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1 

# Source apportionment modeling for multiple urban and rural

PM2.5 chemical speciation sites within the Pacific Northwest

.

Robert Kotchenruther, EPA Region 10. Kotchenruther.Robert@epa.gov



## 1

**. Introduction**  **and Summary**

S ource apportionment modeling was conducted using the Positive Matrix Factorization (PMF)

model on chemically speciated PM2.5 aerosol for 39 urban and rural monitoring locations

throughout the Pacific Northwest. Data from two monitoring networks, the mostly rural

Interagency Monitori ng of Protected Visual Environments (IMPROVE) and EPA’s mainly urban

population

- oriented Speciation Trends Network (STN)

, were used. There were 24 and 15 sites

modeled for IMPROVE and STN, respectively. The time period modeled was roughly 2007 to

the pre sent , but varied by site depending on the history of the site and currently available data.

To allow for maximum site

- to

- site comparability of results, the raw data from each monitor was

prepared using a consistent data preparation methodology

. Following that, each site was

modeled independently but followed a consistent modeling protocol . Model output consists of a

number of ‘factors’ (usually between 4 and 12) that explain the majority of the data varia bility

and a time series of these facto rs’ mass impacts at the monitor.

There were multiple goal s in conducting this analysis as well as multiple possible lines of further

investigation . Some of these are as follows:

**•** Some of the urban monitoring locations are currently designated PM2.5 n onattainment areas

(Utah sites, Tacoma WA, Oakridge OR, Klamath Falls OR), so this analysis provides

additional information for understanding sources of PM2.5 in those areas.

**•** For those monitoring locations currently in attainment of the PM2.5 standard, som e are likely

to be designated as nonattainment areas after EPA’s next review of the PM2.5 standards

(e.g., Yakima WA, Lakeview OR). So this analysis gives a jump start to understanding

sources of elevated PM2.5 in those areas.

**•** Modeling multiple sites us ing a consistent data preparation methodology and modeling

protocol allows for be tter site

- to

- site comparability. For example, wood smoke factors can

be compared between urban sites to examine the relative importance of wood smoke at each

location

.

**•** An ana lysis of the site

- to

- site differences in chemical composition for a given factor can also

help distinguish the nature of sources contributing to that factor at each location

.

**•** Comparing rural factor magnitudes to urban can give an estimate of background vs . ‘urban

excess’.

For this report, results have been aggregated into 2 seasons (winter and summer) and average

contributions calculated for dates when total PM2.5 mass was elevated (total mass greater than

15 ug/m3 for STN monitors, and greater than 7.5 u g/m3 for IMPROVE monitors). Results are

located in Tables 5.5 through 5.8 below. Most days with elevated PM2.5 at the urban STN sites

occur in winter. This is consistent with the general conceptual model that urban sites in the

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2 Pacific Northwest experie nce elevated PM2.5 on wintertime days with air stagnation conditions,

where primary PM2.5 emissions can concentrate and/or wintertime nitrate aerosol formation is

favored. Most days with elevated PM2.5 at the rural IMPROVE sites occur in summer. This is

consistent with the general conceptual model that elevated PM2.5 at rural sites in the Pacific

Northwest is driven by photochemically produced and longer lived aerosol (e.g., sulfate,

secondary organic) and windblown soils overlaid with occasional biomass burning impacts.



## 2. **Monitoring Site s**  **and D ata.** 

**2.1. Map**  **s of the**  **monitoring locations used in this analysis**  **:**

39 urban and rural monitoring locations throughout the Pacific Northwest were considered in this

analysis. Figure s 2 .1 shows a map with the locations of 15 urban EPA Speciation Trends

Network (STN) PM2.5 chemical speciation monitors and Figure 2 .2 shows a map with the

locations of 24 rural IMPROVE network PM2. 5 chemical speciation monitors. Tables 2.1 and

2 .2 provide more detailed monitoring site informa tion for the STN and IMPROVE monitors,

respectively , including; site identification information, the date range of data used , the number of

samples used in this modeling analysis , and the latitude and longitude of the site.

Figure 2 .1

. Map of the Pacifi c Northwest showing the 15 Speciation Trends Network (STN)

monitors used for modeling in this analysis.

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3 Figure 2 .2. Map of the Pacific Northwest showing the 24 IMPROVE monitors used for

modeling in this analysis.

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4 

## 2.2. **Metadata info rmation**  **:**

**2 .2.1. STN**  **Data**

Table 2 .1. Site information for the STN sites used in this modeling analysis.

