Appendix B: Source Apportionment Methods Page B-1

Appendix B: Source Attribution by Receptor-Based 

Methods 

Serpil Kayin

Mid-Atlantic Regional Air Management Association (MARAMA) 

Baltimore, MD. 

Richard L. Poirot

Vermont Department of Environmental Conservation (VT DEC) 

Waterbury, VT. 

ABSTRACT 

This section summarizes a number of receptor model results for rural and urban sites within and upwind of 

the MANE-VU region. These include results from multivariate mathematical models applied to speciated 

aerosol data from individual monitoring sites, as well as ensemble trajectory evaluations, applied to help 

evaluate and interpret the mathematical model results, and to identify the most prominent regional origins of 

these sources. A number of common source categories were identified, which have discernable impacts on 

average PM2.5 mass concentrations and visibility impairment at most Northeastern monitoring sites. These 

include: 

Windblown Dust: a minor contributor to average fine mass, but clearly identified at all sites, with highest 

short term impacts often associated with Sahara dust transport. 

Sea Salt: a minor contributor to fine mass, but clearly identified at coastal and near coastal sites. It can have 

significant visibility impacts at coastal sites like Acadia and Brigantine on the best visibility days. 

Oil Burning: a minor contributor to fine mass, but clearly identified at many MANE-VU sites, especially 

sites within and downwind of the Northeast urban corridor. 

Ammonium Nitrate: a small to moderate contributor to average fine mass, with regional influences at rural 

sites from upwind agricultural ammonia-emitting areas, and significant local source contributions in urban 

areas. 

Wood Smoke: a small to moderate contributor to average fine mass, with contributions typically higher in 

rural areas than urban areas, winter peaks in northern areas from residential wood burning, and occasional 

large summer impacts at all sites from wildfires. 

Motor Vehicles and Secondary Organics: a moderate to large contributor to average fine mass, with 

discernable influence from both gasoline and diesel vehicles in urban areas. At forested rural sites, biogenic 

organics are likely a more important contributor. 

 

Coal Burning: (including primary aerosol and secondary aerosol formation): the largest mass-contributing 

and visibility-impairing source category at most sites, with contributions primarily from utility and industrial 

sources in western MANE-VU, northern VISTAS and the Midwest RPO planning regions.
Appendix B: Source Apportionment Methods Page B-2

Appendix B: Source Attribution by Receptor-Based Methods 

Emissions-based air quality models begin with emissions inventories and act on them in a 

“forward” direction with atmospheric physical and chemical process to predict downwind 

concentrations at multiple “receptor locations.” Once validated by comparison to ambient 

measurement data, these models can be used for apportioning ambient pollutant concentrations to 

specific sources, as well as for evaluating potential effects of future changes in emissions or 

meteorology. Receptor-based models begin with ambient measurement data at one or more 

receptor locations and work “backward” to identify sources contributing to historical ambient 

pollutant concentrations at the receptor locations. Receptor models aren't usually used to predict 

effects of future emissions changes, but can be applied to long historical records, providing a longterm “climatological” indication of past source-receptor relationships and to evaluate the effects of 

historical emissions changes, thus providing a valuable complement to emission-based models for 

determining effective future emissions control strategies. 

Two general categories of receptor methods include multivariate mathematical models and 

ensemble backward trajectory techniques. Multivariate models, such as Chemical Mass Balance 

(CMB), Principal Component Analysis (PCA), Positive Matrix Factorization (PMF) and UNMIX, 

are typically driven by the variations in multiple species data in multiple observations at one or 

more sites. Ensemble trajectory techniques, such as Cluster Analysis, Potential Source Contribution 

Function (PSCF) and Residence Time Analysis (RTA) are driven by large numbers of backward air 

trajectory calculations at one or more sites, which are sorted and/or aggregated as a function of 

measured or modeled pollutant concentrations at the receptor, or grouped as a function of similar 

upwind locations. 

The CMB multivariate model requires input of measured source composition profiles, while 

PCA, PMF and UNMIX develop the source profiles during model operations. An advantage of 

CMB is that the identified source contributions are unambiguous, since the named sources are 

included as model input. A disadvantage is the required assumption that the emissions source 

profiles are accurate and include all the relevant emissions source influences. PCA, PMF and 

UNMIX have the advantage that the source profiles need not be known in advance, and the 

disadvantage that the resulting “sources” require subjective interpretation by the modelers to 

identify what these source influences actually represent. Resulting sources may represent an 

individual point source, source category, source region, meteorological influence, measurement or 

data processing artifacts or various combinations of the above. An additional characteristic of these

multivariate models is that the resulting sources have fixed or constant chemical compositions. This 

presents a particular problem for sources emitting a combination of primary aerosol species (emitted 

directly in particle phase) and secondary aerosol precursors (converting from gas to particle phase 

in the atmosphere). The problem is not so much the mix of primary and secondary species, but 

rather the variable rate of secondary aerosol formation, which can result in a variable “virtual source 

profile” at downwind receptor locations. Consequently, models like PMF and UNMIX may divide 

a source influence into 2 or more “source components”, each with constant but different chemical 

compositions, representing different degrees of secondary aerosol formation. 

Because of these complexities in source interpretation, and because the multivariate models 

typically rely entirely on the measured chemical compositions without regard to meteorology, the 

ensemble trajectory techniques (which rely on meteorology-only) are especially useful for 

interpreting and/or evaluating the multivariate model results. 
Appendix B: Source Apportionment Methods Page B-3

B.1. Multivariate Mathematical Results Relevant to the MANE-VU Region 

B.1.1. Introduction 

This section summarizes source apportionment studies conducted for sites within the MidAtlantic/Northeast Visibility Union (MANE-VU) region, and a few neighboring sites within the upwind or down-wind influence area of MANE-VU. The intent is to provide a general overview of the 

source categories identified in these various studies, and to provide an indication of their relative 

contributions to fine mass concentration and visibility impairment in the MANE-VU region. For 

additional detail, the reader is encouraged to consult the original references, as well as the trajectory 

interpretations in Section B.2 of this report. 

Many of the results presented here were from an exploratory “Phase 1” PM2.5 source 

apportionment study previously sponsored by MANE-VU and the Midwest RPO (Coutant et al. 

2002) which was also extensively used in a subsequent U.S. Environmental Protection Agencysponsored review report (Coutant et al. 2003). These findings were also referenced in a U.S. 

Environmental Protection Agency Report (Air Quality Data Analysis Technical Support Document 

for the Proposed Interstate Air Quality Rule, January 2004) and in a recent Clean Air Act Advisory, 

Science and Technology Subcommittee report (Synthesis of Air Quality Assessments: Identification 

of Important Contributors to Ozone and PM Nonattainment, and Regional Haze, October 15, 2004). 

Other studies and peer reviewed journal articles are also cited in this summary and referenced at the 

end of the document. 

B.1.2. Background 

A number of mathematical receptor models and ensemble trajectory analysis techniques 

have commonly been used in the studies summarized below in order to apportion the PM2.5 mass 

concentrations (and, hence, the light extinction values) into components attributable to the most 

significant source categories, and to indicate the most common origins of these sources. The 

mathematical models include PMF (Positive Matrix Factorization), UNMIX, APCA (Absolute 

Principal Component Analysis), and CMB (Chemical Mass Balance). Some studies also used these 

tools in conjunction with trajectory transport analysis such as PSCF (Positive Source Contribution 

Function), RTA (Residence Time Analysis), Cluster Analysis and other ensemble back-trajectory 

techniques, to assist in interpreting and identifying primary locations of these sources. An extensive

literature explaining these ensemble trajectory techniques is available, but these explanations are not 

referenced in this summary. A recently developed “Combined Aerosol Trajectory Tool” (CATT) 

described by Husar et al. (2004) includes options to calculate a wide range of ensemble trajectory 

metrics for all aerosol species data, and any “user-submitted” receptor model results derived from 

these data for any or all IMPROVE and/or EPA STN sites 

The main goal of the studies summarized here is to describe and quantify the major source 

categories contributing to the observed concentrations of fine particulate matter in the atmosphere. 

This is achieved by mathematically modeling the day to day variation in the PM2.5 mass 

concentration (and 10 to 30 constituent species) as a mixture arriving from the major sources to the 

receptor point. At least initially, it is assumed that each source contributing to the PM2.5

concentration contributes to the observed species concentrations with approximately fixed ratios. 

The list of the ratios of the species mass to the total mass contributed by the source is referred to as 

a source profile. Sources can be identified through these profiles, the associated time series (the 

day-by-day variations in the sources mass contribution), and the magnitude of the source. Further 

evaluation of the results with local surface meteorological data (for local source influences) and 
Appendix B: Source Apportionment Methods Page B-4

back trajectory techniques (for more distant source influences) provides additional insights and 

often leads to refinements or revisions in the original source interpretations. 

Typically, the studies included in this summary used quality assured and validated data, 

conducted trends analyses for data consistency, and considered additional meteorological and 

transport factors, emissions inventory implications and monitoring site specifics to complement the 

source apportionment results. For any given site, and overall regionally, the results are generally 

consistent when comparable data sets were used. However, methodologies vary considerably among 

studies. In addition, the primary goals of apportioning total PM2.5 or visibility are quite different. 

Hence, there are difficulties with making direct comparisons. For example, the modeled source 

profiles typically include mixtures of several different aerosol species with different extinction 

efficiencies and/or hygroscopic growth functions. Several of the reviewed studies have included 

estimates of source contributions to PM2.5 mass on best 20% and worst 20% visibility days, but this 

does not necessarily reflect the visibility impacts of the sources on these days, since sulfates have a 

much larger impact on visibility than the same mass of the other pollutants. 

