicles in Pullman is expected compared to 

the larger cities with greater vehicle miles traveled. 

 

 

4.3 Secondary Aerosol (Secondary Nitrate and 

Secondary Sulfate) 

Secondary aerosol was dominated by ammonium nitrate, NH4NO3, and ammonium sulfate, 

(NH4)2SO4. As shown in Section 3.0, secondary aerosol cumulatively accounted for as much as 

25% of the average apportioned mass. Figures 4.7(a) and 4.7(b) show the source profiles for 

secondary nitrate and secondary sulfate, respectively. The measured nitrate and sulfate 

concentrations were highly correlated with the resolved secondary nitrate and sulfate 

contributions, with Pearson’s correlation coefficients (r2

) of 0.96 and 0.89, respectively. The 

ammonium ion (NH4

+

) was also highly correlated with both secondary nitrate and sulfate, with 

correlation coefficients of 0.86 and 0.84, respectively. 

Secondary nitrate in the atmosphere is primarily formed from photochemically-catalyzed 

atmospheric reactions involving nitrogen oxides (NOx), ammonia and ozone. The CPF for 

secondary nitrate (Figure 4.8(a)) indicates that the secondary nitrate may have resulted from 

processing of NOx emissions originating primarily from traffic traveling along I-5 and major 

arterial streets. The predicted secondary nitrate concentration peaks in the winter and late fall 

because the combination of high relative humidity and low ambient temperature in the winter 

promotes partitioning of ammonium nitrate into the particle phase (Moya et al. 2001; Tolocka et 

al., 2004). 

Figure 4.6(a).  CPF for gasoline

vehicles.

Figure 4.6(b).  CPF for diesel

vehicles.
27 

Species

PM2.5_

Mass

Nitrate

Sulfate

EC

OC

BC

Al

NH4+

As

Br

Ca

Cl

Cu

Fe

Pb

Mg

Mn

Ni

K

Si

Na+

Sr

Ti

V

Zn

Concentration of Species(g/m3

) 0.0001

0.001

0.01

0.1

1

10

Percentage of Species

0

20

40

60

80

100

Concentration

Percentage

Figure 4.7(a).  PMF source profile for secondary nitrate.  Signature species are shown with blue

bars.

Species

PM2.5_

Mass

Nitrate

Sulfate

EC

OC

BC

Al NH4+

As

Br

Ca

Cl

Cu

Fe

Pb

Mg

Mn

Ni

K

Si

Na+

Sr

Ti

V

Zn

Concentration of Species(g/m3

) 0.0001

0.001

0.01

0.1

1

10

Percentage of Species

0

20

40

60

80

100

Concentration

Percentage

Figure 4.7(b).  PMF source profile for secondary sulfate.  Signature species are shown with

blue bars.

  
28 

Figure 4.8(a).  CPF for secondary nitrate. Figure 4.8(b).  CPF for secondary sulfate.

0.0 0.4 0.8 1.2

N

NW

W

SW

S

SE

E

NE

0.0 0.4 0.8 1.2

N

NW

W

SW

S

SE

E

NE

Secondary sulfate particles, on the other hand, are largely formed through atmospheric gas phase 

and aqueous phase reactions involving sulfur dioxide (SO2) and atmospheric oxidants such as the 

hydroxyl radical (OH), hydrogen peroxide (H2O2) and ozone. Some of the sulfate particles may 

also be emitted directly as primary sulfate particles (Kerminen et al. 2000). 

The reaction pathways that lead to the formation of secondary nitrate and sulfate also lead to the 

formation of carbonaceous secondary aerosol. This generally explains the presence of 

significant amounts of carbonaceous species (primarily OC) in the secondary aerosol profiles 

(Figures 4.7(a) and 4.7(b)). 

The CPF for secondary sulfate does not show a meaningful relationship with the overall wind 

profile (Figure 2.4). This indicates that the secondary sulfate resolved in this analysis may have 

primarily been formed from atmospheric processing of locally-generated SO2 emissions, 

including SO2 emissions from point and non-point sources. This conclusion was further 

supported by the prevalence of concentration spikes in the secondary nitrate and sulfate time 

series. Multiple authors have previously reported relatively smooth time series for regionally 

transported aerosol (e.g., Ogulei et al., 2005). The presence of significant amounts of vanadium, 

V (20%) and bromine, Br (35%) in the secondary sulfate profile may suggest a significant 

contribution from ship emissions. 