Site Name Date Range

Modeled Number of

Measurements AQS Number State County Latitude Longitude

Boise 5/3/2007

- 9/12/2011 532 160010010 Idaho Ada 43.600264

- 116.347897

Klamath_Falls 7/6/2009

- 3/28/2011 88 410350004 Oregon Klamath 42.188889

- 121.7225

Lakeview 10/4/2009

- 3/28/2011 83 410370001 Oregon Lake 42.188889

- 120.351944

Oakridge 7/6/2009

- 3/28/2011 100 410392013 Oregon Lane 43.744352

- 122.480518

Portland 5/3/2007

- 8/4/2011 513 410510080 Oregon Multnomah 45.49647386

- 122.6034112

Bountiful 5/6/2007

- 9/6/2011 265 490110004 Utah Davis 40.902967

- 111.884467

Salt_Lake_City 5/3/2007

- 9/12/2011 520 490353006 Utah Salt Lake 40.736389

- 111.872222

Lindon 5/6/2007

- 9/6/2011 265 490494001 Utah Utah 40.341389

- 111.713611

Vancouver 4/1/2009

- 9/6/2011 149 530110013 Washington Clark 45.648333

- 122.586944

Seattle_DW

( Duwamish ) 11/2/2008

- 9/6/2011 174 530330057 Washington King 47.5632

- 122.3405

Seattle_BH

(Beacon Hill) 5/3/2007

- 9/12/2011 435 530330080 Washington King 47.568333

- 122.308056

Tacoma_SL

(South L St.) 5/6/2007

- 9/6/2011 260 530530029 Washington Pierce 47.1864

- 122.4517

Tacoma_AL

(Alexander Ave.) 11/2/2008

- 9/6/2011 174 530530031 Washington Pierce 47.2656

- 122.3858

Marysville 4/7/2009

- 9/6/2011 145 530611007 Washington Snohomish 48.054315

- 122.171529

Yakima 11/8/2007

- 9/6/2011 202 530770009 Washington Yakima 46.59678

- 120.512215



### • The source of STN data was the EPA AQS database and data were downloaded on

1/26/2012. The data reports used were AMP350 raw data report and AMP503 sample blank

report.

**•** The date range of data extracted for each site is shown in Table 2 .1 . The beginning of the

date range was chosen to coincide with the install ation of an updated car bon sampler (URG

3000N ) at each location and the end date represents the latest data available at the time data

were extracted.

**•** Final number of samples used in this modeling analysis at each site ranged from 83 to 532

and depended on sampling frequency, date of site installation, and date of carbon sampling

method change

.

**•** Chemical species used in this modeling analysis depended on data quality at each monitoring

location but generally included elements associated with soil such as Al, Fe, Ti, Ca, and Si,

the elements associated with sea salt Na and Cl, major ions like NH4 and NO3, sulfur as a

surrogate for SO4, organic and elemental carbon species based on the thermal evolution

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5 method, and a range of other trace elements. For a li st of chemical species used at each site,

please refer to t he factor species profil es shown in Appendix A1

.

**2 .2.2. IMPROVE Data** 

Table 2 .2. Site information for the IMPROVE sites used in this modeling analysis.

Site Name Date Range Modeled Number of

Measurements State Name / Class 1 Area Latitude Longitude

CRMO 1/1/2007

- 12/31/2010 ~ 450 ID Craters of the Moon 43.4605

- 113.5551

SAWT 1/1/2007

- 12/31/2010 ~ 450 ID Sawtooth 44.1705

- 114.9271

CABI 1/1/2007

- 12/31/2010 ~ 450 MT Cabinet Mountains 47.9549

- 115.6709

FLAT 1/1/2007

- 12/31/2010 ~ 450 MT Flathead 47.7734

- 114.269

GLAC 1/1/2007

- 12/31/2010 ~ 450 MT Glacier 48.5105

- 113.9966

MONT 1/1/2007

- 12/31/2010 ~ 450 MT Monture 47.1222

- 113.1544

JARB 1/1/2007

- 12/31/2010 ~ 450 NV Jarbidge Wilderness 41.8926

- 115.4261

CRLA 1/1/2007

- 12/31/2010 ~ 450 OR Crater Lake 42.8958

- 122.1361

HECA 1/1/2007

- 12/31/2010 ~ 450 OR Hells Canyon 44.9702

- 116.8438

KALM 1/1/2007

- 12/31/2010 ~ 450 OR Kalmiopsis 42.552

- 124.0589

MOHO 1/1/2007

- 12/31/2010 ~ 450 OR Mount Hood 45.2888

- 121.7837

STAR 1/1/2007

- 12/31/2010 ~ 450 OR Starkey 45.2249

- 118.5129

THSI 1/1/2007

- 12/31/2010 ~ 450 OR Three Sisters Wilderness 44.291

- 122.0434

LABE 1/1/2007

- 12/31/2010 ~ 450 CA Lava Beds 41.7117

- 121.5068

REDW 1/1/2007

- 12/31/2010 ~ 450 CA Redwood 41.5608

- 124.0839

COGO 1/1/2007

- 12/31/2010 ~ 450 WA Columbia Gorge 45.5693

- 122.2103

CORI 1/1/2007

- 12/31/2010 ~ 450 WA Columbia River Gorge 45.6644

- 121.0008

MAKA 1/1/2007

- 12/31/2010 ~ 450 WA Makah Tribe 48.3719

- 124.595

MORA 1/1/2007

- 12/31/2010 ~ 450 WA Mount Rainier 46.7583

- 122.1244

NOCA 1/1/2007

- 12/31/2010 ~ 450 WA North Cascades 48.7316

- 121.0646

OLYM 1/1/2007

- 12/31/2010 ~ 450 WA Olympic 48.0065

- 122.9727

PASA 1/1/2007

- 12/31/2010 ~ 450 WA Pasayten 48.3877

- 119.9275

SNPA 1/1/2007

- 12/31/2010 ~ 450 WA Snoqualmie Pass 47.422

- 121.4259

WHPA 1/1/2007

- 12/31/2010 ~ 450 WA White Pass 46.6243

- 121.3881



### • The source of IMPROVE data was the VIEWS web site

( http://views.cira.colostate.edu/web/ ). D ata were downloaded on 1/10/2012

.

**•** The d ate range of data extracted for the three sites was 1/1/2007

– 12/31/2010

. The

beginning of t his date range was chosen to c oincide with the first year where carbon

measurements in the STN network switched to the IMPROVE methodology. T he end date

represents the latest data available at the time data were extracted.