In this summary, to provide consistency and direct comparability, the data presented are the 

source apportioned PM2.5 mass (and percentage of PM2.5 mass). Quoting from the recent Clean Air 

Act Advisory, Science and Technology Subcommittee report (Synthesis of Air Quality 

Assessments: Identification of Important Contributors to Ozone and PM Nonattainment, and 

Regional Haze, dated October 15, 2004): 

“Because regional haze is linked so closely to PM2.5, few source apportionment studies look 

at haze impacts separately. Nevertheless, some observations about the relationship between 

PM2.5 and haze are appropriate. Light extinction is a function of the particular components 

of PM2.5 as well as relative humidity. While the sources responsible for PM2.5 are the same 

as those for regional haze, the relative importance of those sources varies somewhat in 

relation to their propensity to scatter light (e.g., sulfate contribution to regional haze is 

greater than to fine particle mass). Work by IMPROVE over the past decades has shown that 

light extinction almost everywhere across the country is primarily due to sulfate; organic 

carbon is next most important almost everywhere. Regional differences become important in 

looking at relative importance of the other species; in the west, crustal material and coarse 

mass are third in importance. In the east, nitrate is the third-most important, followed by 

crustal material. However, because the general importance of regional haze species is the 

same (for most of the country) as that for PM2.5, and because impaired visibility has adverse 

effects on public welfare in both urban and rural areas, control measure priorities for 

reducing human health and visibility effects of PM2.5 are strongly convergent.” 

This compilation summarizes several reports and articles focusing specifically on source 

apportionment results in the MANE-VU region and its surroundings. A more detailed review of 

articles, reports and recent presentations on the nation as a whole can be found in Coutant et al. 

(2003). In addition to those, the summary below adds a few new articles for the MANE-VU region. 

Figure B-1 below shows the locations of all sites referred to in this summary. Tables provided 

throughout this summary reference the original study, period of the data and analytical technique 

used, mean mass measured, and apportioned mass into source types by mass and percent of the 

total. 
Appendix B: Source Apportionment Methods Page B-5

A few receptor sites were studied repeatedly, such as Underhill (Vermont), Brigantine (New 

Jersey), and Washington (DC). The monitoring data were primarily taken from the IMPROVE and 

STN networks. NESCAUM (2003) provides a summary and comparison between the sampling 

techniques employed in data resulting from these networks. Typically, more detailed measurement 

data leads to more detailed source characterization. For example, inclusion of the thermally 

stratified TOR carbon fractions (available for IMPROVE sites only), has allowed for separate 

detection of gasoline and diesel motor vehicle sources – especially in urban areas like Seattle 

(Maykut et al., 2003), Toronto (Poirot and Brook, 2004), Atlanta and Washington DC (Kim and 

Hopke, 2003, 2004), and at the “near urban” Brigantine site (Kim and Hopke, 2004). For the recent 

Toronto studies, the detailed aerosol measurements included additional species including high 

quality NH4 and a number of organic ions (oxalate, succinate, etc.), which allowed refinement of a 

secondary sulfate source into neutral and acidic components, with a substantial “secondary organic 

aerosol” component associated with the acidic sulfates (Lee et al, 2003, Poirot and Brook, 2004). 

The general results from many of the studies were found to be similar as the methodologies 

have become more mature and data sets broader and more comparable, and the collective 

experience and knowledge of the modeling community advances. Following Coutant (2003) the 

Figure B-1. Locations of the Monitoring Sites Reviewed in This Report 
Appendix B: Source Apportionment Methods Page B-6

sources identified grouped into seven categories: sulfate/coal, secondary organic 

compounds/mobile, nitrate, biomass burning, industrial, crustal and salt, and other/not identified. 

Except in a few rare cases, where a local source of sulfate is known, “sulfate/coal” was 

identified as regional secondary transport of sulfate primarily attributable to coal-fired power plants 

in the ‘Midwest” (indicated by presence of S and Se). Some studies found a single large “sulfate” or 

“coal” source influence, accounting for high fractions of both total Se (primary) and SO4

(secondary) aerosol concentrations. Several other studies tended to split an overall coal burning 

influence into two “primary” and “secondary” components, each of which have constant 

compositions, but mixed in different proportions on different sample days. In cases where the 

analysis was based on longer-term (5-10 year) historical records, the sulfate/coal sources typically 

showed reductions over time. 

A large “secondary organic matter/mobile” source was indicated by the presence of OC, EC 

and sometimes a small fraction of crustal elements (like Fe, Ca, and Si which may be associated 

with road dust) was also a major source category for nearly all sites. Generally, the sources of 

carbonaceous aerosols have not been clearly identified in many of these studies, especially at rural 

sites, and initial interpretations of “mobile source” influence have frequently been revised following

more detailed analyses to indicate other categories like “biomass combustion”. So the general term 

“secondary organic matter/mobile” employed in this summary should not be taken literally. Only a 

few studies were able to separate mobile source into gasoline and diesel sources, and the 

contributions of these specific source categories, even in urban areas, are typically smaller than the

large general OC sources such as those identified in the exploratory Coutant et al., 2002 studies. 

“Nitrate” was also found to be a significant source (in those studies that included NO3 as an 

input variable). Typically, most of the NO3 tends to break out into a separate general “source” 

category, assumed to be ammonium nitrate, and the model does not associate this pollutant with the 

specific combustion, motor vehicle or agricultural sources from which its precursors originate. This 

may be related to the complex combination of mixed emission sources, temperature and 

atmospheric chemistry conditions needed for accumulation of ammonium nitrate in the ambient air 

in the Northeast. The receptor models have more difficulty differentiating between sources of 

complex secondary pollutants than primary pollutants. In the Eastern US, regional ammonium 

nitrate concentrations are highest in and immediately downwind of agricultural areas in the northern 

Midwest. There also appear to be significant local urban sources of nitrate precursors in 

Northeastern urban areas. 

The “biomass burning” category includes residential wood smoke and forest fires indicated 

by the presence of OC, EC and K. Fireworks emit similar species, and are occasionally included 

with “wood smoke” sources in receptor model results, unless care is taken to screen out dates (July 

3-5) of “obvious fireworks impacts.” The size of the biomass burning source varies considerably 

from site to site, but it is usually higher in rural areas, as expected. Winter residential wood burning 

impacts can also be substantial in small northeastern villages located in deep mountain valleys like 

Rutland, VT (Allen et al., 2004). 

The industrial source category includes a variety of small sources characterized by elemental 

carbon and trace metals, such as smelters (presence of Cu, Zn, Mn, As, etc), incinerators (presence 

of OC, EC, PB, and Zn), oil burning (presence of Ni and V), and in some cases industrial salts. 

Frequently, the industrial sources are associated with known local sources. Residual oil burning 

impacts from both industrial and utility sources are especially evident along the East Coast urban 

corridor. 
Appendix B: Source Apportionment Methods Page B-7

A “crustal” (dust, soil, etc.) source category is indicated by the presence of Si, Al, Ca, Fe, 

and Ti. A crustal source category is almost always identified in all receptor modeling studies and 

sites. In the Northeast, it typically accounts for a small fraction of fine mass, but a relatively large 

fraction of the trace elements listed above (which are also assumed to result predominantly from 

soil in the IMPROVE soil formula). Highest dust impacts at rural northeastern sites often result 

from very long-range transport of African, Asian or SW North American dust storms. Reintrained 

road dust impacts are indicated in some urban areas. 

A “sea salt” source was identified by the presence of Na and Cl. Sea salt is consistently 

identified at Northeastern coastal sites (Acadia, Brigantine) and at near-coastal sites (NYC, 

Washington DC, Shenandoah, Dolly Sods, Gt Smokey Mtns.). The modeled sea salt compositions 

at these sites typically exhibit signs of chemical aging, with loss of chloride ion content and excess

nitrate and sulfate, compared to fresh marine emissions. A curious feature of the sea salt sources 

identified (at 6 sites) in the Coutant (2002) studies was that it that it showed a significant increase 

over time at all sites. Road salt sources, often mixed with crustal elements, are evident at several 

northern inland urban sites. 

B.1.3. Site Summaries 

The following summaries provide a brief description of the monitoring site, period of the 

data used for the analysis, analysis technique(s) used, mean PM2.5 mass, source apportioned PM2.5

mass according to the identified source types and fractions of the total mass (in parenthesis). The 

source of the information is referenced. In addition, where available, additional information is 

provided regarding the major source types and their associated source regions as indicated in the 

studies summarized. In the tables, source types contributing greater than 20 percent but less than 40 

percent of the total mass are shaded yellow; sources contributing greater than 40 percent are shaded 

pink. In order to present a concise overview, some sources were renamed and/or combined. Some 

concentration values were calculated based on reported percentages. The sum of the sources may 

not equal the total due to rounding and/or modeling limitations. 

Table B-1. Acadia National Park, ME 

Sulfate/ Coal Nitrate Mobile Biomass 

Burning Industrial Crustal and Salt Miscell.

Reference

Data Period 

(Analysis 

Method)

Total

Mass

(ug/m3) 

(% 

Mass)

Regional 

Secondary 

Transport General

Secondary 

OC 

/General

Wood 

Smoke

Oil 

/Diesel 

Combustion

Other 

/General

Crustal 

/Dirt 

/Soil

Sea 

Salt

Not 

Identified

Coutant et 

al. 2002

1988-1999 

(PMF) 

7.5 

(100) 

2.3 

(30.7) 

3.9 

(52.0) 

0.3 

(4.0) 

0.4 

(5.3) 0.0 0.1 (1.3) 

0.4 

(5.3) 

0.1 

(1.3) 

The Acadia National Park IMPROVE site (44 N, 68 W) is located near the center of Mount 

Desert Island, at an elevation of 150 meters, on the southern slope of McFarland Hill. Upwind of 

the receptor, there are four small towns with residents that have home heating needs. Park visitation 

is three million people/year, with more than two-thirds visiting during June-September, creating 

high volume vehicle traffic. More than 50 large cruise ships visit Bar Harbor during summer and 

fall, and the number of cruise ships is increasing. PM2.5 concentrations at this site tend to be low, 

averaging 7.5 μg/m3. Initial results from Coutant et al. (2002) indicated that mobile sources 

dominate at this site, followed by the regional secondary sulfate. However, the reconstructed 

masses of these modeled sources are inconsistent with the model-apportioned masses, suggesting 
Appendix B: Source Apportionment Methods Page B-8

that the mobile source contribution has been overestimated while the other sources contributions are 

underestimated. 