The sulfate time series peaked in late summer and early fall, with the highest average sulfate 

concentration observed in August. This is attributable to increased photochemical activity close 

to the monitoring site. In contrast to the sulfate, the nitrate series peaked in the winter and late 

fall (Figure 4.9). As explained by Seinfeld and Pandis (1998), high ambient concentrations of 

particulate secondary nitrate (primarily ammonium nitrate) are observed when there is a 

combination of high relative humidity and low ambient temperature. Our analysis of the area’s 

ambient relative humidity and temperature data, measured during the study period, revealed that 
29 

the highest relative humidity measurements were typically observed between October and 

February, while the lowest ambient temperatures were observed in January. Thus, while there 

may be other factors, it is likely that the observed seasonal patterns in sulfate and nitrate 

concentrations are primarily defined by weather conditions within the study area. 

Season

Winter Spring Summer Fall Winter*

Sulfate Concentration (g/m3

) 0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Nitrate Concentration (g/m3

)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

PM2.5 Mass Conc. (g/m3

)

0

2

4

6

8

10

12

14

16

Sulfate

Nitrate

PM2.5 Mass

Figure 4.9.  Seasonal profile for secondary aerosol and PM2.5 mass.

It is important to note from Figure 4.9 that the time series for total PM2.5 mass is similar to that of 

secondary nitrate. This may suggest that in addition to weather factors, the primary contributors 

to the observed nitrate may also be responsible for much of the observed total mass. 

The total secondary aerosol contribution (25%) resolved in this study is comparable to published 

values from major cities in the Pacific Northwest. For example, Kim et al. (2003) and Jimenez et 

al. (2006) reported total secondary aerosol contributions of 28% in Spokane, WA and 20% in 

Pullman, WA, respectively. Kim and Hopke (2008a) reported secondary nitrate contributions of 

12-26% and secondary sulfate contributions of 17-20% in five sites within the Seattle area. Kim 

and Hopke (2008b) reported secondary nitrate contributions of 15-16% and secondary sulfate 

contributions of 15-18% in the Olympic Peninsula, WA; Portland, OR; and Anchorage, AK. 

While some differences do exist in the actual apportioned contributions in these studies, it is 

clear that secondary aerosol accounts for a significant portion of the total aerosol burden in many 

cities in the Pacific Northwest.

4.4 Industrial Emissions 

Industrial emissions accounted for as much as 90% of the apportioned arsenic (As), 50% of zinc 

(Zn), and about 50% of lead (Pb) emissions in this study (Figure 4.10). We observed strong 
30 

correlations between industrial emissions and total PM2.5 mass, organic carbon (OC), black 

carbon (BC), arsenic (As), lead (Pb) and zinc (Zn), with Pearson’s correlation coefficients of 

0.81, 0.84, 0.74, 0.86, 0.75 and 0.84, respectively. 

Species

PM2.5_

Mass

Nitrate

Sulfate

EC

OC

BC

Al

NH4+

As

Br

Ca

Cl

Cu

Fe

Pb

Mg

Mn

Ni

K

Si

Na+

Sr

Ti

V

Zn

Concentration of Species (g/m3

) 0.0001

0.001

0.01

0.1

1

Percentage of Species

0

20

40

60

80

100

Concentration

Percentage

Figure 4.10.  PMF profile for industrial emissions.  Signature species are shown with blue bars.

On average, contributions from industrial emissions 

were lowest in the summer, probably due to better 

atmospheric mixing in the summer compared to other 

times in the year. Also, as shown in Table 3.2, 

contributions from this source category appeared to 

decrease annually from their 2006 maximum. This 

may indicate that existing control measures for point 

sources might be offsetting the effects of any 

industrial growth on annual emissions thereby 

producing measurable reductions in annual 

emissions. However, more data are needed to verify 

that this decline is real and that it persists when more 

years are included in the analysis. 