**•** Final n umber of samples used in thi s modeling analysis at ea ch location was approximately

450. The exact number of samples at each location differed mainly due to differences in the

frequency of site

- to

- site instrument errors

.

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6 **•** Chemical species used in this modeling analysis depended on data quality at each monitor ing

location but generally included elements associated with soil such as Al, Fe, Ti, Ca, and Si,

the elements associated with sea salt

, Na a nd Cl, major ions like SO4 and NO3, organic and

elemental carbon species based on the thermal evolution method, and a range of other trace

elements. For a list of chemical species used at each site, please refer to the factor species

profiles shown in Appendix A2.



## 3. **Data Preparation M ethodology** 

Except as noted below the measured values, uncertainties, sample blank correction, and method

detection limits for each chemical species were obtained and prepared as recommended in the

EPA Region 10 guidance document “ EPA Region 10 Guidance for the Use of Receptor Models

to Support Policy and Regulatory Decision s ”, dated 12/17/2009 (Kotchenruther, 2009)

.

The data preparation methodology was identical for STN sites and IMPROVE sites. In preparing

the model input data, deviations from the 2009 guidance were as follows

. The recommendation

to discard chemical spe cies with more than 70% of the data recoded below the MDL was

ignored. This was done to retain potentially important species such as EC3 in the AQS dataset

for combustion sources and other trace metals potentially significant in source identification. The

guidance recommendation to discard species with signal

- to

- noise (S/N) ratio s less than 0.2 was

strengthened t o remove species with S/N < 0.30 . This was done to remove trace chemical

species that were detected at levels very near detection limits, but tha t rarely contribute to the

identification of sources, and therefore would needlessly complicate the resulting factor chemical

profiles and factor identification

. It should be noted that while the above MDL requirement was

ignored, strengthening the S/N re quirement to 0.30 takes care of many species with frequent data

near the MDL. T he requirement for a minimum of more than 50% of samples being valid

measurements was also retained here

.

Other data processing choices included:

**•** To avoid double counting of chemical species

, in the AQS datasets Na+, K+, SO4 2

- 

### , and

OP(TOT) were removed in favor of Na, K, S, and OP(TOR) measurements. In the

IMPROVE datasets Cl

- and S were removed in favor of Cl and SO4 2

-



### . The chemical species

that were retained were chose n based on either higher data comple teness or higher S/N ratio.

OP is reported by two measurement methods in the AQS database, TOT and TOR, here OP

via TOR was retained to be consistent with the IMPROVE TOR measurement methodology.

**•** D ata from July 4 and 5 were removed to mitigate the influence of f ireworks in the modeling

result

.



## 4. **Modeling**

The PMF model version used in this analysis was EPA PMF v4.0 beta. The current publically

available version is 3.0 ( http://www.epa.gov/heasd/products/pmf/pmf.html ). For the purposes of

this modeling analysis, v4.0 beta is identical to the publically available v3.0. Each site was

modeled separately.

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7 User setting for these model runs w ere as follows:

**•** PM2.5 mass data was set as the ‘Total Variable’, which allows the model to estimate each

factor contribution to total mass

. Setting total PM2.5 as the ‘Total Variable’ increases its

uncertainty to down weight its influence in the model s olution.

**•** For the base model run, 20 model runs were made to find the best least

- squares minimum

with the ‘seed’ variable set to 10 (‘seed’ determines where the model begins looking for a

solution, setting a specific number rather than using the ‘random’ setting allows the user to

reproduce the model results exactly).

**•** The optimum number of factors for each site was determined somewhat subjectively based

on the interpretation of model results, but also from the quality of the least

- squares fit

(analysis o f Q values) in the model output

.



## 5. **Modeling Results and Analysis**

**5.1. Resulting**  **PMF Factors and**  **Factor Classification**

The PMF model uses a form of factor analysis where the underlying co

- variability of many

variables (e.g., sample to sample variation in particulate matter chemical species) is described by

a smaller set of factors (e.g., particulate matter sources) to which the original variables are

related. While PMF is often called a ‘source apportionment’ model, a more accurate description

would be a ‘factor apportionment’ model. While this might seem to be an esoteric distinction , it

is an important one to understand when viewing results in this report. PMF is a ‘factor analysis’

model, because it divides the full modeling dataset into a much simpler dataset that identifies the

leading ‘factors’ causing day to day variability in the full dataset. A PMF factor could represent

the impact on the monitor of a single source (e.g. industrial facility) , a source category (e.g. a

bunch of sources that have very similar chemical fingerprints and emissions patterns such as

cars, trucks, wo od burning sources) , or m ultiple sources or source categories grouped together

(PMF may group together multiple sources because of insufficient variability in emissions,

chemical fingerprint, and/or temporal/spatial resolution)

.

The interpretation/class ification of factors output by the PMF model is a subjective process. It

can depend on the experience of the modeler, availability of current source profiles and literature

references, availability of supplementary information about the airshed, and an un derstanding of

the range of possible sources impacting the monitoring location.

It should be also noted that the factors determined in a PMF analysis are not necessarily mutually

exclusive. For example, a factor identified as predominantly sulfate ae rosol is possibly from a

combination of multiple sources including diesel engines and industrial facilities, even though

these sources may also have separately identified PMF factors. Hence, caution should be used in

interpreting factor classifications to o literally or with exclusivity.