Table B-2. Lye Brook Wilderness, VT 

Sulfate/ Coal Nitrate Mobile Biomass 

Burning Industrial Crustal and Salt

Reference

Data Period 

(Analysis 

Method)

Total

Mass

(ug/m3) 

(% 

Mass)

Primary 

Coal 

2ndary 

Coal General

2ndary 

OC 

/General 

Wood 

Smoke 

 Oil 

Burning Smelter

Incinerator 

Crustal 

/Dirt 

/Soil 

Hi Ca 

Crustal 

/Dirt 

/Soil 

Low Ca

Road 

Salt 

Coutant et 

al. 2002

1991-1999 

(PMF) 

7.6 

(100) 

0.36 

(5.2) 

3.2 

(46.3) 

2.4 

(35.1) 

0.38 

(5.5) 

0.46 

(6.6) 

0.19 

(2.8) 

0.13 

(1.9) 

0.37 

(5.3) 

0.07 

(1) 

The Lye Brook IMPROVE site (43 N, 73 W) is located in Southwestern VT at an elevation 

of 1010 meters. The site is surrounded by mountainous and forested areas and these areas are 

sparsely populated for about 20 miles in all directions from the site. The area has some home 

heating activity (in nearby valleys), but no industrial sources of pollution in the vicinity. This is a 

popular tourist region with substantial vehicle traffic. Coutant et al. (2002) estimated that regional 

secondary sulfate was the most important source type, followed by biomass burning/wood smoke. 

Table B-3. Underhill, VT 

Sulfate 

/Coal Nitrate Mobile

Biomass 

Burning Industrial Crustal and Salt Miscell. 

Reference

Data Period 

(Analysis 

Method)

Total

Mass

(ug/m3) 

(% 

Mass)

Regional 

Secondary

Transport General General

Wood 

Smoke Smelter Incinerator

Oil/ 

Diesel 

Combustion 

Other 

/General

Crustal

/Dirt 

/Soil 

Road 

Salt

Sea 

Salt

Not 

 Identified

Polissar et 

al. 2001 

1988-1995 

(PMF & PSCF) 

6.4 

(100) 

3.7 

(57.8) 

1.0 

(15.6) 

0.2 

(3.1) 

0.4 

(6.3) 

0.6 

(9.4) 

0.3 

(4.7) 

0.1 

(1.6)

0.3 

(4.7) 

Poirot et 

al. 2001 

1988-1995 

(UNMIX&RTA) 

8.4 

(100) 

5.8 

(69.0) 

1.3 

(15.5) 

0.1 

(1.2) 

0.7 

(8.3) 

0.3 

(3.6) 

0.2 

(2.4) 

Poirot et 

al. 2001 

1988-1995 

(PMF & UNMIX) 

7.9 

(100) 

4.8 

(60.8) 

1.2 

(15.2) 

0.1 

(1.3) 

0.6 

(7.6) 

0.9 

(11.4) 

0.3 

(3.8) 0.0 

Song et al. 

2001 

1988-1995 

(PMF) 

8.0 

(100) 

4.9 

(61.3) 

1.2 

(15.0) 

0.2 

(2.5) 

0.6 

(7.5) 

0.5 

(6.3) 

0.2 

(2.5) 

0.3 

(3.8) 0.0 0.1 (1.3)

Gao et al. 

2004 

2001-2003 

 (PMF & PSCF) 

5.5 

(100) 

2.6 

(47) 

0.6 

(11) 

1.1 

(20) 

0.1 

(1) 

0.5 

(9) 

0.2 

(4) 

0.4 

(7) 

Gao et al. 

2004 

2001-2003 (best 

visibility days) 

(PMF & PSCF) 

(47) (8) (25) (1) (2) (9) (8) 

Gao et al. 

2004 

2001-2003 (worst 

visibility days) 

(PMF & PSCF) 

(80) (10) (7) (3) 

Samples were collected at a remote background site in Underhill, Vermont (45 N, 73 W, 

elevation 400 m) using the IMPROVE monitoring protocol. The first three studies summarized 

above (Poirot et al. 2001, Polissar et al. 2001, and Song et al. 2001) used data from the same period 

but employed different analysis techniques. The last study (Gao et al. 2004) used a more recent data 

set. Results were very consistent, showing similar chemical composition profiles and temporal 

variations, providing confidence in the results obtained by the different techniques. The most 

important source type was found to be regional secondary sulfate, followed by wood smoke. This is 

a rural northern latitude area with substantial wood burning activities. The effects of oil burning and 

industrial sources were also found in the area. The analysis of more recent data by Gao et al. (2004) 

differentiated between two types of sulfur-rich secondary aerosol; in Table B-3 the total of these 
Appendix B: Source Apportionment Methods Page B-9

two were used. They also separated subsets of the results for the best and worst 20% visibility days. 

Secondary sulfate was found to be significantly more important during the worst days. Gao et al. 

also noted a substantial reduction of about 35% in the total fossil fuel (coal + oil) source influence

for the 2001-2003 analyses compared to the earlier PMF and UNMIX results based on 1989-95 

data. 

Table B-4. New York, NY 

Sulfate 

/Coal Nitrate Mobile 

Biomass 

Burning Industrial Crustal and Salt Miscell. 

Reference

Data Period 

(Analysis 

Method)

Total

Mass

(ug/m3) 

(% 

Mass)

Regional 

Secondary 

Transport General

Secondary 

OC 

/General General

Oil 

/Diesel 

Combustion

Other 

/General

Crustal 

/Dirt 

/Soil

Sea 

Salt General 

Coutant et 

al. 2003 

2001-2002 

(PMF) 

16.1 

(100) 

5.3 

(32.9) 

4.1 

(25.5) 

2.5 

(15.5) 

1.2 

(7.5) 

1.8 

(11.2) 

1.0 

(6.2) 

0.3 

(1.9) 

Ito et al. 

2004 

2001-2002 

(APCA & PMF) 

15.8 

(100) 

5.6 

(35.4) 

2.5 

(15.8) 

4.2 

(26.6) 

2.2 

(13.9) 

Ito et al. 

2004 

2001-2002 

(APCA & PMF) 

16.1 

(100) 

6.7 

(41.6) 

6.2 

(38.5) 

1.8 

(11.2) 

2.9 

(18.0) 

Ito et al. 

2004 

2001-2002 

(APCA & PMF) 

15.1 

(100) 

6.8 

(45.0) 

5.5 

(36.4) 

1.8 

(11.9) 

1.6 

(10.6) 

Note: The table summarizes results from four studies of three sites in New York City. 

The first and second studies in the table above looked at data from New York Botanical 

Gardens in the Bronx (an urban Speciation Trends Network site, a Met One sampler). This site is 

located in the middle of the Bronx (41 N, 74 W), a heavily populated urban area. There are local 

sources that could potentially have a significant effect on the site. These include mobile emissions, 

fuel oil (particularly in the winter), two oil-fired power plants, street cleaning, and marine 

influences. The difference between these two studies is probably attributable to the fact that only the 

first study distinguished nitrate as a source type.

The third study in the table above looked at I.S. 52 in the Bronx (an urban speciation Trends 

Network site, R and P sampler) and the fourth study focused on Queens College in Queens (an 

urban speciation Trends Network site, R and P sampler). These three sites are within six miles of 

each other. 

Results obtained from the four studies summarized above are highly consistent. All four 

studies found secondary sulfate the most important source type. The studies concluded that the 

temporal correlation across monitors in NYC varied considerably across individual PM2.5 species, 

indicating that the precision of population exposure estimates for specific elements can vary 

depending on the species. However, as expected, the constituents of secondary aerosols showed 

consistently high temporal correlations across the monitors. Other PM2.5 species that are 

constituents of major source types (soil, traffic, oil burning, and incineration) showed low to high 

correlation (Ito et al., 2004). 
Appendix B: Source Apportionment Methods Page B-10

Table B-5. Brigantine Wilderness, NJ 

Sulfate 

/Coal Nitrate Mobile 

Biomass 

Burning Industrial 

Crustal and 

Salt Miscell. 

Secondary OC 

 /General 

Reference

Data Period 

(Analysis 

Method)

Total

Mass

(ug/m3) 

(% 

Mass)

Regional 

Secondary

Transport General Diesel Gasoline

Wood 

Smoke Incinerator

Oil 

/Diesel 

Combustion

Other 

/General

Crustal 

/Dirt 

/Soil

Sea 

Salt

Not 

Identified

Song et al. 

2001 

1992-1999 

(PMF) 

11.4 

(100) 

7.1 

(62.3) 

0.9 

(7.9) 

0.6 

(5.3) 

0.9 

(7.9) 

0.5 

(4.4) 

0.2 

(1.8) 

0.1 

(0.9) 

0.9 

(7.9) 

Coutant et 

al. 2002 

1992-1999 

(PMF) 

11.6 

(100) 

5.7 

(49.1) 

3.5 

(30.2) 

1.3 

(11.2) 

0.3 

(2.6) 

0.1 

(0.9) 

0.7 

(6.0) 0.0 

Lee et al. 