While the CPF (Figure 4.11) suggests the existence 

of contributing sources in multiple directions, it also 

Figure 4.11.  CPF for industrial

emissions.

0.0 0.4 0.8

N

NW

W

SW

S

SE

E

NE
31 

indicates that point sources located to the northeast, and, perhaps, the Port of Tacoma, are partly 

responsible for the observed mass concentrations at the monitor. The observed strong correlation 

between these emissions and combustion products such as OC and BC suggests a significant 

influence of combustion sources. 

Arsenic Concentrationsin

parts per million (ppm)

Study Site

Figure 4.12.  Predicted extent of arsenic contamination due to the ASARCO Smelter

(Department of Ecology Facility/Site Identification System).

The strong correlation with arsenic (As) suggests that the contributions that make up this source 

may also consist of crustal-laden remnants of the now-defunct ASARCO Tacoma Smelter 

previously located in the town of Ruston, WA. Figure 4.12 shows the Department of Ecology’s 

prediction of the extent of arsenic contamination as a result of the now-defunct ASARCO 

Tacoma Smelter. The map shows an estimate of the highest arsenic levels likely to be found in 

an area, based upon a relatively small number of samples. It can be observed that high arsenic 
32 

concentrations are predicted to occur in the southwest, northwest and northeast quadrants relative 

to the sampling site. 

We confirmed through a CPF analysis of the measured ambient air arsenic concentrations that 

there were no significant arsenic contributions coming from the southeast. The CPF for the 

measured arsenic concentrations (CPF not shown here) showed that most of the arsenic came 

from the north-northwest and southwest directions. Thus, the southeast direction shown by the 

CPF for industrial emissions (Figure 4.11) may suggest the existence of industrial point sources 

in that direction. 

An on-going regional atmospheric deposition study has reported generally higher fluxes of 

metals (As, Cu, Pb) and total polycyclic aromatic hydrocarbons (PAHs) within Tacoma 

Commencement Bay (located about 5 miles northeast of the sampling site) than in any of the 

other Puget Sound area sites being investigated (Brandenberger et al., 2009). While the true 

sources of the observed heavy metals in our study are unknown, it is likely that background 

contamination from the now-defunct smelter, coupled with freshly-emitted point and non-point 

emissions, are responsible for the observed metallic concentrations. 

Industrial emissions accounted for about 6% of the overall apportioned mass. This value is in 

agreement with published contributions from other sites within Washington State. For example, 

Maykut et al. (2003) reported an average contribution of 7% from “industry” in Beacon Hill 

(Seattle). 

4.5 Fugitive Dust 

Fugitive dust was characterized by an abundance of crustal elements, aluminum (Al), calcium 

(Ca), iron (Fe) and silicon (Si). It accounted for about 80% of the apportioned aluminum and 

82% of the silicon (Figure 4.13). 

Fugitive dust accounted for about 4% of the total apportioned mass. The CPF (Figure 4.14) 

suggests significant contributions from re-suspended dust from highway and local traffic. 

Because there is a gravel operation located to the southwest of the sampling site, it is also likely 

that the southwesterly direction indicated by the CPF indicates contributions from that facility. 

Fugitive dust was poorly correlated with total PM2.5 mass (r2

 = 0.19) indicating that its influence 

on the measured total mass was not significant. As expected, fugitive dust emissions were 

highest in the summer and lowest in the winter. 
33 

Species

PM2.5_

Mass

Nitrate

Sulfate

EC

OC

BC

Al

NH4+

As

Br

Ca

Cl

Cu

Fe

Pb

Mg

Mn

Ni

K

Si

Na+

Sr

Ti

V

Zn

Concentration of Species (g/m3

) 0.0001

0.001

0.01

0.1

1

Percentage of Species

0

20

40

60

80

100

Concentration

Percentage

Figure 4.13.  PMF profile for fugitive dust.  Signature species are shown with blue bars. 

   

4.6 Oil Combustion and 

Ships 

Oil combustion and ships represent only about 1% 

of the apportioned mass. They accounted for about 

70% of the apportioned nickel (Ni) and 72% of 

vanadium (V). Ni and V are known markers for oil 

combustion and emissions from ships. Although 

the PMF profile (Figure 4.15) indicates the 

presence of significant concentrations of many 

species in emissions associated with this source, 

correlations were generally weak between the oil 

combustion and ships source with those species. 