For the 15 AQS sites modeled in this analysis there were 18 different f actors identified

, and for

the 24 IMPROVE sites modeled there were 17 different factors identified . Table s 5 .1 and 5.3 list

the names given to each f actor for AQS and IMPROVE sites, respectively, as well as a brief

description of the unique chemical characteristics of each factor. Tables 5.2 and 5.4 list the

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8 number and types of factors found at each location for AQS and IMPROVE sites, respectively.

T abl es 5.5 and 5.7 show the average percent contribution of factors to total PM2.5 mass at STN

sites on winter and summer days , respectively, when PM2.5 was above 15 ug/m3. Tables 5.6

and 5.8 show the average percent contribution of factors to total PM2.5 mass at IMPROVE sites

on winter and summer days , respectively, when PM2.5 was above 7.5 ug/m3. Most days with

elevated PM2.5 at the urban STN sites occur in winter. This is consistent with the general

conceptual model that urban sites in the Pacific Nor thwest experience elevated PM2.5 on

wintertime days with air stagnation conditions, where primary PM2.5 emissions can concentrate

and/or wintertime nitrate aerosol formation is favored. Most days with elevated PM2.5 at the

rural IMPROVE sites occur in sum mer. This is consistent with the general conceptual model

that elevated PM2.5 concentrations at rural sites in the Pacific Northwest are driven by

photochemically produced and longer lived aerosol (e.g., sulfate, secondary organic) and

windblown soils ove rlaid with occasional biomass burning impacts.

The PMF factor chemical profiles for AQS sites and IMPROVE sites are located in Appendices

A1 and A2, respectively, which are separate excel files from this report.

Table 5 .1. Factors found at STN sites.

Name Factor Description and Identifying Features

Ammonium Chloride Factor mass dominated by the sum of Cl and NH 4



### .

Calcium Rich Factor mass dom inated by calcium and sulfate. Previous work has linked

similar calcium rich factors to cement production ( Kim et al., 2004 ; Shi et al.,

2009 ).

Copper Rich Factor mass dominated by Cu and EC. Factor possibly linked to metal

processing industry ( Amato and Hopke 2011 ).

Fireworks Factor mass dominated by potassium and sulfate, with variable amounts of OC.

Cu and Mg are trace metals but significant to factor identification. Factor

identified by chemical constituents and prevalence on days of the year known

for significant fireworks activity.

Gasoline Vehicles Factor mass dominated by the sum of OC2, OC3 and OC4 species with an

approximate ratio of 1:2:1 between OC2:OC3:OC4. Usually no OC1 or OP

component. EC component usually 15

– 20 % of total mass

, and mostly EC1.

Previous work has linked similar factors to gasoline vehicles ( Hwang and

Hopke, 2007 ; Kim et al., 2004 ; Maykut et al., 2003 ; Zhao and Hopke, 2004;

Kim and Hopke, 2006 ; Kim and Hopke, 2008b ; Zhao and Hopke, 2006 ).

Iron Rich Fe is important in the identification of factor with concentrations ranging

between 5

- 50% of total factor mass. Factor also has variable amounts of OC,

EC, SO 4 

### , and/or NO 3



### . Factor identification is unclear. Previous work has

linked similar Fe rich factors to a variety of sources including diesel vehicles,

heavy

- duty diesel, oil combustion, or industrial activity [ Karanasiou et al., 2009

(Oil combustion) ; Kim et al., 2004 (Diesel) ; Maykut et al., 2003 (Diesel) ;

Ramadan et al., 2000 (Heavy

- duty diesel); Kim and Hopke, 2008a (Diesel) ;

Kim and Hopke, 2008b (Metal processing); Lee et al., 2008 (Metal/Industrial

processing) ; Wu et al., 2007 (Diesel); Zhao and Hopke, 2006 (Secondary

Sulfate) ; Zhou et al., 2004 (Traffic); Amato and Hopke 2011 (Diesel); Kim et

al., 2010 (Diesel) ; Lewis et al., 2003 (Diesel) ].

Nitrate Rich Factor mass dominated by the sum of NO 3 

### and NH 4



### . Organic Pyrolysis Factor mass dominated by the sum of OC fractions (often > 95% of mass). OC

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9 (OP) Rich fractions are weighted to the higher temperature fractions (OC3, OC4, OP). OP

is often the dominant mass of the OC fractions. This factor likely has a

significant contribution from secondary organic aerosols. OP rich factors have

been previously identified by Hwang and Hopke, 2007 ; Kim et al., 2004; and

Zhao and Hopke, 2006

. Potassium Rich Factor mass has a high percentage of potassium and sulfate. Possibly fireworks

related, but not grouped with the ‘ fireworks ’ factor here because time series did

not show a particular bias to days when fireworks are more likely (e.g., Jan 1;

note, July 4 & 5 were removed f rom add data inputs).

Sea Salt Factor mass dominated by the sum of Na and Cl.

Secondary Aerosol,

Marine Related Sodium concentrations ranged between 5

– 13 % of factor mass with no

associated Cl mass and about 1% Mg . Factor also associated with variable

amounts of SO 4 

### , NO 3 

### , and OC. Factor identified by the importance of Na and

Mg in chemical profile and the absence of Cl.

Soil Factor mass dominated by the sum of Al, Ca, Fe, Ti, and Si

. Soil ( Calcium Rich) Factor mass dominated by the sum of Al, Ca, Fe, Ti, and Si. However, calcium

concentration is elevated compared to the other soil profile.