2003 

1991-1999 

(PMF) 

11.4 

(100) 

7.9 

(69.3) 

1.8 

(15.8) 

0.2 

(1.8) 

0.3 

(2.6) 

0.2 

(1.8) 

0.9 

(7.9) 

Poirot & 

Wishinski 

2002 

1991-1999 

(UNMIX & RTA)

10.7 

(100) 

5.3 

(50) 

0.8 

(7) 

1.0 

(9.2) 

1.4 

(12.7) 

0.2 

(1.9) 

0.4 

(3.9) 

0.4 

(3.8) 

0.5 

(4.6) 

0.7 

(7.0 

Poirot & 

Wishinski 

2002 

1991-1999 (Best 

Visibility Days) 

(UNMIX & RTA)

(% mass on 

20% days) (29) (4.3) (12.1) (13.5) (3.4) (6.3) (5.6) (6.0) (20.1)

Poirot & 

Wishinski 

2002 

1991-1999 (Worst 

Visibility Days) 

(UNMIX & RTA)

(% mass on 

20% days) (60.9) (6.2) (5.4) (13.2) (1.1) (2.3) (3.5) (3.7) (3.5) 

Kim & Hopke 

2004 

1992-2001 

(PMF&PSCF) 

11.2 

(100) 

6.7 

(59.8) 

0.4 

(3.6) 

0.3 

(2.7) 

1.4 

(12.5) 

0.2 

(1.8) 

0.1 

(0.9) 

1.3 

(11.6)

Five studies analyzed samples that were collected at the IMPROVE site located in the 

Brigantine Wilderness (New Jersey) since 1991. This site is located near the Atlantic Ocean, 12 km 

northwest of Atlantic City, 90 km southeast of Philadelphia, and 150 km south of New York City. 

Highways are closely situated to the north, south, and west of the monitoring site. The last study, in

contrast to the previous two-carbon-fraction analyses, included eight temperature-resolved carbon 

fractions. This helped distinguish between the diesel and gasoline mobile source types. 

Sulfate-rich secondary aerosols are the largest PM2.5 (and hence regional haze) source, 

consistently accounting about 60% of the mass in all the studies of Brigantine that included high 

PM days. The Poirot-Wishinski analysis of the 20% best visibility days showed much lower sulfate 

influence. The Potential Source Contribution Function (PSCF) analysis (Kim and Hopke, 2004) 

shows the source areas and pathways of sulfate-rich secondary aerosols, including the regional 

influences of the biogenic as well as anthropogenic secondary aerosol arriving from the Southeast 

and Midwest. Back-trajectories indicate that the elevated airborne soil impact on Brigantine is likely

to be caused by both Asian and Saharan dust storms. The impacts from local sources (mobile, 

biomass, and industrial) are also seen using PMF results combined with the conditional probability 

plots. 

Table B-6. Baltimore-Washington Corridor 

Sulfate 

/Coal Nitrate Mobile 

Biomass 

Burning Industrial 

Crustal 

and Salt Miscell. 

Reference

Data Period 

(Analysis 

Method)

Total

Mass

(ug/m3) 

(% 

Mass)

Regional 

Secondary 

Transport Local General

Secondary 

OC 

/General

Wood 

Smoke Smelter General General

Chen et al. 

2002 

1999-2001 

(UNMIX&BT) 

13.0 

(100) 

5.3 

(40.8) 

0.9 

(6.9) 

2.0 

(15.4) 

1.8 

(13.8) 

1.6 

(12.3) 

0.2 

(1.5) 

From 1999 to 2001, samples were collected at Fort Meade (39 N, 77 W; elevation 46 m), 

Maryland. Fort Meade is in a suburban area located between Baltimore and Washington. Fort 

Meade is generally downwind of the Washington, DC area and the highly industrialized Midwest. 
Appendix B: Source Apportionment Methods Page B-11

The PM2.5 at this site is expected to be from both local and regional sources. Measurements over a 

two year period include eight seasonally representative months. 

The authors conclude the predominate source affecting this site is an aged sulfate source 

more regional in character, resulting from emissions in the Midwest. The aged sulfate moves into 

Maryland from the north, while the fresh SO2/sulfate mixture likely originates from proximate 

urban areas north of Fort Meade during stagnant air conditions. Wintertime wood burning occurs 

more in rural areas of Virginia and West Virginia, and the mobile-related factors are dominated by 

traffic emissions on the nearby highways. A small industrial/smelter source comes from the 

industrial corridor to the northeast of the site. 

Chen et al. 2002, indicate summertime PM2.5 at Fort Meade is dominated by the regional 

sulfate source. The local contribution (mobile sources plus local sulfate) increases from less than 

30% in summer to more than 60% in winter. Though high PM2.5 episodes were observed in both 

summer and winter, the relative importance of local and regional sources could be very different. 

Table B-7. Washington, DC 

Sulfate 

/Coal Nitrate Mobile Biomass Burning Industrial 

Crustal and 

Salt Miscell. 

Secondary OC 

/General

Reference

Data 

Period 

(Analysis 

Method)

Total

Mass

(ug/m3) 

(% 

Mass)

Regional 

Secondary 

Transport 

General Diesel GasolineWood 

smoke

Vegetative

burning

Incinerator

Oil 

/diesel 

combustion

Crustal 

/Dirt 

/ Soil

Sea 

salt Other

Other 

defined

Coutant et 

al. 2002 

1989-1999 

(PMF) 

17.9 

(100) 

7.5 

(41.9) 

6.6 

(36.9) 

0.3 

(1.7) 

1.8 

(10.1) 

1.4 

(7.8) 

0.3

(1.7) 

Coutant et 

al. 2003 

2001-2002 

(PMF) 

16.7 

(100) 

7.7 

(46.1) 

1.2 

(7.2) 

4.7 

(28.1) 

1.1 

(6.6) 

1.5 

(9.0) 

0.5 

(3.0) 

Fire

works

Song et al. 

2001 

1988-1999 

(PMF) 

17.6 

(100) 

10.0 

(56.8) 

3.5 

(19.9)

1.6 

(9.1) 

0.9 

(5.1) 

0.6 

(3.4) 

0.5 

(2.8) 

0.6

(3.4) 

Kim and 

Hopke 2003

1988-1997 

(PMF) 

17.9 

(100) 

10.7 

(59.8) 

1.6 

(8.9) 

0.4 

(2.2) 

3.8 

(21.2) 

0.7 

(3.9) 

0.4 

(2.2) 

0.4 

(2.2) 

0.4

(2.2) 

Samples in the studies summarized above were collected at the McMillan IMPROVE 

monitoring site in Washington, DC. This site is located (at 39 N, 77 W) near the Potomac River, 2 

km southeast of the Lincoln Memorial, 3 km northeast of the Ronald Reagan Washington National 

Airport, and 30 m above sea level. Highways are closely situated to the north and west of the site. 

The total mass measured was relatively high in all four studies. Source apportioned results 

consistently showed that the most important source type affecting this site was the regional 

secondary sulfate, followed by the mobile/secondary OC and crustal sources. In the one study 

where diesel and gasoline sources were differentiated, gasoline dominated the mobile sources (Kim 

and Hopke, 2003). 

Table B-8. Shenandoah National Park, VA 

Sulfate 

/Coal Nitrate Mobile 

Biomass 

Burning Industrial Crustal and Salt Miscell. 

Reference

Data Period 

(Analysis 

Method)

Total

Mass

(ug/m3) 

(% 

Mass)

Regional 

Secondary 

Transport General

Secondary 

OC 

/General

Vegetative

Burning General

Crustal 

/Dirt 

/Soil

Sea 

Salt

Not 

Identified

Coutant et 

al. 2002 

1988-1999 

(PMF) 

11.8 

(100) 

4.5 

(38.1) 

3.2 

(27.1) 

3.5 

(29.7) 

0.2 

(1.7) 

0.3 

(2.5) 

0.1 

(0.8) 
Appendix B: Source Apportionment Methods Page B-12

This is an IMPROVE site (39 N, 78 W, 1098 m) surrounded by forested mountain areas 

located in the Blue Ridge Mountains. This site is located close to Skyline Drive in Shenandoah 

National Park. This is an elevated site well above the valley, where most local pollutant sources are 

located. The site is thought to be subject to long range transport of pollutants. This site generally 

remains above the valley-based inversion layer at night and early morning. 

Results obtained at this elevated site showed the dominating effect of regional secondary 

sulfate sources. Mobile or other secondary OC sources, secondary organics and vegetative burning 

sources are also important. 

Table B-9. Jefferson/James River Face Wilderness Area, VA 

Sulfate/ Coal Nitrate Mobile 

Biomass 

Burning Industrial 

Crustal 

and Salt Miscell. 

Reference

Data Period 

(Analysis 

Method)

Total

Mass

(ug/m3) 

(% 

Mass)

Regional 

Secondary 

Transport 

General

Secondary 

OC 

/General

Wood 

smoke

Incinerator

Oil 

/Diesel 

Combustion

Other 

/General

Crustal 

/Dirt 

/Soil General

Coutant et 

al. 2002 

1995-1999 

(PMF) 

14.7 

(100) 

7.2 

(49.0) 

3.7 

(25.2) 

2.2 

(15.0) 

0.3 

(2.0) 

0.4 

(2.7) 

0.5 

(3.4) 

0.5 

(3.4) 

This is an IMPROVE site located at 38 N, 79 W, at an elevation of 280 m, surrounded by a 

forested area. There is an interstate highway 4 km from the site and several other local roads nearby.

There are several mills and some open mining of gravel and sandstone 10 miles from the site. A fair 

amount of wood burning to heat homes occurs in this area during the winter. As expected, the 

results at this site showed the strong influence of regional secondary sulfate followed by mobile 

sources, secondary organics and wood smoke 

Table B-10. Dolly Sods Wilderness Area, WV 

Sulfate 

/Coal Nitrate Mobile 

Biomass 

Burning Industrial Crustal and Salt Miscell. 

