# Sources of Fine Particles in the Wapato Hills-Puyallup River Valley

PM 2.5 

# 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 Offi ce, 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 anothe r 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 PM 2.5 

# Nonattainment Area

by

David Ogulei, PhD, PE

Air Quality Program Washington State Department of Ecology Olympia, WashingtonThis page is purposely left blanki 

# Table of Contents

Page

List of Figures and Tables................................................................................................... i i

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

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

Acknowledgements ............................................................................................................ i v

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 Meas ured 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 Nitr ate 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 ............................................................................................42ii 

# List of Figures and Tables

Page 

## Figures

Figure 1.1. Wapato Hills-Puyallup Ri ver Valley nonattainment area. . .............................2

Figure 2.1. Location of the sampling site wi th 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 unc ertainties against measured concentrations for elemental carbon (EC) and organic carbon (OC). ............................................................9

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

Figure 3.1. Percentage contri butions, 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 PM 2.5 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 PM 2.5 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. .................................................27iii 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 fo r secondary aerosol and PM 2.5 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 c ontamination 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 fo r oil combustion/ships...........................................................34

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

Figure 4.17. PMF profiles for fireworks, fres hly-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 PM 2.5 mass concentrations and average wind direction on days when the FRM or STN samplers reported ambient mass concentrations exceeding 30μg/m 3 ..............................................................................................................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 av eraged over indi vidual years. .....................................17

Table 3.3. Contributions on days when the observed PM 2.5 mass concentration was greater than or equal to 30 μg/m 3 . ....................................................................19iv 

# Acknowledgements

This report represents a collec tion of significant cont ributions 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 contri bution 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 Programv 

# Executive Summary

Purpose

This report provides information to policy makers about the sources of fine particle air pollution in Washington’s Wapato Hills-Puyall up River Valley nonattainment area.



### Background information

States monitor air quality in different areas to fi nd 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 ba sed on a state's recommendation:

 Attainment (meeting a standard)

 Nonattainment (failing to meet a standard)

 Unclassifiable (not enough in formation to designate)



### Fine particle pollution in the Wap ato Hills-Puyallup River Valley area

On November 13, 2009, EPA formally designated pa rt of Pierce County in Washington (the Wapato Hills-Puyallup River Valley area) as nonattainment. The de signation took effect December 14, 2009. This area is nonattainment for the 2006 National Ambient Air Quality Standard (NAAQS) for 24-hour PM 2.5 . PM 2.5 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 pollu tion, nonattainment design ations 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. Th e SIP is a plan for bringing the area back into attainment as quickly as possible. The SIP mu st 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 PM 2.5 

### sources

To determine how to control a pollutant, th e Washington Department of Ecology (Ecology) needs to understand the pollutant’s sources. Ecology identified the main sources of PM 2.5 in the Wapato Hills-Puyallup River Valley area by us ing receptor modeling techniques to analyzevi existing PM 2.5 speciation data. The data were collected from Janua ry 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 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%)

 fireworks (2%)

 oil combustion and ships (1%)

 fresh and processed sea salt (4%)



### Conclusions

Wood smoke is the single mo st important source of PM 2.5 in the Wapato Hills-Puyallup River Valley nonattainment area. Wood smoke c ontributed more than half of the PM 2.5 on most days in the winter, as well as on days when PM 2.5 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 si gnificantly to PM 2.5 in this area, because the area surrounding the sampling site is classi fied as an Urban Growth Area (UGA), where outdoor burning is prohibited.

Although wood smoke emissions were res ponsible for the majority of the PM 2.5 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 98 th
percentile values of the 24-hour wood smoke concentrations.1 

# 1.0 INTRODUCTION

Numerous health effect studies have shown that exposure to elev ated concentrations of fine particles that are less than 2.5 micr ometers in aerodynamic diameter (PM 2.5 ) causes adverse health effects, including cardi ovascular and respiratory effect s in sensitive populations (e.g., Pope, 1996; Lewtas, 2007; Huang and Ghio, 2009). Most PM 2.5 found in the ambient air results from direct emissions from stationary and m obile emission sources, and secondary particles formed through atmospheric reactions.

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

the 24-hour PM 2.5 national ambient air quality standa rds (NAAQS) from 65 micrograms per cubic meter (μg/m 3 ) to 35 μ g/m 3 , but retained the annual PM 2.5 NAAQS at 15 μ g/m 3 (71 FR 61144). The revised 24-hour PM 2.5 standard became effective on December 18, 2006. To attain this 24-hour standard, the 3-year average of the 98 th percentile of 24-hour PM 2.5 concentrations at each population-oriented monitor within an area must not exceed 35 μg/m 3

.

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

those areas that meet or violate the 2006 24-hour PM 2.5 NAAQS (74 FR 58688). Through that Federal Register notice, EPA formally desi gnated 31 areas including part of Pierce County, Washington, as nonattainm ent of the 2006 24-hour PM 2.5 NAAQS, in accordance with the requirements of the federal Clean Air Act Sectio n 107(d). The final designations are effective December 14, 2009, and are based upon 2006 through 2008 monitored PM 2.5 data.