Strong correlations were observed with Ni (r2

 = 

0.86) and V (r2

 = 0.92), while moderate 

correlations were found with sulfate (r2

 = 0.46), 

calcium, Ca (r2 = 0.58) and iron, Fe (r2 = 0.51). A 

relatively weak correlation was observed with total 

Figure 4.14.  CPF for fugitive dust. 

0.0 0.4 0.8

N

NW

W

SW

S

SE

E

NE
34 

PM2.5 mass (r2 = 0.29), which suggests a relatively insignificant influence by oil 

combustion/ships on the measured total mass concentrations. 

Species

PM2.5_

Mass

Nitrate

Sulfate

EC

OC

BC

Al

NH4+

As

Br

Ca

Cl

Cu

Fe

Pb

Mg

Mn

Ni

K

Si

Na+

Sr

Ti

V

Zn

Concentration of Species (g/m3

) 0.0001

0.001

0.01

0.1

1

Percentage of Species

0

20

40

60

80

100

Concentration

Percentage

Figure 4.15.  PMF profile for oil combustion/ships.  Signature species are shown with blue

bars. 

The CPF (Figure 4.16) suggests contributions from 

multiple directions probably as a result of 

scattering of residual oil combustion sources as 

well as wind meandering close to the sampling 

site. It can also be observed that emissions 

originating from the Port of Tacoma may have 

impacted the measured mass concentrations at the 

sampling site. The southwesterly direction shown 

by the CPF may indicate ship emissions being 

blown offshore by southwesterly winds. The CPF 

also indicates the presence of significant oil 

combustion contributions from the northwest and 

southwest directions. 

The contribution from oil combustion/ships 

resolved in this analysis is less than the value 

reported from other study sites in Washington 

State. Kim and Hopke (2008a) reported average oil 

Figure 4.16.  CPF for oil combustion and

ships.

0.0 0.4 0.8 1.2

N

NW

W

SW

S

SE

E

NE
35 

combustion contributions of 4-6% in five sites within the Seattle area. The authors suggested 

that a major portion of the oil combustion emissions may be attributable to ships operating 

within the Port of Seattle. In a related study, Kim and Hopke (2008b) reported an oil combustion 

contribution of 19% in the Olympic Peninsula, primarily attributable to ship emissions. 

The averaged contribution from oil combustion/ships generally peaked in August or September. 

Although shipping activity within the Port of Tacoma fluctuates throughout the year, increased 

shipping activity in late summer/early fall was reported by the Pacific Maritime Association 

during the study period (PMA, 2009). Thus, shipping activity within the Port of Tacoma may 

have been responsible for much of the contributions apportioned to this source category. 

4.7 Other Sources 

Other emission sources resolved in this analysis include fireworks, freshly-emitted sea salt and 

processed (aged) sea salt. These sources cumulatively accounted for about 6% of the 

apportioned mass, with individual contributions of 2%, 4% and 0.3%, respectively. Neither of 

these sources had a meaningful correlation with PM2.5 mass indicating that their influence on the 

measured total mass concentrations was insignificant. Figure 4.17 shows PMF source profiles 

for fireworks, freshly-emitted sea salt and aged sea salt. The corresponding CPFs are shown in 

Figure 4.18. 

The primary identifying species for fireworks are strontium (Sr) and potassium (K). Fireworks 

were highly correlated with copper, Cu (r2 = 0.89), magnesium, Mg (r2 = 0.71), potassium, K (r2

= 0.89), and strontium, Sr (r2 = 0.86). The time series for the resolved fireworks source (Figure 

4.19) confirmed that fireworks displays associated with Independence Day celebrations were the 

primary contributors to the resolved fireworks emissions. The CPF for fireworks (Figure 

4.18(a)) indicates that fireworks emissions mostly originated from the northwest and southwest 

of the sampling site. A literature review revealed no source apportionment data for fireworks, 

presumably because most source apportionment studies have typically omitted fireworks data 

from their analyses. 