Reference

Data 

Period 

(Analysis 

Method)

Total

Mass

(ug/m3) 

(% 

Mass)

Regional 

Secondary 

Transport General

Secondary 

OC 

/General

Vegetative

Burning

Incinerator

Oil 

/Diesel 

Combustion

Crustal 

/Dirt 

/Soil

Sea 

salt General 

Coutant et 

al. 2002 

1992-1999 

(PMF) 

12.7 

(100) 

5.0 

(39.4) 

4.5 

(35.4) 

0.7 

(5.5) 

0.2 

(1.6) 

0.2 

(1.6) 

0.8 

(6.3) 

1.2 

(9.4) 

This is an IMPROVE site (39 N, 79 W, 1158 m) at a forested mountainous site in the Dolly 

Sods Wilderness Area. A highway and local paved and unpaved roads are in the vicinity. There are 

major power plants to the north and northeast within 10-15 km of the site and a quarry charcoal 

plant at 0-20 km. Results obtained at this rural site showed the dominating effects of regional 

secondary sulfate sources followed by “mobile sources” or other secondary organics. 

Table B-11. Mammoth Cave National Park, KY 

Sulfate 

/Coal Nitrate Mobile 

Biomass 

Burning Industrial 

Crustal and 

Salt Miscell. 

Reference

Data Period 

(Analysis 

Method)

Total

Mass

(ug/m3) 

(% 

Mass)

Regional 

Secondary 

Transport General

Secondary 

OC 

/General

Wood 

Smoke

Oil 

/Diesel 

Combustion

Other 

/General

Crustal

/Dirt 

/ Soil

Road 

Salt General 

Coutant et 

al. 2002 

1992-1999 

(PMF) 

16.0 

(100) 

4.9 

(30.6) 

6.1 

(38.1) 

3.0 

(18.8) 

0.7 

(4.4) 

0.1 

(0.6) 

0.8 

(5.0) 

0.3 

(1.9) 

This IMPROVE site (37 N, 86 W, 248 m) is located on the south boundary of the Mammoth 

Cave National Park, Kentucky. This is a forested and agricultural area with a major highway within 
Appendix B: Source Apportionment Methods Page B-13

10 km. Various types of agriculture surround the site. Immediately adjacent to the south are hay 

fields. Some of the farms within 10 miles of the site have small hog operations, providing some 

local sources of ammonia. There are also two commercial chicken barns near the site. Nearly all of 

the former gravel roads in the area are, at a minimum, chip-sealed with asphalt. The terrain is on a 

level ridge-top area adjacent to forested valley/ridge terrain within the park. 

This is one of the rare sites included in this summary where the regional secondary sulfate 

was not identified as the dominant source type. However (as with the Acadia results), several 

aspects of the Mammoth Cave model results appear illogical and indicate problems with the initial 

source interpretations, modeling procedures or input data. For example, the largest modeled source, 

interpreted as “mobile sources/ secondary OC”, is composed of less than 5% OC, and the 

reconstructed source mass totals only 10% of the apportioned source mass. The source’s sulfur: 

sulfate ratio – which should be 1:3 -- is an irrational 40:1. Using sulfur, rather than sulfate ion for 

defining the source composition would increase its ammonium sulfate fraction from 0.01% to 60% 

(compared to 4% OC). This source also correlates strongly with the secondary sulfate source, 

especially after 1994, where the inter-source correlation (R2

) is 0.91. All of the above suggest 

major problems with the input measurement data, modeling procedures or source interpretation. 

Table B-12. Great Smoky Mountains National Park, TN 

Sulfate/ 

Coal Nitrate Mobile 

Biomass 

Burning Industrial 

Crustal and 

Salt Miscell. 

Reference

Data 

Period 

(Analysis 

Method)

Total

Mass

(ug/m3) 

(% 

Mass)

Regional 

Secondary 

Transport General

Secondary 

OC 

/General

Vegetative

Burning Incinerator

Oil 

/Diesel 

Combustion

Crustal 

/Dirt 

/Soil

Sea 

Salt

Not 

Identified

Coutant et 

al. 2002 

1988-1999 

(PMF) 

13.4 

(100) 

4.8 

(35.8) 

6.6 

(49.3) 

0.7 

(5.2) 

0.3 

(2.2) 

0.2 

(1.5) 

0.6 

(4.5) 

0.2 

(1.5)

0.1 

(0.7) 

This IMPROVE site (36 N, 84 W, 815 m) is located in the Great Smoky Mountains National 

Park, Tennessee. The immediate surroundings of the site are forested areas. Within a ten miles 

distance there is an airport, a moderately-sized residential areas, and an aluminum plant. There are 

several local roads in the vicinity of the site. Similar to Mammoth Cave, the largest source category 

is “mobile sources/secondary organics.” As with the Mammoth Cave and Acadia results there are 

inconsistencies with the source profiles and mass apportionment, although a high “secondary 

organic” biogenic source contribution may provide a more rational interpretation. 

Table B-13. Boundary Waters canoe area, MN 

Sulfate 

/Coal Nitrate Mobile 

Biomass 

Burning Industrial Crustal and Salt Miscell. 

Reference

Data Period 

(Analysis 

Method)

Total

Mass

(ug/m3) 

(% 

Mass)

Regional 

Secondary 

Transport General

Secondary 

OC 

/General

Vegetative

Burning Incinerator

Other 

/General

Crustal 

/Dirt 

/Soil

Road 

Salt General

Coutant et 

al. 2002 

1991-1999 

(PMF) 

5.4 

(100) 

2.4 

(44.4) 

2.2 

(40.7) 

0.2 

(3.7) 

0.2 

(3.7) 

0.2 

(3.7) 

0.2 

(3.7) 

0.1 

(1.9) 

This Midwestern IMPROVE site is located at 48 N, 91 W, at 524 m elevation. The 

immediate vicinity contains forested sites with local paved and unpaved roads. There are several 

large power plants within 60 miles. Sulfate/Coal and Secondary Organics sources are of similar 

magnitude here. 
Appendix B: Source Apportionment Methods Page B-14

Table B-14. Charlotte, NC 

Sulfate/ 

Coal Nitrate Mobile 

Biomass 

Burning Industrial 

Crustal and 

Salt Miscell. 

Reference

Data 

Period 

(Analysis 

Method)

Total

Mass

(ug/m3) 

(% Mass)

Regional 

Secondary 

Transport General

Secondary 

 OC 

/General General Smelter

Oil 

/Diesel 

Combustion

Crustal 

/Dirt 

/Soil

Sea 

Salt Other

Other 

Defined

Coutant et al. 

2003 

2001-2002 

(PMF) 

16.2 

(100) 

5.7 

(35.2) 

1.2 

(7.4) 

3.9 

(24.1) 

0.7 

(4.3) 

1.9 

(11.7) 

0.6 

(3.7) 

0.1 

(0.6) 

0.5 

(3.1) 

Fireworks 

This EPA Trends site is located at 35 N, 81 W, at 230 m, on the campus of Garinger High 

School in Charlette, NC. The area surrounding the school is primarily residential, but contains some 

commercial land uses that would be associated with densely populated residential areas 

(convenience stores, restaurants, and other small businesses) near intersections along the main 

thoroughfares. The area also contains some light industrial land uses within relatively close 

proximity. This site is dominated by the secondary sulfate and mobile sources / secondary organics. 

Table B-15. Boston, MA 

Sulfate 

/Coal Nitrate Mobile 

Biomass 

Burning Industrial 

Crustal 

and Salt Miscell. 

Reference

Data Period 

(Analysis 

Method)

Total

Mass

(ug/m3) 

(% Mass)

Regional 

Secondary 

Transport General

Secondary OC 

/General General

Oil 

/Diesel 

Combustion General Other

Laden et al. 

2000 

1979-1988 16.5 

(100) 

8.3 

(50.3) 

4.8 

(29.1) 

0.5 

(3.0) 

2.9 

(17.6) 

This site was part of the Harvard Six Cities Study (Laden et al., 2000) at a central residential 

monitoring site in Watertown, Massachusetts. Sulfate and mobile sources / secondary OC account 

for the majority of fine particle mass. 

Table B-16. Potsdam and Stockton, NY 

Sulfate 

/Coal Nitrate Mobile 

Biomass 

Burning Industrial 

Crustal 

and Salt Miscell. 

Reference

Data Period 

(Analysis 

Method)

Total

Mass

(ug/m3) 

(% Mass)

Regional 

Secondary 

Transport General General

Wood 

Smoke Smelter

Crustal 

/Dirt 

/Soil

Not 

Identified

Liu et al.

2003 

2000-2001 

(PMF & PSCF) 

10.8 

(100) 

6.0 

(56.0) 

1.0 

(9.2) 

1.0 

(8.8) 

0.9 

(8.2) 

1.6 

(14.6) 

0.3 

(3.2) 

Liu et al. 

2003 

2000-2001 

(PMF & PSCF) 

18.5 

(100) 

10.4 

(56.2) 

0.4 

(2.2) 

0.3 

(1.7) 

2.8 

(15.3) 

4.3 

(23.9) 

0.1 

(0.7) 

The first site in the table above is Potsdam, located in St. Lawrence county in northern New 

York near the Canadian border. The second site is Stockton, located in Chautauqua county, about 

twelve miles south of Fredonia in New York and six miles from the eastern shore of Lake Erie. 

Daily sampling was conducted during the summers of 2000 and 2001. Compared to IMPROVE 

methods, the sampling and analysis methods employed here yielded less concentrated sample 

deposits and poorer representation of some low detection limit species. However analyses were also 

conducted for a number of specific PAH compounds, providing additional detail to the input data. 