Figure 1.1 shows an outline of Pierce County’s PM 2.5 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 PM 2.5 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 PM 2.5 NAAQS in Washington State.

As a result of this designation, Washington Stat e (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 e xpeditiously as practicable (74 FR 58688). The SIP submission must include an attainment demonstration showing that the application of selected reasonably available control measures and c ontrol technologies to specific source types will lead to a ttainment of the 2006 24-hour PM 2.5 NAAQS as soon as possible. The exact requirements of the SIP are unknown b ecause EPA has not published implementation

instructions for the 2006 PM 2.5 NAAQS.

Preparation of an effective SIP begins with pr oper identification and pr ioritization of emission sources to be controlled. In general, emissi on sources found to be directly or indirectly contributing to the highest mass concentrati ons in the nonattainment area should be given priority. Useful information fo r identifying the most important em ission sources to be controlled can be obtained through analysis of speciated PM 2.5 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 PM 2.5 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 th e most important source s contributing to the

measured mass concentrations within the designated nonattainment area. Existing PM 2.5 speciation data collected using the Speciation Trends Network (STN) sampling protocols in South Tacoma, Washington, from January, 2006 to May, 2009 were analy zed in this study.

The source apportionment analysis was conducted using Positive Matrix Factorization (PMF; Paatero and Tapper, 1994; Paatero, 1997) a nd 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 collect ed between January 11, 2006 and May 7, 2009 at 7802 South L Street in Tacoma, Pierce County, Wa shington. The sampling site (Latitude 47.1863 o

, Longitude -122.4515 o ) is operated by Puget Sound Clean Air Agency (PSCAA) and is located approximately 4 miles south of downtown T acoma. The area surrounding the site is a predominantly low income residential neighborhood. A major north-south fr eeway (Interstate-5) runs less than one mile west of the site (Figur e 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 i ndustrial facilities thatare 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 inve ntory 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 ai r quality data further found that the highest total PM 2.5 mass concentrations in the area are observed exclusively in the winter, with carbonaceous PM 2.5 being the dominant chemical component on days with the highest PM 2.5 mass concentration (EPA, 2008b).

Twenty-four-hour PM 2.5 speciation measurements were ma de every sixth day according to Speciation Trends Network (STN) protocols. There were a total of 74 species measured, including total PM 2.5 mass. Ionic species (i.e., ammoni um, sulfate, nitrate, sodium, and potassium ions) were analyzed by ion chromatogr aphy 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 meas urements 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 speci ation 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), mo lybdenum (Mo), niobium (Nb), phosphorus (P), samarium (Sm), scandium (Sc), silver (Ag), tantal um (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 su lfur (S), potassium ion (K + ) or sodium (Na) in this study because they were highly correlated with sulfate (SO 4 2- ), potassium (K) and sodium ion (Na + ), respectively, suggesting that their ambient co ncentrations were being double- counted in the data set. Additionally, chromium (C r), cobalt (Co), mercury (Hg), rubidi um (Rb), and selenium (Se) had signal-to-noise ratios of less than or equa l 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.psclea nair.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 sec ond (m/s), and generally approached from the south-southwest. We observed only three occurrences between Ja nuary 1, 2006 and May 7, 2009, when the winds exceeded 4 m/s, meaning that winds were generally mild within the study5 

## Wind Speed (m/s) 0

‐ 1 1

‐ 2 2

‐ 3 3

‐ 4 >=4WindDirection(DegreesfromNorth)



### 0 45 90 135 180 225 270 315 360 area. On days when the measured to tal mass concentrations exceeded 30 μg/m 3 , winds blew from the southeast approximately two-thirds of th e 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 25 th and 75 th 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 10 th and 90 th percentiles, respectively. Any

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

On 5/12/07, STN began using revised thermal optic al transmittance and reflectance protocols for organic carbon (OC) and elementa l 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 an d 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., EC 1, EC2, EC3, OC1, OC2, OC3, OC4 and OP) because

EC fractions were not meas ured prior to 5/12/07.6 Table 2.1. Total PM 2.5 mass concentrations and average wind direction on days when the

FRM or STN samplers reported ambient mass concentrations exceeding 30 μg/m 3

Sample Date FRM Mass

Concentration (μg/m 3 ) STN Mass

Concentration (μg/m 3 ) 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 PM 2.5 mass concentration measured by the STN speciation sampler was 9.66 μg/m 3 (N = 191 samples) while the average total mass concentration measured by the federal reference method (FRM) during this period was 9.62 μg/m 3 (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/m 3 ) b

Average Standard

Deviation Minimum Maximum

PM 2.5 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 (NH 4 + ) 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/m 3 (Table 2.1) indicates that the STN speciation sampler closely replicated the FR M’s values. In fact, the mass concentrations obtained by the STN sampler tended to be sligh tly higher than the co rresponding FRM values.

Note that STN data were obtaine d 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 PM 2.5 mass measurement, the two measuremen ts were strongly correlated, with an overall correlation coefficient (r 2 ) of 0.95 (N = 186 samples).