Sulfate Rich Factor mass dominated by the sum of SO 4 

### and NH 4



### .

Sulfate Rich , Fuel Oil

Related Factor mass dominated by SO4 and NH4 with Ni and V playing a significant

role in factor identification at trace levels. Approximate ratio of V:Ni of 3:1.

OC and EC also can contribute to factor mass. Previous work has linked similar

factors to marine shipping fuel oil combustion due to Ni and V tracer species

and high fuel sulfur content (Maykut et al., 2003; Kim and Hopke, 2008a ; Kim

and Hopke, 2008b )

. Urban Unidentified Factor dominated by a mixture of OC, EC, SO 4 

### and NO 3 

### . The EC2 is

particularly important in the identification of this factor. Factor identification is

unclear. Previous work has linked similar factors to a variety of sources

including railroad traffic and diesel vehicles [ Kim et al., 2004 (railroad traffic) ;

Zhao and Hopke , 2004 (Diesel); Kim and Hopke, 2006 (secondary sulfate ) ; Han

et al., 2007 ( Diesel) ]. It is likely that this factor is in some way connected to

fuel combustion.

Wood Smoke Factor dominated by the sum of OC and EC plus about 1% potassium

. Usually

OC do minated by the OC1 fraction, EC dominated by EC1, and no OP

component. Majority of mass impacts in winter. EC can range between 10

– 30% of factor. Previous work has linked similar factors to wood smoke, as here

with no OP but others found OC3 mass high er than OC1. Hence, previous

work’s wood smoke factors are more similar to the “ Secondary Organic Aerosol

& Aged Wood Smoke ” factors in the IMPROVE site factors discussed below

( Hwang and Hopke, 2007; Ki m et al., 2004; Ma ykut et al., 2003; Ki m and

Hopke, 2008b; Zhao and Hopke, 2006).

Zinc Rich Zn concentrations ranged from 3

– 7% of factor mass, remaining mass

dominated by OC and EC. Factor identification is unclear. Previous work has

linked similar factors to a variety of sources including diesel vehicles and

industrial activity [ H wang and Hopke, 2007 (diesel); Kim and Hopke, 2006

(diesel); Kim a nd Hopke, 2008b (diesel); Kim et al., 2010 (industrial) ]. Zinc is

widely used as an a dditive in lubricating oils for diesel engines

, for gasoline

powered engines in motor vehicles and other machines.

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10 Table 5 .2. Number and type of f actors found at each STN site

.

Site NameNumber of Factors FoundAmmonium ChlorideCalcium RichCopper RichFireworksGasoline VehiclesIron RichNitrateRichOrganic Pyrolysis (OP) RichPotassium RichSea SaltSecondary Aerosol, Marine RelatedSoilSoil(Ca Rich)SulfateRichSulfateRich, Fuel Oil RelatedUrbanUnidentifiedWood SmokeZinc Rich

Boise 8        

Klamath_Falls 5     

Lakeview 7       

Oakridge 7      

Portland 8        

Bountiful 11           

Salt_Lake_City 11           

Lindon 12            

Vancouver 8        

Seattle_DW 11           

Seattle_BH 11           

Tacoma_SL 10          

Tacoma_AL 9         

Marysville 9         

Yakima 10          

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11 Table 5 .3. Factors found at IMPROVE sites.

Name Factor Description and Identifying Features

Iron Rich Fe is important in the identification of factor with concentrations ranging

between 11

- 25 % of total factor mass. Factor also has variable amounts of

OC, EC, SO 4 

### , and/or NO 3



### . Factor identification is unclear. Previous work

has linked similar Fe rich factors to a variety of sources including diesel

vehicles, heavy

- duty diesel, oil combustion, or industr ial activity

[ Karanasiou et al., 2009 (Oil combustion) ; Kim et al., 2004 (Diesel) ; Maykut

et al., 2003 (Diesel) ; Ramadan et al., 2000 (Heavy

- duty diesel); Kim and

Hopke, 2008a (Diesel) ; Kim and Hopke, 2008b (Metal processing); Lee et

al., 2008 (Metal/Industrial processing) ; Wu et al., 2007 (Diesel); Zhao and

Hopke, 2006 (Secondary Sulfate) ; Zhou et al., 2004 (Traffic); Amato and

Hopke 2011 (Diesel); Kim et al., 2010 (Diesel) ; Lewis et al., 2003 (Diesel) ].

Mixed, Nitrate & Sulfate Factor mass do minated by the sum of nitrate and sulfate in roughly equal

proportions.

Mixed, Organic Carbon,

Elemental Carbon &

Sulfate Factor mass dominated by the sum of organic carbon, elemental carbon, and

sulfate in variable amounts.

Mixed, Organic Carbon &

Nit rate Factor mass dominated by the sum of organic carbon and nitrate in roughly

equal amounts.

Mixed, Organic Carbon &

Soil (Ca Rich) Factor mass dominated by the sum of organic carbon and soil elements in

roughly equal amounts. Soil elements have elevat ed calcium compared to

the typical soil profile.

Mixed, Sulfate & Soil Factor mass dominated by the sum of sulfate and soil elements in roughly

equal amounts.

Mixed, Sulfate & Soil (Ca

Rich) Factor mass dominated by the sum of sulfate and soil elements in roughly

equal amounts. Soil elements have elevated calcium compared to the typical

soil profile.

Nitrate Rich Factor mass dominated by nitrate.