On average secondary sulfate sources contribute over 50% to the summertime mass and the 

authors identify the Ohio River Valley as the primary source region for both sites. Soil source 
Appendix B: Source Apportionment Methods Page B-15

regions include the agricultural areas from Ohio to Illinois. Wood smoke was mainly from the area 

surrounding the Great Lakes, particularly the upper Great Lakes. A copper source (indicative of 

industrial/smelter) in central Ohio was important at Potsdam, while at Stockton, the smelter source 

area was further east in Ontario trailing into western Quebec. For Potsdam, the main potential 

source areas for the zinc smelter were in eastern Quebec. For Stockton, the main potential source 

areas were along the US-Canada border from central Ontario to southern Manitoba. It appears to be 

influenced by smelters from outside the domain of trajectories such as the facilities at Flin Flon and

Thompson, Manitoba. A nickel smelter source was also obtained at Potsdam. The results showed 

this source most likely included the smelters at Noranda, Quebec and Falconbridge, Ontario. 

Table B-17. Toronto, Canada 

Sulfate 

/Coal Nitrate Mobile 

Biomass 

Burning Industrial 

Crustal 

and Salt Miscell. 

Reference

Data Period 

(Analysis 

Method)

Total

Mass

(ug/m3) 

(% Mass)

Coal Related 

+ Organic 

Acids 

Ammonium 

Nitrate

MV + 

Road Dust 

+ 

Road Salt

Wood 

Smoke Smelter

Crustal 

/Dirt 

/Soil

Not 

Identified

Lee et al. 

2003 

2000-2001 

(PMF) 14.0 5.8 5.1 2.5 0.5 

Poirot & 

Brook 2004 

2000-2001 

(UNMIX) 

14.0 

(100) 

4.7 

(33.6) 

4.8 

(34.3) 

3.1 

(22.1) 

0.6 

(4.3) 

Poirot & 

Brook 2004 

2000-2001 

(Average PMF& 

UNMIX) 

14.0 

(100) 

5.3 

(37.9) 

4.9 

(35.0) 

2.8 

(20.0) 

0.3 

(2.1) 

Poirot 2004 

pers. com. 

2000-2001 

(Average PMF& 

UNMIX) 

%Bext 

Best 20% (30.6) (17.4) (47.2) 4.8 

Poirot 2004 

pers.com. 

2000-2001 

(Average PMF& 

UNMIX) 

%Bext 

Worst 

20% 

(43.3) (44.1) (10.5) 2.0 

This is a Canadian site located at the University of Toronto in Canada’s largest urban area 

and just off the Northwest corner of the MANE-VU region. The measurement data were similar to 

those collected in routine IMPROVE and STN networks, but include more detailed measurements 

on ammonium and several organic anions (oxalate, succinate, etc.). These additional species 

allowed for more detailed source resolution than for many of the other sites summarized above. 

PMF and UNMIX modeling were initially conducted independently by the Canadian and US groups 

respectively, followed by comparison and reanalysis or re-interpretation of the resulting sources. 

The model results were very similar, with the exception that the PMF results divided a total Motor 

Vehicle influence into 2 components (MV exhaust + road dust, and MV exhaust + road salt), while 

the UNMIX results broke a similar total MV influence into Diesel MV, Gasoline MV and Road 

Dust. Both models also identified 2 “secondary sulfate” sources: one fully neutralized (NH4)2SO4

and one acidic (approximately NH4HSO4) which also included a large fraction of several organic 

acids and other secondary organic matter (consistent with the “acid-catalyzed SOA formation 

mechanism” identified in chamber studies by Jang et al. (2002). A large “secondary nitrate” source, 

composed of nearly pure NH4NO3 was identified and of similar mass magnitude to the combined 

sulfate + SOA sources. 

The visibility impacts of the sources were also estimated by applying IMPROVE formulae 

to the individual source species and adding hygroscopic growth functions for the (NH4)2SO4, 

NH4HSO4 and NH4NO3 fractions, using nearby Toronto airport (hourly) RH data. Local mobile 

sources were dominant on the cleanest days, and the sulfate and nitrate-related sources were 

dominant on the haziest days. 
Appendix B: Source Apportionment Methods Page B-16

Table B-18. MANE-VU Region Multi-Site UNMIX (July and August, 2002) 

Reference 

Data Period 

(Analysis Method) 

Total

Mass

(ug/m3) 

(% Mass)

Regional 

Sulfates 

Wood 

Smoke 

Mixed 

 Urban 

Airborne 

Dust 

Poirot & Husar, 

2004 

July, August 2002 UNMIX 

on 17 IMPROVE sites 

14.0 

(100% 

7.4 

(53%) 

4.1 

(29.4% 

1.5 

(10.9%) 

0.9 

(6.6%) 

Poirot & Husar, 

2004 

July, August 2002 UNMIX 

on 43 STN sites 

24.6 

(100%) 

10.1 

(41%) 

8.0 

(32.4%) 

4.1 

(16.7%) 

2.4 

(9.9%) 

This analysis employed data from all IMPROVE sites and all STN sites in the MANE-VU 

region for the months of July and August, 2002, in 2 separate UNMIX model runs, one for each 

network. The input data were constrained to include only major mass-contributing species, selected 

crustal elements, and trace elements (K and Sr). The objective was to develop a chemical 

fingerprint for the large Quebec forest fire impacts centered on July 2002, while also accounting for 

other regional source influences. This required regionally consistent source compositions (the same 

for all sites in each network), but also yielded variable source contributions for each site and sample 

date. Results were surprisingly consistent across the MANE-VU sites in the two different networks, 

and included a large regional sulfate source, a wood smoke source, a (primarily local) “mixed 

urban” source and a crustal source. The 7/7/02 Quebec fire impacts resulted in the highest 1-day 

fine mass and light extinction ever recorded across the MANE-VU region, and one of the largest 

Sahara Dust events ever recorded in the Northeast was detected on the preceding sample day. 

Averaged over the 2-month summer period, however, the regional sulfate source predominated, but 

both the wood smoke and crustal sources were proportionately higher than in more “typical” 

summers. Modeled impacts for the IMPROVE sites are displayed graphically in Figure B-2. 

Figure B-2. Modeled Regional Source Impacts for MANE-VU IMPROVE sites, Summer 2002
Appendix B: Source Apportionment Methods Page B-17

B.1.4. Estimated Source Visibility Contributions from the Battelle Study 

While many of the above studies had a primary objective of apportioning fine mass to 

source types at selected urban or rural locations, the Battelle analysis (Coutant, 2002) also had the 

specific objective of estimating source impacts on visibility on best and worst 20% days. This 

apportionment was estimated by regressing the normalized daily source contributions vs. 

reconstructed extinction (calculated by IMPROVE formulae), stratified by month (for application of 

monthly mean f(RH) factors). 

Figure B-3. Estimated Visibility Impacts from Battelle PMF sources on 

IMPROVE sites on Best 20% (top) and Worst 20% (bottom) visibility days 

(adapted from Coutant, 2002). 
Appendix B: Source Apportionment Methods Page B-18

Results of this analysis, with sources grouped into 6 summary categories, are presented 

graphically in Figure B-3. On the best visibility days, regional sulfate sources account for about 30

to 40% of the light extinction, and are usually the largest contributing category (with the exception 

of the urban Washington DC site, where motor vehicle and secondary OC sources predominate. On 

the worst visibility days, regional sulfate / coal related sources account for 60 to 80% of the light 

extinction, with the exception of the most westerly Boundary Waters MN site, which is much less 

frequently downwind of large SO2-emitting sources than are the MANE-VU and VISTAS regions. 

B.2. Ensemble Trajectory Evaluation of Multivariate Model Results 

B.2.1. Background 

Trajectory models employ meteorological data to calculate paths of airmass motion over 

space and time. Commonly employed models include NOAA HY-SPLIT, CAPITA Monte Carlo, 

and ATAD, which can in turn be driven by gridded wind-field data such as from the ETA, FNL, or 

MM5 meteorological models, or for the older ATAD model, driven by rawinsonde measurement 

data. Run in a backward direction from a given starting location and time, they can be considered as 

a form of “meteorological receptor model” and are commonly employed to evaluate potential 

origins and transport routes of pollution events observed at ambient monitoring sites. 

Compared to more sophisticated photochemical grid models, computational requirements 

for trajectory calculations are modest, facilitating the generation of long-term (multi-year) trajectory 

databases. Trajectory databases developed for ambient monitoring sites can then be employed in 

various “ensemble trajectory techniques” to evaluate common origins and transport of long-term 

monitoring data, such as the speciated aerosol data collected for many years at IMPROVE sites, or 

to aid in interpretation and validation of results of multivariate mathematical results generated from

these speciated measurement data. Examples of commonly employed ensemble trajectory 

techniques include: Cluster Analysis, Quantitative Transport Bias Analysis, Residence Time 

Analysis, and Potential Source Contribution Function. Generally, the above ensemble techniques 

(and their many variations) involve sorting (screening and/or aggregating) trajectories as a function 

of resultant receptor concentrations, or sorting receptor concentrations as a function of prior 

trajectory locations. 