Except for organic carbon, all of the speciation data used in th is study were blank-corrected using the provided field blanks . For organic carbon (OC), we corrected for OC sampling artifacts by plotting the uncorrected to tal OC against the measured total PM 2.5 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 fire works 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 da ys were 7/4/2006, 12/31/2006, 7/5/2007, 7/5/2008 and 1/1/2009. Since the high total mass concentr ation on 12/31/2006 may have affected the PM 2.5 design value for that year, we therefore found it im portant to ascertain the exact contribution of

fireworks to the measured ambien t concentrations in the area.

Table 2.2 provides a statistical summary of the fi nal species used in th e source apportionment analysis. For completeness and data comparison, Table 2.2 also shows the statistics for total

PM 2.5 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/m 3 , respectively), it is safe to conclude that, th e average source contributions resolved in this study would not be expected to substantially ch ange 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 discusse d 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 th at 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 la test version of EPA’s formulation of the PMF model (EPA PMF 3.0) was used in this st udy. 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 adequa tely explain the observations at the sampling site. The resolved9 sources are identified and validated through information known a priori about the airshed being studied. The modeling procedures recommende d by EPA (2008a) were followed in this study.

2.2.1.1 Data Treatment for PMF Analysis

A PMF analysis requires extensiv e 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 bla nk corrections must be appropriately substituted with non-negative values. Any missi ng 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/m 3 ) 012345TotalEC(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/m 3 

### ) 0 5 10 15 20 25 30TotalOC(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 in terpolated 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 fo r aluminum (Al), values reported to be below detection were replaced with one-h alf of the sample detection limit for that species. We then

calculated the associated uncertain ties for such data points as tw ice the estimated concentration. In accordance with the air quality monitoring advisory for Al (< http://vista.cira.colostat e.edu/improve/Data/QA_QC/Advisory/da0010/da0010_Almdl.pdf >), if Al was reported below the detection limit, we cal culated 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 a nd Si values were repor ted. The corresponding

uncertainties were calculated as four times the estimated concentrations.

For OC and EC, since no measurement uncertain ties were reported under the revised TOT or TOR protocols, the required measurement un certainties were estimated by regressing the reported uncertainties against the ambient concentrations de termined under the old TOT and TOR protocols. Based on the regression analysis , the required uncertainties were calculated using the following equations:

For EC: EC Unc ij = (0.058)×(EC Conc ij ) + 0.225 (1)

For OC: OC Unc ij = (0.069)×(OC Conc ij ) + 0.199 (2)

where Unc ij is the calculated (estimated) measuremen t error for species j and sample i; Conc ij is the observed species concentration; and the cons tants are regression coefficients (Figure 2.3).

Because no measurement uncertainties were pr ovided for black carbon (BC), the required uncertainties were calculated using the equati on reported by Ogulei et al. (2006), with the arbitrary constant set to 0.1. An empirical extra mode ling uncertainty of 20% was added to the estimated uncertainties of all sp ecies to account for unc orrected random and systematic errors. In addition, PM 2.5 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 interpre tability of the model results. The experimental residual sum of squares, Q, should be approximate ly equal to its theoretic al value (EPA, 2008a). The theoretical value of Q was approximated by th e total number of data points minus the total number of degrees of freedom, that is,

Q theoretical = (number of species × nu mber 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 a ssociated with ambient

concentrations that exceed an operationally-defined threshold. In this study, the threshold was defined as the highest 25% (i.e., 75 th percentile) of the resolved c ontributions. We calculated the CPF for each wind sector using the following equation:

(4)

where Δθ is the wind sector (20 o each); m Δθ is the number of contri bution occurrences from wind

sector Δθ in which the resolved contributions fell in the upper 25% of a ll contributions; and n Δθ is the total number of contribu tion 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 in itially 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 PM 2.5 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 measur ed 24-hour averaged wind speeds in the calculations.

A plot of the overall wind pr ofile 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 indi vidual species to determine the directions associated with high measured concentrations for those species (CPFs not shown here). Those CPFs showed similar potential source locations associ ated with the measured organic carbon (OC) and black carbon (BC) concentrations. There were also similariti es between potential source locations associated with the measured nickel (Ni) and va nadium (V) concentrations. Total PM 2.5 mass emissions appeared to come from multiple directio ns 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 NE12 

# 3.0 SOURCE APPORTIONMENT RESULTS

3.1 Criteria for Source Identification

In this study, we defined the “correct” soluti on as the optimum number of sources that corresponded to acceptable least squares and regre ssion statistics, and physically interpretable source profiles. The solution was checked fo r stability through a bootstrapping technique. Bootstrapping is a re-sampling t echnique in which a new data set having the same dimensions as the original data set is randomly generated (sampl ed) 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 el even 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 profile s 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 summ er from those that typically peak in the
winter. For example, secondary nitrate a nd 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 peak ed 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 in formation on source contributions enabled

identification of certain s ources, such as wood smoke.

We identified the eleven emissi on sources as: wood smoke, gasolin e vehicles, diesel vehicles, fugitive dust, fireworks, secondary nitrate, sec ondary 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 PM 2.5 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/m 3 ) resolved in this analysis. The contributions have been averaged over th e 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 contri bution of 4.0 μg/m 3 , or about 45% of the total apportioned PM 2.5 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 pr ovided later in the report.

