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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

Source 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 Monitoring 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 present, 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 variability 

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

There were multiple goals 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 nonattainment 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, some 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 using a consistent data preparation methodology and modeling 

protocol allows for better 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 analysis 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 ug/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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Pacific Northwest 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 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 Sites and Data. 

2.1. Maps of the monitoring locations used in this analysis:

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

analysis. Figures 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 information 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 Pacific Northwest showing the 15 Speciation Trends Network (STN)

monitors used for modeling in this analysis.
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Figure 2.2. Map of the Pacific Northwest showing the 24 IMPROVE monitors used for 

modeling in this analysis.
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2.2. Metadata information:

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 installation of an updated carbon 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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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 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/). Data were downloaded on 1/10/2012. 

• The date range of data extracted for the three sites was 1/1/2007 – 12/31/2010. The 

beginning of this date range was chosen to coincide with the first year where carbon 

measurements in the STN network switched to the IMPROVE methodology. 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 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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• 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 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 Methodology

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 Decisions”, 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 species 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) ratios less than 0.2 was

strengthened to 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 that 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 requirement to 0.30 takes care of many species with frequent data 

near the MDL. The 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+, SO42-

, 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 SO42-. The chemical species 

that were retained were chosen based on either higher data completeness 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. 

• Data from July 4 and 5 were removed to mitigate the influence of fireworks 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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User setting for these model runs were 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 solution. 

• 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 of 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, wood burning sources), or multiple 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/classification 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 understanding 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 aerosol 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 too literally or with exclusivity.

For the 15 AQS sites modeled in this analysis there were 18 different factors identified, and for 

the 24 IMPROVE sites modeled there were 17 different factors identified. Tables 5.1 and 5.3 list

the names given to each factor 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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number and types of factors found at each location for AQS and IMPROVE sites, respectively. 

Tables 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 Northwest 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 summer. 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 overlaid 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 NH4.

Calcium Rich Factor mass dominated 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, SO4, and/or NO3. 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 NO3 and NH4.

Organic Pyrolysis Factor mass dominated by the sum of OC fractions (often > 95% of mass). OC 
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(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 from 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 SO4, NO3, 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 SO4 and NH4.

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, SO4 and NO3. 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 dominated 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 higher 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; Kim et al., 2004; Maykut et al., 2003; Kim 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 [Hwang and Hopke, 2007 (diesel); Kim and Hopke, 2006 

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

widely used as an additive in lubricating oils for diesel engines, for gasolinepowered engines in motor vehicles and other machines. 
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Table 5.2. Number and type of factors found at each STN site.

Site Name 

Number of Factors FoundAmmonium ChlorideCalcium RichCopper RichFireworksGasoline VehiclesIron RichNitrate Rich

Organic Pyrolysis (OP) RichPotassium RichSea SaltSecondary Aerosol, Marine RelatedSoil Soil (Ca Rich)Sulfate RichSulfate Rich, Fuel Oil RelatedUrban UnidentifiedWood 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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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, SO4, and/or NO3. 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)].

Mixed, Nitrate & Sulfate Factor mass dominated 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 & 

Nitrate 

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 elevated 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 dominated 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 – 53 % of factor mass with no 

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

amounts of SO4, NO3, 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; Kim et al., 2004; Maykut et al., 2003; Kim 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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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

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

role in factor identification at trace levels. Approximate 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 species 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. Factor identification is unclear. Previous work 

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

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

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

is widely used as an additive in lubricating oils for diesel engines, for 

gasoline-powered engines in motor vehicles and other machines. 
DRAFT - Monday, March 6, 2012

13

Table 5.4. Number and type of factors found at each IMPROVE site.

Site 

Name

Number of Factors FoundIron RichMixed, Nitrate & SulfateMixed, OC , EC & SulfateMixed, Organic Carbon & Nitrate Mixed, Organic Carbon & Soil (Ca Rich)Mixed, Sulfate & Soil Mixed, 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     
DRAFT - Monday, March 6, 2012

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 Name

Average 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 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 - Monday, March 6, 2012

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 

Name

Average 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 - Monday, March 6, 2012

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 Name

Average Measured Mass (ug/m3)Sample Count

Model 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 - Monday, March 6, 2012

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 

Name

Average 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 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 - Monday, March 6, 2012

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, Atmospheric 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, Atmospheric Environment 41, 506–518, 

2007.

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

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

Atmospheric Environment 43, 3385–3395, 2009.

Kim, Eugene, and Philip K. Hopke, Characterization of fine particle sources 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, Atmospheric Environment 42, 6047– 6056, 2008a.

Kim, Eugene, and Philip K. Hopke, Characterization of 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, Atmospheric 

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, Atmospheric Environment 44, 3095-3100, 

2010.

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Policy and Regulatory Decisions, December 2009. 

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

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Lewis, C.W., Norris, G.A., Henry, R.C., and Conner, T.L., Source Apportionment of
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Phoenix PM2.5 Aerosol with the Unmix Receptor Model. Journal of the Air & Waste

Management Association 53, 325-338, 2003.

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IMPROVE Site in Seattle, Washington, Environ. Sci. Technol., 37, 5135-5142, 2003. 

Ramadan, Z., Song, X.-H., and Hopke, P.K., Identification of Sources of Phoenix

Aerosol By Positive Matrix Factorization. Journal of the Air & Waste Management

Association 50, 1308-1320, 2000.

Shi, Guo-Liang, Xiang Li, Yin-Chang Feng, Yu-Qiu Wang, Jian-Hui Wu, Jun Li, and Tan Zhu, 

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Liu, Source apportionment of PM2.5 and selected hazardous air pollutants in Seattle, Science of 

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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 Factors at IMPROVE Sites. See associated excel file 

“Appendix A2 – EPA PNW IMPROVE PMF Profiles.xlsx”.