Organic Pyrolysis (OP)

Rich Factor mass do minated by the sum of OC4 and OP. This factor likely has a

significant contribution from secondary organic aerosols. OP rich factors

have been previously identified by Hwang and Hopke, 2007 ; Kim et al.,

2004; and Zhao and Hopke, 2006

. Sea Salt Factor mass dominated by the sum of Na and Cl.

Secondary Aerosol,

Marine Related Sodium concentrations ranged between 9

– 5 3 % of factor mass with no

associated Cl mass and about 1% Mg

. Factor also associated with variable

amounts of SO 4 

### , NO 3 

### , and OC. Factor identified by the importance of Na

and Mg in chemical profile and the absence of Cl.

Secondary Organic

Aerosol & Aged Wood

Smoke Factor mass dominated by the sum of organic components. Usually OC3 is

elevated above OC2, OC4, and OP with OC1 usually the lowest component.

Variable amount of EC, but usually less than 10%. K in trace amounts but

usually significant for factor identification. Time series of mass impacts

often has a broad summer maximum with occasional elevated ‘spikes’ of 5

-

10 ug/m3. Previous work has linked similar factors to wood smoke ( Hwang

and Hopke, 2007; Ki m et al., 2004; Ma ykut et al., 2003; Ki m and Hopke,

2008b; Zhao and Hopke, 2006). However, since factor time series had

ubiquitous summertime mass around 1

- 2 ug/m3 with occasional elevated

spikes, it is deemed likely that this factor is a combination of secondary

organic aerosol and aged wood smoke.

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12 Soil Factor mass dominated by the sum of Al, Ca, Fe, Ti, and Si.

Soil (Ca Rich) Factor mass dominated by the sum of Al, Ca, Fe, Ti, and Si. However,

calcium concentration is elevated compared to the other soil profile.

Sulfate Rich Factor mass dominated by sulfate.

Sulfate Rich, Fuel Oil

Related Fact or mass usually dominated by SO4 with Ni and V playing a signific ant

role in factor identification at trace levels. Approxima te ratio of V:Ni of 3:1.

OC, EC , and/or NO3 usually also contribute to factor mass. Previous work

has linked similar factors to marine shipping fuel oil combustion due to Ni

and V tracer specie s and high fuel sulfur content (Maykut et al., 2003; Kim

and Hopke, 2008a ; Kim and Hopke, 2008b ).

Zinc Rich Zn concentrations ranged from 1

– 9 % of factor mass, remaining mass

usually dominated by OC and EC but also occasionally containing

significant Si and/or SO4

. Fac tor identification is unclear. Previous work

has linked similar factors to a variety of sources including diesel vehicles and

industrial activity [ H wang and Hopke, 2007 (diesel); Kim and Hop ke, 2006

(diesel); Kim a nd Hopke, 2008b (diesel); Kim et al., 2010 (industrial) ]. Zinc

is widely used as an a dditive in lubricating oils for diesel engines

, for

gasoline

- powered engines in motor vehicles and other machines.

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13 Table 5 .4. Number and ty pe of factors found at each IMPROVE site.

Site

NameNumber of Factors FoundIron RichMixed, Nitrate & SulfateMixed, OC , EC & SulfateMixed, Organic Carbon & NitrateMixed, Organic Carbon & Soil (Ca Rich)Mixed, Sulfate & SoilMixed, Sulfate & Soil(Ca Rich)Nitrate RichOrganic Pyrolysis (OP) RichSea SaltSecondary Aerosol, Marine RelatedSecondary Organic Aerosol & Wood SmokeSoilSoil (Ca Rich)Sulfate RichSulfate Rich, Fuel Oil RelatedZinc Rich

CRMO 6      

SAWT 6      

CABI 5     

FLAT 4    

GLAC 5     

MONT 4    

JARB 6      

CRLA 4    

HECA 5     

KALM 6      

MOHO 4    

STAR 5     

THSI 4    

LABE 7       

REDW 5     

COGO 9         

CORI 7       

MAKA 7       

MORA 7       

NOCA 5     

OLYM 7       

PASA 5     

SNPA 7       

WHPA 5     

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14 

## 5.2. **Average Factor**  **Contributions on Winter**  **( Quarter 1 and 4, October** 

**–**

**March )**  **Season Days with Elevated PM2.5** 

Table 5 .5. Average factor percent contribution to total PM2.5 mass at STN sites on winter (Oct.

– Mar.) days when PM2.5 was above 15 ug/m3.

Site NameAverage Measured Mass (ug/m3)Sample CountModel ResidualUnattributed mass (%)Ammonium Chloride(%)Calcium Rich(%)Copper Rich(%)Fireworks(%)Gasoline Vehicles(%)Iron Rich(%)Nitrate Rich(%)Organic Pyrolysis (OP) Rich(%)Potassium Rich(%)Sea Salt(%)Secondary Aerosol, Marine Related(%)Soil(%)Soil (Ca Rich)(%)Sulfate Rich(%)Sulfate Rich, Fuel Oil Related(%)Urban Unidentified(%)Wood Smoke(%)Zinc Rich(%)

Boise 21 25 6 9 34 8 4 5 13 1 21

Klamath_Falls 27 28 3 6 10 6 2 72

Lakeview 29 16 0 7 13 5 5 1 2 67

Oakridge 24 13 0 10 12 3 1 2 72

Portland 21 52 8 24 9 8 3 4 8 3 34

Bountiful 26 25 3 6 3 5 0 47 5 2 4 11 1 13

Salt_Lake_City 29 65 3 4 2 6 5 45 6 0 7 8 1 13

Lindon 29 27 3 8 2 8 2 56 3 0 3 6 2 5 2

Vancouver 24 8 2 0 11 18 1 3 9 6 50

Seattle_DW 22 9 2 3 12 13 10 6 1 2 5 6 35 4

Seattle_BH 19 9

- 5 0 24 16 13 13 2 2 2 12 3 18

Tacoma_SL 27 29 3 17 7 4 11 2 2 2 5 4 44

Tacoma_AL 22 14 0 15 4 12 6 2 5 6 2 48

Marysville 25 12 3 19 5 4 1 2 5 2 2 56

Yakima 24 55 3 16 3 24 9 5 1 7 4 24 6

### DRAFT

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15 Table 5 .6. Average factor percent contribution to total PM2.5 mass at IMPROVE sites on winter

(Oct.