Table 3.1. Average contributions resolved by the PMF model.

Resolved 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
Aged Sea Salt 0.03 0.02



## 3.3 Seasonal Contributions

To better understand the contributio ns of individual s ources, 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 seas ons 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 sour ces. Specifically, wood smoke, fireworks, secondary nitrate, motor vehi cles, industrial emissions , and fresh sea salt exhibited pronounced fluctuations in average cont ributions between two or more seasons. Most of the fugitive dust and firework s contributions occurr ed in the summer, while most of the wood

smoke contributions oc curred in the winter.

Average contributions from motor ve hicles and industry were highest in the winter probably due to the low atmospheric mixing height in the wi nter. Also, for motor vehicles, the increased

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

the winter.

The differences in contributions between heatin g 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 discusse d later in the report, this observation is not surprising given the reported he avy reliance of the study area’s local residents on wood heating as the primary source of heat in the winter.15 

### 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 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/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

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 aver age concentrations were generally less pronounced than seasonal aver age contributions. As show n in Figure 3.4, the observed differences between weekday and weekend concentr ations for some of th e sources were within experimental error suggesting that daily human ac tivities were not the exclusive explanation for

the observed contributions from those sources. In fact, this analysis found that weather conditions also played a signifi cant role in the observed PM 2.5 concentrations on certain days.



### Source Type W o

o

d S m

o

k

e

S

e

c

. N i t

r a

t

e

G

a

s

o

l i n

e

V

e

h

i c l e

s

S

e

c

. S u

l f

a

t

e

A

r s

e

n

i c

‐ r i c h I n

d

u

s t

r i a

l

F

r e

s h S e

a S a

l t

D

i e

s

e

l V e

h

i c l e

s

F

u

g

i t

i v

e D u

s t

O

il C o

m

b

u

s t i o

n /

S

h

i p

s

F

i r e

w

o

r k

s

A

g

e

d S e

a S a

l tAverageConc.(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 not iceably higher than weekend average contributions for motor vehicles, secondary nitrate and fugitive dust. We ekend average contributions were higher than weekday values for secondary sulfate, industr ial emissions and oil combustion/ships. The weekday-weekend difference was within experi mental error for the other sources, including

wood smoke.17 

## 3.5 Annual Contributions

Table 3.2 shows annual average cont ributions 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 av erage contributions for wood smoke and industry appeared to annually decrease from the 2006 levels. This annual decline appeared to ex ist in absolute terms (concentrations) and relative term s (percentage). There was no i ndication of a d ecline in annual average contributions from other source categories.

Note that while there is a no ticeable decline in th e annual averaged wood smoke emissions, a longer time period is needed to establish a trend in contributions. Moreover, the 98 th percentile

values of the 24-hour concentrations di d not show a similar decline. The 98 th percentile concentrations for wood smoke were 23, 24 and 13 μg/m 3 for 2006, 2007 and 2008, respectively,

while the 98 th percentile concentrat ions for motor vehicles were 2.3, 1.9 and 1.2 μg/m 3 for 2006, 2007 and 2008, respectively.

Table 3.2. Source contributions averaged over individual years

.

Sample Year 2006 2007 2008 2009*

Measured PM 2.5 Mass (μg/m 3 ) 9.4 10.8 8.3 10.4

Average Concentrations (μg/m 3 ) (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 co ntributions for four different ambient PM 2.5 mass concentrations. The resolved average contri butions on days when the measured total PM 2.5 mass concentration was or exceeded 30 μg/m 3 are shown in Table 3.3. As indicated by Figure 3.5 and Table 3.3, wood smoke was responsible for at leas t one-half of the obser ved total mass on most days when the measured total PM 2.5 mass concentration exceeded 30 μg/m 3 . On those days

when the measured total PM 2.5 mass concentration was or exceeded 30 μg/m 3 , the wood smoke proportion reached 72% at one time, on 11/1/200 6. The corresponding total mass concentration18 was 33 μg/m 3 . Table 2.1 (Section 2.1) indicates that the average wind direction on that day (11/1/2006) was southeast.

Wood smoke contributions also aver aged about 52% when the total PM 2.5 mass concentration exceeded 20 μg/m 3 , which is a healthy air goal establishe d by the Washington State Department of Ecology (Figure 3.5). On two occasions when the total PM 2.5 mass concentration exceeded 20 μg/m 3 during the study period, the wood smoke contri bution was 72% of the daily total mass.



### Source W

o

o d

Sm ok e Sec. N

i t rate

G

a

s ol

in

e

Ve

h

icl

e

s Sec. Sulfate

Arsenic ‐ ri

ch Ind u st

ri

al

Fre sh S

e

a

Sal t

D

i e sel Ve

h

icl

e

s

F

u gitive D

u st

Oil Combustion/Ships

F i re

works 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 3.5. 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%, da ily-averaged winds blew from the southeast or southwest approximately 70% of the time. Th is 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 concen trations on those days.19 Besides wood smoke, motor vehicles and secondar y aerosol (secondary nitrate plus secondary sulfate) also contributed signi ficant amounts to the measured to tal mass on most days when the

PM 2.5 ambient concentration was or exceeded 30 μg/m 3 . Motor vehicles c ontributed about onequarter of the total measured mass on Martin Luth er King Jr. Day (1/19/2009). An analysis of

the measured PM 2.5 data showed that there was a marked increase in ambient concentrations of multiple species on that day. The total PM 2.5 mass was 38 and 39 μg/m 3 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 eas t. Motor vehicle contributions were also significant on 12/31/2006 when the measured to tal mass concentration was the highest (54 μg/m 3 ).