The “Combined Aerosol Trajectory Tools” (CATT) is a set of web-based analytical tools 

that facilitate the application of many various ensemble trajectory techniques for evaluation of 

speciated aerosol data, multivariate model results derived from these data, or anything else that’s 

been measured at or modeled for ambient monitoring sites in the IMPROVE or EPA Speciation 

Trends Network. CATT was initially conceived as a tool for trajectory evaluation of the 

multivariate mathematical model results (PMF & UNMIX) from the Battelle “Phase I” analysis of 

IMPROVE and CASTNet data1 from 16 eastern US sites. An initial set of CATT tools was 

developed in 2003 by the CAPITA group at Washington University, with funding support from the 

MANE-VU and Midwest RPOs. The CATT tools have since been considerably enhanced over the 

past year, with additional funding from EPA and the other RPOs, and cooperative technical support 

from CIRA and NPS. In its current configuration, CATT allows combined aerosol/trajectory 

analyses of all speciated aerosol data from the IMPROVE and EPA STN networks for their 

respective periods of record. The aerosol data access is directly linked to VIEWS2

 and the 

trajectories were provided by NPS (Kristi Gebhart) using the ATAD model. The ATAD trajectories 

 

1

http://www.marama.org/visibility/SA_report 

2

http://vista.cira.colostate.edu/views 
Appendix B: Source Apportionment Methods Page B-19

were calculated with 5-day backward duration for 4 start-times/day. The many varied CATT 

analysis and display options are currently evolving, but are generally described by Husar et al, 

20053 and in the more recent CATT Illustrated Instruction Manual4. The following section presents 

example applications of several of the CATT tools for evaluation of selected aerosol species data as 

well as some of the multivariate mathematical model results (summarized in Section B.1 above) 

derived from those data. 

B.2.2. Illustrations of Multivariate Model Source Impacts in MANE-VU Region 

 Using trajectory analyses of the results of source apportionment studies can identify upwind 

regions on days when specific source factors predominate. CATT is a tool designed to facilitate 

these analyses. One of the basic CATT tools allows plotting of Multi-Site, Single Day Trajectories5

& Aerosol Species Concentrations for all IMPROVE and STN sites, where the aerosol species data 

symbols can be sized in proportion to concentration, and the trajectories colored (from blue – low to 

red – high) to emphasize the upwind locations associated with low and high concentrations of the 

selected species. The Gao et al. (2004) PMF modeling of 2001-2003 IMPROVE data from 

Underhill, VT and the Poirot & Husar (2004) multi-site UNMIX modeling of July and August 2002 

data from all MANE-VU IMPROVE and STN sites identified 3 regional source influences which 

were interpreted similarly by the two modeling groups, and which showed similar species 

compositions (for common species), and similar time series and absolute magnitudes (for common 

dates). These common sources (and their dates of maximum influence in both model runs) included 

“windblown dust” (7/4/02), “wood smoke” (7/7/02) and “secondary sulfates” (8/12/02). The 7/4/02 

dust and 7/7/02 wood smoke have also been identified as resulting from unique large natural 

emissions from Sahara dust storms and Quebec forest fires respectively, and so represent locational 

and chemical “tracers of opportunity.” Figure B-4 displays the multi-site trajectories for all 

(Eastern US) IMPROVE and STN sites, with color-weighting to reflect highest concentrations of Al 

(7/4/02), OC (7/7/02) and SO4 (8/12/02) respectively. It may be noted that the 7/4/02 soil event is 

also clearly evident in the Ito et al. (2004) APCA and PMF results for several NY City sites. Thus 

the results from multiple receptor models, modeling groups and MANE-VU receptor sites are 

consistent with each other, with the back trajectory calculations and with clearly identified “tracers

of opportunity.” 

B.2.2.1. Dust 

The 7/4/02 “Sahara Dust” event illustrated by highest Aluminum concentrations in Figure 

B-4 resulted in some of the highest “fine soil” (by IMPROVE formula) concentrations recorded to 

date at MANE-VU IMPROVE sites. However, soil concentrations are higher in, and trajectories 

come from, the West and Southwest of MANE-VU – in apparent conflict with the interpretation of 

a Sahara dust origin far to the Southeast. While visibility and fine mass concentrations in the 

MANE-VU region are typically less influenced by soil dust than any other RPO region, the highest 

MANE-VU dust concentrations are typically associated with Sahara dust events, and their transport 

route into the Northeast typically comes from a southwesterly direction. The 5-day ATAD 

trajectories don’t fully illustrate the much longer transport route for the two reasons that their 

duration is too short and they exceed the southern and eastern limits of the meteorological domain. 

A better sense of the full transport route for this event is indicated by the progression of high soil

 

3

http://www.anr.state.vt.us/air/Planning/PublicDocs/AWMA97CATT.pdf 

4

http://capita.wustl.edu/capita/capitareports/0411CATTReport/Tutorial/CATT_Manual.htm 

5

http://webapps.datafed.net/dvoy_services/datafed.aspx?page=CATT/CATT_RS
Appendix B: Source Apportionment Methods Page B-20

concentrations over the preceding week and longer trajectory duration (Figure B-5) combined with 

concurrent NAAPS6 model calculations of global dust emissions, transport and effects on aerosol 

optical depth. 

 

6

http://www.nrlmry.navy.mil/aerosol/Docs/globaer_model.html 

Figure B-4. Multi-Site, Single Day Trajectories & Species Concentrations for all visible 

IMPROVE and STN Sites for Wood Smoke, Soil Dust and Sulfate Events 

Source: Gao et al. (2004) PMF of PMRF, VT IMPROVE data and Poirot & Husar (2004) Multisite UNMIX for all 

MANE-VU IMPROVE and STN Sites. 

Figure B-5. From Poirot & Husar (2004) Figure B-6. From NRL Monterrey NAPS model 
Appendix B: Source Apportionment Methods Page B-21

A number of the other mathematical model results summarized in Section A.2.1 also 

identify “crustal dust” sources at Northeastern sites, and observe that the highest modeled dust 

contributions in the MANE-VU region are most often associated with Sahara Dust events, with 

initial easterly transport into the Caribbean, followed by northwesterly transport from the Gulf 

Coast across the central US into the Northeast. For example, Kim and Hopke (2003) 

identify an “airborne soil” source in PMF modeling of 1988-1997 Washington, DC IMPROVE data, 

for which the highest daily concentration occurred on 7/7/93 with trajectories similar to those in 

Figure B-5, during a Sahara Dust event previously documented by Perry et al. (1997). The same 

date also saw the highest “crustal” source contribution in the Battelle Phase 1 PMF and UNMIX 

modeling of 1988-2000 IMPROVE data at Acadia, ME and was also identified as the highest 

“soil” source day in PMF and UNMIX analyses of 1988-1995 IMPROVE data at Underhill, VT by 

Polissar et al. (2001) and Poirot et al. (2001) 

CATT trajectories for eastern IMPROVE 

sites, color weighted by (IMPROVE formula) 

“soil” concentration are displayed in Figure B-7 

for this Summer 1993 event, which saw initial 

impacts on 6/26/93 at the Virgin Islands and 

Everglades sites, followed by impacts along the 

TX/LA coast, before progressing toward New 

England over the following week. At that time, 

7/7/93 saw the highest soil levels recorded to 

date at: Gt Smokey Mtns., TN, Shenandoah, VA, 

Washington, DC, Dolly Sods, WV, Ringwood, 

NJ, Mohawk Mtn., CT, Whiteface Mtn., NY, 

Burlington, VT, Mt. Sunapee, NH, Acadia, ME. 

Routine Canadian sampling (on 7/6/93) also 

noted the highest Si levels to date at: Winsor, 

Egbert, Toronto, Ottawa, Montreal, Sutton and 

Quebec City. In addition to these fairly routine 

(nearly every summer) spikes of Sahara dust, 

occasional Asian dust events are also observed in 

the MANE-VU region. For example, the well 

documented continental-scale impacts of the 

Spring 2001 Asian Dust event are observed in the modeled soil results of Kim and Hopke (2004) at 

Brigantine, NJ and Gao et al (2004) at Underhill, VT. 

A clear distinction should be drawn between fine “soil” as calculated by the IMPROVE 

formula (2.2[Al]+2.49[Si]+1.63[Ca] +2.42[Fe]+1.94[Ti] ) and “soil, dust or crustal” sources 

identified by receptor models. The IMPROVE formula represents an idealized form of “pure” 

crustal material, composed of the most prevalent oxides of the elements used in the equation (all of 

which are assumed to result predominantly from crustal sources) plus additional minor adjustments 

to account for oxides of K, Mg, Na and water and carbonate (which also have non-soil sources). 

Receptor modeled soil sources typically include most of the above “crustal elements”, usually in 

similar relative proportions to those generated by the IMPROVE formula, and may include several 

crustal sources at locations influenced by nearby calcarious soils, as well as by more distant sources

like Sahara dust, which tends to be relatively depleted in Ca and enriched in Al. The above 

“crustal” elements are also often partially distributed among other modeled sources, as they are 

Figure B-7. High Soil color-weighted ATADs 

for Eastern US IMPROVE sites during 

Summer 1993 Sahara Dust Event. 
Appendix B: Source Apportionment Methods Page B-22

emitted by various combustion and industrial processes, kicked up by motor vehicle traffic, etc. In 

addition, modeled soil sources are frequently “aged” or “contaminated” by other pollutants like 

SO4, NO3 and/or OC, all of which can react with or “coat” fine soil particles during transport, or 

which may be present in the soil emission source, as might be anticipated for reentrained road dust, 

enriched by deposited motor vehicle exhaust, oil leaks, etc. Consequently the modeled soil mass 

contributions are typically higher than the IMPROVE soil calculations (by about a factor of 2), as 

they represent “aged” soil and the various natural and anthropogenic contaminants which have been 

added during transport. In the Northeastern US, sulfate often accounts for up to half of the mass of 

these soil modeled soil sources, which is not surprising considering the lengthy transport routes. 

However, even with this “anthropogenic enhancement” fine soil typically accounts for < 5% of 

PM2.5 mass, and an even smaller fraction of extinction on both best and worst visibility days in the 

Northeast. 