– Mar.) days when PM2.5 was above 7.5 ug/m3.

Site

NameAverage Measured Mass (ug/m3)Sample CountModel Residual Unattributed mass (%)Iron Rich (%)Mixed, Nitrate & Sulfate (%)Mixed, OC , EC & Sulfate (%)Mixed, Organic Carbon & Nitrate (%)Mixed, Organic Carbon & Soil (Ca Rich) (%)Mixed, Sulfate & Soil (%)Mixed, Sulfate & Soil (Ca Rich) (%)Nitrate Rich (%)Organic Pyrolysis (OP) Rich (%)Sea Salt (%)Secondary Aerosol, Marine Related (%)Secondary Organic Aerosol & Wood Smoke (%)Soil (%)Soil (Ca Rich) (%)Sulfate Rich (%)Sulfate Rich, Fuel Oil Related(%)Zinc Rich (%)

CRMO NA 0 NA NA NA NA NA NA NA

SAWT 16 3 13 1 1 81 1 3 1

CABI 9 3 2 1 78 6 11 1

FLAT NA 0 NA NA NA NA NA

GLAC 9 9 13 3 9 55 3 18

MONT 12 5 10 3 75 5 7

JARB 12 2 43 1 10 2 39 3 1

CRLA 10 1 40 24 34 1 1

HECA 9 9 8 10 61 6 14 0

KALM 10 5 9 2 7 72 2 8 0

MOHO 8 1 5 6 9 1 79

STAR 9 8 21 11 1 61 3 4

THSI 14 3 35 1 57 1 6

LABE 11 2 17 1 0 1 78 2 1 0

REDW 15 3 13 2 10 9 58 7

COGO 10 27 10 5 10 1 5 49 4 12 2 1

CORI 11 33 7 1 27 3 35 7 17 3

MAKA 10 19 8 6 7 6 53 4 8 8

MORA NA 0 NA NA NA NA NA NA NA NA

NOCA 8 1 9 3 60 16 2 9

OLYM NA 0 NA NA NA NA NA NA NA NA

PASA 10 2 13 1 75 5 5 2

SNPA 9 2 16 24 2 43 1 9 1 4

WHPA NA 0 NA NA NA NA NA NA

### DRAFT

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16 

## 5.2. **Average Factor Contributions on Summer (Quarter 2 and 3, April** 

**– September) Season Days with Elevated PM2.5** 

Table 5 .7. Average factor percent contribution to total PM2.5 mass at STN sites on summer

(Apr.

– Sept.) days when PM2.5 was above 15 ug/m3.

Site NameAverage Measured Mass (ug/m3)Sample CountModel Residual Unattributed mass (%)Ammonium Chloride(%)Calcium Rich(%)Copper Rich(%)Fireworks(%)Gasoline Vehicles(%)Iron Rich(%)Nitrate Rich(%)Organic Pyrolysis (OP) Rich(%)Potassium Rich(%)Sea Salt(%)Secondary Aerosol,Marine Related(%)Soil(%)Soil (Ca Rich)(%)Sulfate Rich(%)Sulfate Rich, Fuel Oil Related(%)Urban Unidentified(%)Wood Smoke(%)Zinc Rich(%)

Boise 22 10 28 5 2 21 5 16 4 1 18

Klamath_Falls NA 0 NA N

A N

A N

A N

A N

A

Lakeview NA 0 NA N

A N

A N

A N

A N

A N

A N

A

Oakridge 25 2 12 6 48 2 1 7 24

Portland 19 1 5 20 2 14 0 4 4 7 44

Bountiful 19 4 22 4 3 12 0 2 10 4 23 9 2 9

Salt_Lake_City 19 5 24 1 5 10 3 2 13 3 28 4 1 6

Lindon 22 3 15 0 1 27 1 6 9 8 11 16 1 3 1

Vancouver 26 1 85 0 2 1 0 2 3 7 0

Seattle_DW 18 2 18 5 8 2 3 23 1 7 5 12 16 0

Seattle_BH 17 5 23 0 10 7 4 10 0 7 4 14 3 16

Tacoma_SL 15 1 10 14 4 4 6 4 20 24 9 4 0

Tacoma_AL NA 0 NA NA N

A N

A N

A N

A N

A N

A N

A N

A

Marysville 17 2 36 13 8 1 0 10 1 21 2 8

Yakima NA 0 NA NA N

A N

A N

A N

A N

A N

A N

A N

A N

A

### DRAFT

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17 Table 5 .8. Average factor percent contribution to total PM2.5 mass at IMPROVE sites on

summer (Apr.

– Sept.) days when PM2.5 was above 7.5 ug/m3.