Fresh sea salt was identified by the predominance of the chloride ion (Cl-

), sodium ion (Na+) and 

magnesium (Mg). About 97% of the Cl-

 was apportioned to freshly-emitted sea salt. The CPF 

(Figure 4.18(b)) is consistent with sea salt emissions that are blown offshore by strong 

predominantly southwesterly winds. 

Aged sea salt is characterized by high sodium (Na+

), magnesium (Mg) and calcium (Ca) 

concentrations, accompanied by no chloride (Cl-

) or other halogens. In this case, the chloride has 

been substituted by nitrate (NO3

-

) through atmospheric reaction with nitric acid (HNO3). In 

contrast to fresh sea salt, aged sea salt contributions were highest in the summer probably 

because aged sea salt particles have been reported to associate with sulfate particles (Zhuang et 

al., 1999). 
36 

(a) Fireworks

Species

Species

PM2.5_

Mass

Nitrate

Sulfate

EC

OC

BC

Al

NH4+

As

Br

Ca

Cl

Cu

Fe

Pb

Mg

Mn

Ni

K

Si

Na+

Sr

Ti

V

Zn

0.0001

0.001

0.01

0.1

1

0

20

40

60

80

100

(c) Aged Sea Salt

Species

PM2.5_

Mass

Nitrate

Sulfate

EC

OC

BC

Al NH4+

As

Br

Ca

Cl

Cu

Fe

Pb

Mg

Mn

Ni

K

Si Na+

Sr

Ti

V

Zn

0.0001

0.001

0.01

0.1

1

0

20

40

60

80

100

(b) Fresh Sea Salt

Concentration of Species (g/m3

) 0.0001

0.001

0.01

0.1

1

Percentage of Species

0

20

40

60

80

100

Figure 4.17.  PMF profiles for (a) fireworks, (b) freshly‐emitted sea salt, and (c) processed

(aged) sea salt.  Bars are concentrations and a red cross in the chart is the percentage of that

species apportioned to that source.  Non‐gray colors in the fireworks profile represent typical

colors of those elements in a typical fireworks display.  Blue color bars in other source profiles

indicate “marker” species.
37 

Figure 4.18.  CPFs for (a) Fireworks, (b) Freshly emitted sea salt, and (c) Processed ("aged")

sea salt. 

Fireworks

Sample Date

1/1/06   7/1/06   1/1/07   7/1/07   1/1/08   7/1/08   1/1/09   7/1/09  

Mass Concentration (g/m3

) 0

1

2

3

4

5

6

7

Figure 4.19.  Time series for fireworks.  The contributions were dominated by Independence

Day fireworks displays. 

The contribution from freshly-emitted sea salt is comparable to contributions found in the 

technical literature, but the contribution estimate for aged sea salt is lower than the published 

contributions. For example, Kim and Hopke (2008a) reported aged sea salt contributions of 4-

12% and fresh sea salt contributions of 2-4% in five sites within the Seattle area. Additionally, 

Kim and Hopke (2008b) reported aged sea salt contributions of 5-10% and fresh sea salt 

contributions of 2-10% within the Olympic Peninsula, WA, Portland, OR and Anchorage, AK. 

Wu et al. (2007) found a “marine” source (approximately 1-2% of the total mass) and an “aged 

sea salt” source (approximately 5-6% of the total mass) in Beacon Hill (Seattle). 

(a) Fireworks

0.0 0.4 0.8 1.2

N

NW

W

SW

S

SE

E

NE

(c) Aged Sea Salt

0.0 0.4 0.8

N

NW

W

SW

S

SE

E

NE

(b) Fresh Sea Salt

0.0 0.4 0.8

N

NW

W

SW

S

SE

E

NE
38 

5.0 CONCLUSION 

By analyzing the measured elemental species concentrations in Tacoma, Washington, this study 

has determined that the most significant sources of ambient fine particles in the nonattainment 

area are wood smoke, gasoline vehicles, diesel vehicles, fugitive dust, fireworks, secondary 

nitrate, secondary sulfate, oil combustion and ships, arsenic-rich industrial emissions, freshlyemitted sea salt, and processed (aged) sea salt. On average, these sources explained more than 

90% of the measured average mass concentration. 