Secondary aerosol (secondary nitrate plus secondar y sulfate) was responsib le for about one-third of the total mass on 11/8/2007, and about one-fifth of the tota l 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 PM 2.5 mass concentrations. However, furthe r analysis is needed to better characterize the sources of secondary aerosol within the study area.

Table 3.3

. Contributions on days when the observed PM 2.5 mass concentration was greater

than or equal to 30 μg/m 3

.

Sample Date Measured PM 2.5 Mass (μg/m 3 ) 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 indi vidual sources on the measured ambient mass concentrations were typi cally exacerbated by air stagnation events. For example, there appeared to be an air st agnation 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 PM 2.5 concentration on this day was 54 μg/m 3 . Because a significant

number of wood smoke sources were located to th e southeast of the sampli ng site, the effect of those sources may have been exacerbated by stagnation and inversi on events on 12/31/2006. Similarly, the effect of fireworks on 12/31/2006 ma y have been more significant than had there been no stagnation or meteorologi cal inversion on that day.

In a previous analysis, the fastest winds fr om the south-southwest quadrant were found to correspond to the lowest PM 2.5 mass concentrations while the slowest winds from the east to southeast corresponded to the highest PM 2.5 concentrations (EPA, 2008b). This illustrates that stagnations and inversions, such as those typically prevalent in Tacoma in the winter and fall, are additionally responsible fo r the area’s elevated PM 2.5 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 a ny signal day was 72% of the daily PM 2.5 mass concentration. The maximum wood smoke cont ribution (72%) was obser ved on two occasions: 11/1/2006 and 12/8/2007, when the measured daily PM 2.5 mass concentrations were 33 and 21 μg/m 3 , respectively. This analysis was not able to distinguish between the various types of wood smoke sources. However, based on emissions i nventory and census information collected by Ecology and PSCAA, we believe that the wood smoke contributions resolved in this analysis represent combined contributions from wood stove s 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 Grow th Area (UGA) where outdoo r 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 abundanc es of organic carbon (OC), elemental carbon (EC), black carbon (BC) and potassium (K). Ab out 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 consiste nt with abundances re ported in the source apportionment literature (e.g., Kim and Hopke , 2008a). The wood smoke concentrations correlated very well with BC (r 2 = 0.89), total OC (r 2 = 0.97), total EC (r 2 = 0.79) and total PM 2.5 mass concentrations (r 2 = 0.92).21 Species

P M

2

. 5 _

M

a s s

N i t r a t e

S u l f a t e

E C

O C

B C A l

N H 4 + A s B r

C

a C

l

C

u F

e

P b

M

g

M

n N

i K S i N

a + S r T

i V Z nConcentrationofSpecies(g/m3)

0.0001

0.001

0.01

0.1

1 10PercentageofSpecies

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 contributio ns of 7-37% in various areas near Seattle. Kim and Hopke (2008a) reported a significant difference in wood smoke contributions be tween the residential

and downtown areas of Seattle. In their study, wood smoke accounted for about 31% of the apportioned mass in Lake Fore st Park (residential) and

7-10% in the commercial si tes (Olive St., Beacon Hill, Duwamish).

Conversely, comparable average wood smoke

contributions to this stud y 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

PM 2.5 mass when the measured total mass exceeded 30 μg/m 3 (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 NE22 southeast of the sampling site. This is consistent with suspected locations of residences that rely

on wood stoves for residentia l heating (Figure 4.3).

The CPF also indicates contributi ons from the southwest, which is also the direction of the overall wind profile (Figure 2.4). This may sugges t that the wind direction was also a factor in the observed contributions at the receptor mean ing that any wood smoke sources located to the southwest of the sampling site had an influence on the PM 2.5 mass concentrations recorded at the sampling site. However, as stated above, wood sm oke sources located to the south and southeast of the receptor were the most significant.

Figure 4.4 shows the time series of wood sm oke 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 PM 2.5 mass peaked if wood smoke peaked suggesting that wood smoke lik ely 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 Monitor23 

### BC (Aethalometer/PSCAA)MassConcentration(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 PM 2.5 

### (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 PM 2.5 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 vehi cles. While this analysis did not utilize carbon fractions to aid in the separation of gasoline and diesel contri butions, 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 gaso line and diesel vehicl es, respectively. The observed pollutant loadings are similar to load ings reported in gasolin e and diesel vehicle

emission inventories and other so urce apportionment studies. Fo r example, the high proportions of manganese (Mn) and iron (Fe) apportioned to the diesel profile are c onsistent with reported findings from the Seattle area (May kut et al., 2003) and Phoenix (R amadan et al., 2000; Lewis et al., 2003).