Site

NameAverage Measured Mass (ug/m3)Sample CountModel Residual Unattributed mass (%)Iron Rich (%)Mixed, Nitrate& Sulfate (%)Mixed, OC , EC & Sulfate (%)Mixed, Organic Carbon & Nitrate (%)Mixed, Organic Carbon & Soil (Ca Rich) (%)Mixed, Sulfate & Soil (%)Mixed, Sulfate & Soil (Ca Rich) (%)Nitrate Rich (%)Organic Pyrolysis (OP) Rich (%)Sea Salt (%)SecondaryAerosol, Marine Related (%)Secondary Organic Aerosol & Wood Smoke (%)Soil (%)Soil (Ca Rich) (%)Sulfate Rich (%)Sulfate Rich, Fuel Oil Related (%)Zinc Rich (%)

CRMO 11 14 4 1 0 76 14 4 0

SAWT 14 25 9 1 3 64 18 4 1

CABI 12 14 10 0 66 16 8 0

FLAT 16 21 11 1 66 14 7

GLAC 13 34 6 3 1 60 18 12

MONT 15 27 6 2 64 23 6

JARB 10 23 16 1 26 45 7 3 1

CRLA 12 11 28 64 4 3 1

HECA 16 18 11 1 69 8 11 0

KALM 12 10 14 2 10 55 3 17 0

MOHO 13 9 20 7 11 1 61

STAR 10 17 6 1 2 74 11 6

THSI 12 29 6 5 46 37 7

LABE 14 17 4 2 1 1 81 6 6 0

REDW 11 22 9 3 31 3 35 20

COGO 10 17 8 8 2 0 6 38 13 17 8 0

CORI 10 34 5 1 1 8 40 30 12 2

MAKA 9 17 1 2 9 17 29 4 22 17

MORA 9 15 10 2 2 49 6 10 20 1

NOCA 12 11 7 2 65 3 1 21

OLYM 11 2 15 5 0 2 66 7 4 1

PASA 13 11 9 0 72 10 8 0

SNPA 10 16 10 2 4 56 6 8 13 1

WHPA 14 9 7 71 7 4 10 0

### DRAFT

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18 

## 5.0 References

Amato, Fulvio, and Philip K. Hopke , Source apportionment of the ambient PM2.5 across St.

Louis using constrained positive matrix factorization, Atmosp heric Environment 46, 329

- 337

, 2012.

Han, Yongming, Junji Cao, Judith C. Chow, John G. Watson, Zhisheng An, Zhangdong Jin,

Kochy Fung, and Suixin Liu, Evaluation of the thermal/optical reflectance method for

discrimination between char

- and soot

- EC, Chemosphere 69

, 569

– 574 , 2007.

Hwang

, InJo and Philip K. Hopke , Estimation of source apportionment and potential source

locations of PM2.5 at a west coastal IMPROVE site, At mospheric Environment 41, 506

– 518

, 2007.

Karanasiou, A.A., P.A. Siskos, and K. El eftheriadis , Assessment of source apportionment by

Positive Matrix Factorization analysis on fine and coarse urban aerosol size fractions,

At mospheric Environment 43, 3385

– 3395 , 2009.

Kim, Eugene

, and Philip K. Hopke

, Characterization of fine particle sou rces in the Great Smoky

Mountains area

, Science of the Total Environment 368, 781

– 794 , 2006.

Kim, Eugene

, and Philip K. Hopke

, Source characterization of ambient fine particles at multiple

sites in the Seattle area

, A tmospheric Environment 42, 6047

– 6056

, 2008a.

Kim, Eugene

, and Philip K. Hopke

, Characterization o f Ambient Fine Particles in the

Northwestern Area and Anchorage, Alaska

, J . Air & Waste Manage. Assoc. 58, 1328

– 1340

, 2008b.

Kim, Eugene, Philip K. Hopke, and Eric S. Edgerton, Improving source identification of Atlanta

aerosol using temperature resolved carbon fractions in positive matrix factorization, At mospheric

Environment 38, 3349

– 3362 , 2004

.

Kim, Eugene , Katarzyna Turkiewicz, Sylvia A. Zulawnick, and Karen L. Magliano

, Sources of

fine particles in the South Coast area, California

, Atmosph eric Environment 44, 3095

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Kotchenuther, Robert, EPA Region 10 Guidance for the Use of Receptor Models to Support

Policy and Regulatory Decisions , December 2009.

Lee, Sangil, Wei Liu, Yuha ng Wang, Armistead G. Russell

, and Eric S. Edgerton , Source

apportionment of PM2.5: Comparing PMF and CMB results for four ambient monitoring sites in

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– 4137 , 2008.

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### DRAFT

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Maykut, N., J. Lewtas, E. Kim, and T. Larson, Source Apportionment of PM2.5 a t an Urban

IMPROVE Site in Seattle, Washington, Environ. Sci. Technol. , 37, 5135

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.

Ramadan, Z., Son g, X.

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Shi, Guo

- Liang, Xiang Li, Yin

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wilderness, At mospheric Environment 38, 5901

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.

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### DRAFT

- Monday, March 6, 2012

20 

## A. **Appendices**

A1. Chemical Profiles for PMF Factors at STN Sites . See associated excel file “ Appendix A1

–

EPA PNW STN PMF Profiles.xlsx ”

.

A2. Chemical Profiles for PMF Fac tors at IMPROVE Sites . See associated excel file

“ Appendix A2

– EPA PNW IMPROVE PMF Profiles.xlsx ”

.