The single most dominant source of fine particles in the nonattainment area is wood smoke. We 

believe that the resolved wood smoke contributions represent combined contributions from wood 

stoves and other wood-fired home heating devices. Wood smoke contributions had the strongest 

relationship with the measured total PM2.5 mass which illustrates that wood smoke contributions 

were responsible for the majority of the observed trend in total PM2.5 mass concentrations. The 

source apportionment results showed that wood smoke accounts for nearly one-half of the total 

measured PM2.5 mass on most days. The wood smoke contributions were most significant during 

the winter and on days when the measured daily PM2.5 mass concentration exceeded 20 or 30 

μg/m3

. 

Wind direction and wind speed were found to be a factor in the observed wood smoke 

concentrations. Accordingly, the most significant sources of wood smoke appeared to be those 

that were located to the southerly and southeasterly directions with respect to the sampling site. 

We observed a strong presence of secondary aerosol in the study area. About 25% of the 

ambient PM2.5 mass concentrations were attributable to secondary aerosol. Further analyses 

identified the resolved secondary aerosol as locally-generated aerosol from multiple local 

sources of nitrogen oxides, sulfur dioxide and volatile organic compounds. This indicates that a 

complete emissions control strategy would also consider evaluating local sources of precursors 

for secondary aerosol, including mobile and non-mobile sources of nitrogen oxides, sulfur 

dioxide and volatile organic compounds. 

We attributed a small but significant portion of the observed PM2.5 mass to industrial emissions 

(roughly 6%). Industrial emissions were rich in arsenic, lead, and zinc. Further analyses 

revealed that these emissions might be a combination of background/re-suspended emissions 

from the now-defunct ASARCO Smelter and emissions from active major industries located 

within the Port of Tacoma. 

Finally, our results show that average contributions attributable to wood smoke and local 

industry appear to have declined approximately linearly, on an annual basis, since the year 2006. 

We did not see a similar decline with other sources or with the 98th percentile wood smoke 

contributions. Because of the limited time period, it was not possible to verify whether or not the 

observed decline constitutes a trend. Nonetheless, it is possible that existing control strategies 

for wood smoke and industrial sources may be creating measurable annual emissions reductions. 

A follow-up detailed study which includes several years of ambient data may be necessary to 
39 

determine the true effect of existing control strategies on annual PM2.5 emissions. However, in 

order to reduce ambient fine particle mass concentrations on specific days, a combination of 

targeted source- and location-specific emissions reductions and careful forecasting of 

meteorological conditions may be required. 
40 

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Acronyms and Abbreviations 

Al Aluminum 

As Arsenic 

BC Black Carbon 

Br Bromine 

Ca Calcium 

Cl Chlorine 

Cl-

 Chloride Ion 

Co Cobalt 

CPF Conditional Probability Function 

Cr Chromium 

Cu Copper 

EC Elemental Carbon 

Ecology Washington State Department of Ecology 

EPA United States Environmental Protection Agency 

Fe Iron 

FRM Federal Reference Method 

Hg Mercury 

K Potassium 

K+

 Potassium ion 

Mg Magnesium 

Mn Manganese 

N Number of valid samples 

Na Sodium metal 

Na+

 Sodium Ion 

NAAQS National Ambient Air Quality Standard 

NH4

+

 Ammonium ion 

Ni Nickel 

NO3

-

 Nitrate ion 

NOx Nitrogen oxides 

OC Organic Carbon 

Pb Lead 

PM2.5 Particulate matter with an aerodynamic diameter less than 

or equal to 2.5 micrometers 

PMF Positive Matrix Factorization 

PSCAA Puget Sound Clean Air Agency 

Rb Rubidium 
43 

S Sulfur 

Se Selenium 

Si Silicon 

SIP State Implementation Plan 

SO2 Sulfur dioxide 

SO4

2- Sulfate ion 

Sr Strontium 

STN Speciation Trends Network 

Ti Titanium 

TOR Thermal Optical Reflectance 

TOT Thermal Optical Transmittance 

V Vanadium 

Zn Zinc 

μg/m3

 Micrograms per cubic meter of ambient air