Species
P M2 . 5 _

M

a s s

N i t r a t e

S u l f a t e EC O C B C A l

N H 4 + A s B r C

a C l C u F e P

b

M

g

Mn N i K S i N

a + S r Ti V Z nConcentrationofSpecies(g/m3)

0.0001

0.001

0.01

0.1

1PercentageofSpecies

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 recepto r modeling studies (Solomon et al., 2008). Generally, the concentration of total OC is expect ed to be higher than the EC concentration in emissions from gasoline-fired vehicles. Conversel y, except for diesel vehicles operating at very slow speeds and in stop-and-go traffic, the concen tration of total EC is expected to be higher

than the OC concentration in emissions from di esel-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 such25 emissions have been shown to contain OC/EC c oncentrations that are comparable to those observed in typical gasoline emissions.

Species
P M2 . 5 _

M

a s s

N i t r a t e

S u l f a t e E

C

O C

B C A l

N H 4 + A s B r C

a C l C u F e P

b

Mg M

n N i K S i N

a + S r T

i V Z nConcentrationofSpecies(g/m3)

0.0001

0.001

0.01

0.1

1PercentageofSpecies

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 (F igures 4.6(a) and 4.6(b)) are nearly identical suggesting similar potential source locations. The C PFs 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 di rection indicated by the motor ve hicle CPFs may suggest that mobile source emissions origina ting from the McChord Air Force Base may have been included

in the motor vehicle contributi ons, the actual impact of the Ai r Force Base’s emissions is expected to be insi gnificant (EPA, 2008b).

It should be noted that if th e analysis were based upon partic le number concentrations, it is possible that the resolved relativ e contributions of motor vehicles would be different. This is because motor vehicles have been reported to emit significant amounts of u ltrafine particles (less than 100 nanometers in aerodynamic diameter), whic h do not weigh as much as the heavier super micron particles (Ogulei et al., 2007). Additi onally, because motor vehicles are significant contributors of precursors of s econdary aerosol, it is likely that some of the motor vehicle

emissions are included in the secondary aerosol contributions.

The total motor vehicle contributi on reported in this study (13%) is comparable to the 11% value reported by Kim et al. (2003) in Spokane, WA. Al so, Maykut et al. (2003), Wu et al. (2007) and Kim and Hopke (2008a) reported moto r vehicle contributions of 10-44% within the Seattle area. But, Jimenez et al. (2006) reported a lower contri bution of 2% in the easte rn Washington city of26 

### 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 ve hicles in Pullman is expected compared to the larger cities with grea ter vehicle miles traveled.



## 4.3 Secondary Aerosol (Secondary Nitrate and Secondary Sulfate)

Secondary aerosol was dominated by ammonium nitrate, NH 4 NO 3 , and ammonium sulfate, (NH 4 ) 2 SO 4 . 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, resp ectively. The measured nitrate and sulfate concentrations were highly correlated with the resolved secondary nitrate and sulfate contributions, with Pearson’s correlation coefficients (r 2 ) of 0.96 and 0.89, respectively. The ammonium ion (NH 4 + ) was also highly correlated with both secondary nitrate a nd sulfate, with

correlation coefficients of 0.86 and 0.84, respectively.

Secondary nitrate in the atmo sphere 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 n itrate may have resulted from processing of NOx emissions originating primar ily from traffic traveling along I-5 and major arterial streets. The predicted secondary nitrate concentration p eaks in the winter and late fall because the combination of high relative humidit y and low ambient temperature in the winter promotes partitioning of ammonium nitrate into the particle phas e (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
P M

2

. 5 _

M

a s s

N i t r a t e

S

u l f a t e E C

O C

B C A l

N H 4

+ A s B r C a C l C u F e P

b

M

g

M

n N i K S i N

a + S r T i V Z nConcentrationofSpecies(g/m3)

0.0001

0.001

0.01

0.1

1 10PercentageofSpecies

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
P M

2

. 5 _

M

a s s

N i t r a t e
Su

l f a t e E C

O C B C A l

N H4

+ A s B r C a C l C u Fe P b

M

g

M

n N i K Si N

a + Sr T i V Z nConcentrationofSpecies(g/m3)

0.0001

0.001

0.01

0.1

1 10PercentageofSpecies

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, ar e largely formed through atmospheric gas phase and aqueous phase reactions involving sulfur dioxide (SO 2 ) and atmospheric oxidants such as the hydroxyl radical (OH), hydrogen peroxide (H 2 O 2 ) and ozone. Some of the sulfate particles may also be emitted directly as primary sulf ate particles (Kermine n et al. 2000).

The reaction pathways that lead to the formation of secondary nitrat e and sulfate also lead to the formation of carbonaceous secondary aerosol. This generally explains the presence of significant amounts of carbonaceous species (prima rily 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 indicate s that the secondary su lfate resolved in this analysis may have primarily been formed from atmosphe ric processing of locally-generated SO 2 emissions, including SO 2 emissions from point and non-point s ources. This conclusion was further supported by the prevalence of c oncentration spikes in the secondary nitrat e and sulfate time series. Multiple authors have pr eviously reported relatively sm ooth 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 second ary 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 attr ibutable 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 amm onium nitrate) are observed when there is a combination of high relative humidity and low ambien t temperature. Our analysis of the area’s ambient relative humidity and temperature data, m easured during the study period, revealed that29 the highest relative humidity measurements we re typically observed between October and February, while the lowest ambient temperatures were observed in January. Thus, while there may be other factors, it is lik ely that the observed seasonal pa tterns in sulfate and nitrate concentrations are primarily defined by w eather conditions within the study area.



### Season Winter Spring Summer Fall Winter*SulfateConcentration(g/m3)

0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2NitrateConcentration(g/m3)

0.0 0.5 1.0 1.5 2.0 2.5 3.0PM2.5MassConc.(g/m3)

0 2 4 6 8 10 12 14 16

Sulfate

Nitrate

PM 2.5 Mass

Figure 4.9. Seasonal profile for secondary aerosol and PM 2.5 mass

.

It is important to note from Figure 4.9 that the time series for total PM 2.5 mass is similar to that of secondary nitrate. This may sugge st that in addition to weather f actors, the primary contributors to the observed nitrate may also be responsib le for much of the observed total mass.

The total secondary aeroso l contribution (25%) reso lved 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 cont ributions 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 co ntributions of 15-16% and secondary sulfate contributions of 15-18% in the Olympic Penins ula, WA; Portland, OR; and Anchorage, AK. While some differences do exist in the actual ap portioned contributions in these studies, it is clear that secondary aerosol accounts for a signifi cant 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 strong30 correlations between industri al emissions and total PM 2.5 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

P M

2

. 5 _

M

a s s

N i t r a t e

S

u l fa

t e

E C

O C

B C A l

N H 4

+ A s B r

C

a C l

C

u F e

P b

M

g

M

n N i K S i N

a + S r T i V Z

nConcentrationofSpecies(g/m3)

0.0001

0.001

0.01

0.1

1PercentageofSpecies

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 th eir 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 an d 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 NE31 indicates that point sources locate d to the northeast, and, perhaps, the Port of Tacoma, are partly responsible for the observed mass co ncentrations at the monitor. The observed strong correlation between these emissions and combustion product s such as OC and BC suggests a significant

influence of combustion sources.

Arsenic Concentrations in

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 th at the contributions that make up this source may also consist of crustal-laden remnants of the now-defunct AS ARCO 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 re sult 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 sa mples. It can be observed that high arsenic32 concentrations are predicted to occur in the southwest, northwestand northeast quadrants relative to the sampling site.

We confirmed through a CPF analysis of the meas ured 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 directio ns. Thus, the southeast direction shown by the CPF for industrial emissions (Figure 4.11) may s uggest the existence of industrial point sources

in that direction.

An on-going regional atmospheric deposition stu dy has reported generally higher fluxes of metals (As, Cu, Pb) and total polycyclic ar omatic hydrocarbons (PAHs) within Tacoma Commencement Bay (located about 5 miles northeas t 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, co upled with freshly-emitted point and non-point emissions, are responsible for the observed metallic concentrations.

Industrial emissions accounted for about 6% of th e 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 cont ribution of 7% from “industry” in Beacon Hill (Seattle).



## 4.5 Fugitive Dust

Fugitive dust was characterized by an abundance of crustal elements, al uminum (Al), calcium (Ca), iron (Fe) and sili con (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-suspe nded 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 cont ributions from that facility.

Fugitive dust was poorly correlated with total PM 2.5 mass (r 2 = 0.19) indicating that its influence on the measured total mass was not significant. As expected, fugitive dust emissions were highest in the summer an d lowest in the winter.33 

### Species

P M

2

. 5 _

M

a s s

N i t r a t e

S

u l f

a t e

E C

O C

B C A l

N H 4

+ A s B r

C

a C l

C

u F e

P b

M

g

M

n N i K S i N

a + S r T i V Z

nConcentrationofSpecies(g/m3)

0.0001

0.001

0.01

0.1

1PercentageofSpecies

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 ni ckel (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 sour ce with those species. Strong correlations were observed with Ni (r 2 = 0.86) and V (r 2 = 0.92), while moderate

correlations were f ound with sulfate (r 2 = 0.46), calcium, Ca (r 2 = 0.58) and iron, Fe (r 2 = 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 NE34 PM 2.5 mass (r 2 = 0.29), which suggests a relatively insignificant influence by oil combustion/ships on the measured total mass concentrations.



### Species

P M

2

. 5 _

M

a s s

N i t r a t e

S

u l f

a t e

E C

O

C

B C A l

N H 4

+ A s B r

C

a C l

C

u F e

P b

M

g

M

n N i K S i N

a + S r T i V Z

nConcentrationofSpecies(g/m3)

0.0001

0.001

0.01

0.1

1PercentageofSpecies

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 NE35 combustion contributions of 4-6% in five sites within the Seattle area. The authors suggested that a major portion of the oil combustion emi ssions may be attributable to ships operating

within the Port of Seattle. In a related st udy, Kim and Hopke (2008b) reported an oil combustion contribution of 19% in the Olym pic Peninsula, primarily attr ibutable to ship emissions.

The averaged contribution from oil combustion/ship s generally peaked in August or September. Although shipping activity w ithin the Port of Tacoma fluctuat es throughout the year, increased shipping activity in late summer/early fall wa s reported by the Pacific Maritime Association during the study period (PMA, 2009). Thus, shippi ng activity within the Port of Tacoma may have been responsible for much of the cont ributions 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 meani ngful correlation with PM 2.5 mass indicating that their influence on the measured total mass concentrations was insigni ficant. 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 ar e strontium (Sr) and potassium (K). Fireworks were highly correlated with copper, Cu (r 2 = 0.89), magnesium, Mg (r 2 = 0.71), potassium, K (r 2
= 0.89), and strontium, Sr (r 2 = 0.86). The time series for the re solved fireworks source (Figure 4.19) confirmed that fireworks displays associat ed with Independence Day celebrations were the primary contributors to the resolved firewo rks 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 litera ture 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 pr edominance 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 wi th 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 (NO 3 - ) through atmospheric reacti on with nitric acid (HNO 3 ). In contrast to fresh sea salt, aged sea salt c ontributions were highes t in the summer probably because aged sea salt particles have been report ed to associate with sulfate particles (Zhuang et al., 1999).36 

## (a) Fireworks

Species Species

P M

2

. 5 _

M

as

s

N i t r at

e

S

u l f a t e

E C

O C

B C

A l N H 4 +

A

s B r C

a

C l C u

F e P

b

M

g

M

n

N i K S i N a +
S

r T

i V Z n

0.0001

0.001

0.01

0.1

1

0 20 40 60 80 100



## (c) Aged Sea Salt

Species

P M

2

. 5 _

M

a s s

N i t r at

e

S

u l f a t e

E C

O C

B C A l

N H 4 + A s B r

C

a C

l

C

u F e

P b

M

g

M

n N i K S i N

a+ S r T

i V Z

n 0.0001

0.001

0.01

0.1

1

0 20 40 60 80 100 

## (b) Fresh Sea SaltConcentrationofSpecies(g/m3)

0.0001 0.001

0.01

0.1

1PercentageofSpecies

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 7/1/09MassConcentration(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 c ontribution 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 ag ed sea salt contributions of 5-10% and fresh sea salt contributions of 2-10% within the Olympic Peni nsula, WA, Portland, OR and Anchorage, AK. Wu et al. (2007) found a “marine” source (approxim ately 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 NE38 

# 5.0 CONCLUSION

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

has determined that the most si gnificant sources of ambient fine particles in the nonattainment area are wood smoke, gasoline vehicles, diesel ve hicles, fugitive dust, fireworks, secondary nitrate, secondary sulfat e, oil combustion and ships, arseni c-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 nonatta inment area is wood smoke. We

believe that the resolved wood smoke contributio ns represent combined contributions from wood stoves and other wood-fired home heating devices. Wood smoke contributi ons had the strongest relationship with the measured total PM 2.5 mass which illustrates that wood smoke contributions were responsible for the majority of the observed trend in total PM 2.5 mass concentrations. The source apportionment results showed that wood smoke accounts for nearly one-half of the total

measured PM 2.5 mass on most days. The wood smoke cont ributions were most significant during the winter and on days when the measured daily PM 2.5 mass concentration exceeded 20 or 30 μg/m 3

.

Wind direction and wind speed were found to be a factor in the observed wood smoke concentrations. Accordingly, the most significant sources of w ood smoke appeared to be those

that were located to the southerl y and southeasterly dire ctions with respect to the sampling site.

We observed a strong presence of secondary ae rosol in the study area. 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 multiple local sources of nitrogen oxides, sulfur dioxide and vol atile 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-m obile sources of nitrogen oxides, sulfur dioxide and volatile organic compounds.

We attributed a small but signi ficant portion of the observed PM 2.5 mass to industrial emissions (roughly 6%). Industrial emissions were rich in arsenic, lead, and zi nc. Further analyses revealed that these emissions might be a co mbination of background/re-suspended emissions

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

within the Port of Tacoma.

Finally, our results show that average contri butions 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 w ith other sources or with the 98 th percentile wood smoke contributions. Because of the limited time period, it was not possible to verify whether or not the

observed decline constitutes a tre nd. Nonetheless, it is possible th at existing control strategies for wood smoke and industrial sources may be cr eating measurable annual emissions reductions. A follow-up detailed study which includes several years of ambient data may be necessary to39 determine the true effect of exis ting control strategies on annual PM 2.5 emissions. However, in order to reduce ambient fine particle mass con centrations on specific days, a combination of targeted source- and location-specific emi ssions 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

NH 4 + Ammonium ion

Ni Nickel

NO 3 - Nitrate ion
NOx Nitrogen oxides

OC Organic Carbon

Pb Lead

PM 2.5 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 Rubidium43 S Sulfur

Se Selenium

Si Silicon
SIP State Implementation Plan

SO 2 Sulfur dioxide

SO 4 2- Sulfate ion

Sr Strontium
STN Speciation Trends Network

Ti Titanium
TOR Thermal Optical Reflectance
TOT Thermal Optical Transmittance

V Vanadium

Zn Zinc

μ g/m 3 Micrograms per cubic meter of ambient air