B.2.2.2. Salt 

Many of the above receptor model studies identify a small mass-contributing, but significant 

“sea salt” source. This source influence is especially evident at the MANE-VU coastal Class I sites 

– Acadia and Brigantine, but was also detected at “near coastal” urban sites (Washington DC, New 

York City) and further inland at Shenandoah VA, Dolly Sods WV and Charlotte, NC. Like the 

crustal sources, sea salt can be a useful “tracer of opportunity,” as its origin should be unambiguous

(from the sea). Figure B-8 illustrates applications of local surface meteorology and ensemble 

Figure B-8. Meteorological Evaluation of UNMIX Sea Salt Source at Brigantine, NJ. 

Note : Top-right: Incremental probability, bottom-left: Upwind average 
Appendix B: Source Apportionment Methods Page B-23

trajectory methods to identify the most common origins of the Brigantine sea salt source from 

UNMIX modeling by Poirot and Wishinski (2002). This modeled source, and other sources from 

that UNMIX application, were also found to be quite similar to the Battelle UNMIX and PMF 

sources, as well as the PMF sources reported by Song et al. (2001), Lee et al. (2003) and Kim et al. 

(2004). See Appendix T in Coutant et al. (2002) for a more detailed comparison. 

The wind rose is based on local surface wind data, constrained to days when the modeled 

salt source was high (top 10%). The radial plot is also based on local wind data but shows averages 

for each directional sector, expressed as % deviations from the mean. The ATAD trajectory based 

“incremental probability” plot (upper right) shows locations where the trajectories were more 

frequent on high salt days (top 10%) than on an everyday basis. The “upwind is average” plot 

(lower right) shows average sea salt at Brigantine as a function of upwind location. Differences 

between the two trajectory techniques reflect differences in the questions they pose. Incremental 

probability asks: “if the source contribution was high, where did the air previously reside?” The 

upwind average asks: “if the air was previously over this location, how high was the source 

contribution at the downwind receptor?” Incremental probability is influenced by the frequency of 

contribution, while the upwind average emphasizes areas with the greatest potential to contribute. In 

the above plot, the very high upwind average off the coast of the Carolinas coincides with the area 

where the Gulf Stream comes closest to the east coast and where sea salt emissions might be 

anticipated to be highest. These analyses were conducted prior to development of the CATT tool, 

but can now be reproduced in CATT for these or any other modeled sources or any of the raw 

measured species data at Brigantine or any other IMPROVE site. 

Like the crustal sources, “sea salt” sources identified by receptor models often reflect an 

“aged” composition. Over time, chlorine is lost from airborne NaCl and replaced with “excess” 

NO3 and SO4. The Brigantine PMF results from Lee et al. (2003) illustrate this most clearly, as the 

modelers identified 2 sea salt sources identified as “fresh” and “aged” respectively. Their fresh sea

salt contained similar fractions of Na and Cl (on a molar basis) and very little of anything else, 

while their aged sea salt source contained S, NO3 and Na but no Cl. The aged source contributed 

substantially more mass, averaging 8 times higher than the fresh source. Results from all the other 

Brigantine receptor modeling analyses found a single sea salt source with relatively aged 

composition. 

In all of the Brigantine (and Acadia) modeling studies, the sea salt source accounted for a 

large fraction of the total sodium at the receptor – typically 90% or more. Thus sodium alone 

represents a good “tracer” for sea salt, at least at coastal sites, which are in turn most likely to be 

impacted by marine sources. Figure B-9 illustrates another ensemble trajectory metric created using 

the CATT tool, in which the entire IMPROVE database – all sites and sample dates – was queried 

for sites and dates with “high” sodium (> 1.5 ug/m3). CATT then returns the associated trajectories 

and highlights the site locations where this condition was met. It can be noted in the top half of 

Figure B-9 that these tend to be coastal or near-coastal sites. For the bottom half of Figure B-9, the 

“messy” individual trajectories, which for such a large query tend to pile up on top of each other, 

were further processed by a gridder, which counts and displays the sums of all trajectory endpoints 

within prespecified grid cells (in this case 1x1 degree of latitude and longitude). Clearly Na is an 

excellent tracer for sea salt, which comes in turn from marine locations off the east and west coasts. 

The use of multiple receptor locations and dates tends to correctly identify the “known” source 

region(s) by “triangulation,” even though that region lies outside the domain (continental US) of the 

receptor sites. An important related point is that since sea salt comes from the sea, it tends to occur 

at highest concentrations when anthropogenic pollutants (from within the US) are lowest. Thus its 
Appendix B: Source Apportionment Methods Page B-24

influence on visibility will tend to be greatest on days with lowest “reconstructed extinction,” 

especially since a sea salt component is not currently included in the regional haze equation. It may 

be noted from Table B-5 in Section B.1.3, that the Poirot & Wishinski (2002) sea salt source 

averaged 20% of the fine mass at Brigantine on the 20% best visibility days. In addition, sea salt is

hygroscopic, and should also include additional mass in the coarse mode. So its fractional 

contribution to visibility impairment on clean days at coastal sites is likely to be much greater than

its fractional contribution to fine mass. 

Figure B-9. CATT trajectories for all IMPROVE sites and dates with 

Na > 1.5 ug/m3

 (top) and gridded, aggregated trajectory endpoint counts for these “High Na” 

sites and dates (bottom) 
Appendix B: Source Apportionment Methods Page B-25

B.2.2.3. Smoke 

A “wood smoke” or “biomass combustion” source is identified at most of the rural sites for 

which model results are available, but is often not detected or of very small magnitude in larger 

urban areas – for example none of the results for NYC, Toronto and Boston and none in 3 of 4 

studies in Washington DC. This makes sense considering the difficulties of storing large piles of 

fuel wood in densely populated urban centers and/or municipal bans on woodstoves and fireplaces. 

Due to its relatively abundant rainfall and vegetation types the MANE-VU region is not especially 

prone to forest fires, compared to other regions further west or south. A notable exception to these 

generalizations occurred during the 7/7/02 Quebec fires which affected MANE-VU urban and rural 

sites alike, and which also resulted in the largest 1-day regional fine mass concentrations and 

visibility impacts recorded in recent years. 

Current receptor models (with routine speciation data) are not able to distinguish between 

wood smoke due to residential wood burning and forest fires. The PMF and UNMIX results from 

Polissar et al (2001) and Poirot et al. (2001) both identified similar wood smoke sources at 

Underhill, VT, based on 1989-1995 measurement data, and both also noted a temporal pattern of 

highest average concentrations (wood stoves) combined with occasional summer spikes (forest 

fires, several of which were located in roughly the same area as the 2002 fires). The area just east 

of James Bay Quebec is a relatively chronic summer forest fire area. Figure B-10 shows the 

incremental probability for the above-mentioned Underhill wood smoke source in comparison with 

the 7/02 fires and historical pattern of large Canadian fires. 

It may be noted that the incremental probability field includes the local region around the 

receptor site, as might be expected for residential wood burning, but also includes a distinct tail to

the north and east, consistent with occasional Canadian wild fire influences. 

Figure B-10. Incremental probability for 1989-1995 Underhill VT wood smoke 

compared with Canadian forest fire locations 
Appendix B: Source Apportionment Methods Page B-26

Gao et al. (2004) also identified a wood smoke source in Underhill, VT, based on 2001-2003 

data. It was similar in magnitude to the 1989-95 Underhill wood smoke sources (averaging 1.1 

ug/m3), but contributed a higher mass fraction (20% vs. 16%) due to reductions in the magnitude of 

other source influences, especially sulfates. This source also made a large proportionate 

contribution (25%) to visibility impairment on the best 20% visibility days but dropped to 7% on 

the worst visibility days. This might be expected from Canadian fire influences, as air from that 

direction is typically otherwise very clean. It should also be noted that Gao et al. (2004) 

intentionally excluded the 7/7/02 sample day from their model input, as the source was clearly 

known and the huge 1-day impact tended to produce illogical PMF results. However, the chemical 

composition of Gao's PMF smoke source was very similar to the composition of the Quebec fire 

smoke identified by the multi-site UNMIX analysis in Poirot and Husar (2004). 

The incremental probability 

field for the wood smoke source at 

Lye Brook VT, based on the 1991-99 

PMF results in Coutant et al., (2002) 

is shown in Figure B-11. As was the 

case in Underhill, Lye Brook shows 

influence from both local sources as 

well as more distant Canadian fires. 

This Lye Brook smoke source 

contributed a relatively high fraction 

(25%) of the total fine mass, but was 

not found to be especially important 

there on either the best or worst 20% 

visibility days. 

A relatively smaller wood 

smoke source influence was also 

noted in several of the Brigantine 

model results (though not in all 

studies of Brigantine). Figure B-12 shows the incremental probability field and the upwind average 

field for wood smoke source contributions from the Poirot and Wishinski (2002) UNMIX model 

results. It also displays the “everyday probability field for Brigantine (where the air most frequently 

Figure B-11. Incremental probability for Lye Brook wood 

smoke source (PMF modeling by Coutant et. Al. (2002) 

Figure B-12. Everyday probability, incremental probability and upwind average for UNMIXmodeled wood smoke source at Brigantine, NJ (from Poirot & Wishinski, 2002) 
Appendix B: Source Apportionment Methods Page B-27

resides on all sample days). Unlike the more northern VT sites, there is not strong indication of 

Canadian fire influence, although that would change if the 7/02 Quebec fire was considered, since 

Brigantine had the highest impacts of any IMPROVE site for that event, exceeding 100 ug/m3. 

Also unlike the VT results, there’s no indication of strong local wood burning source influences, 

and a much different regional origin – in the Southeastern US – is indicated. 

Figure B-13 displays 

incremental probabilities for 

PMF modeled wood smoke 

sources at selected Eastern US 

sites from the Coutant et al. 

(2002) analysis. These were 

done on an earlier version of the 

CATT tool which employed 

HYSPLIT trajectories, rather 

than ATADs. The gridded 

results were exported from 

CATT and imported to ArcView 

GIS, where additional 

interpolation and contouring was 

conducted. 

Acadia, like Underhill 

and Lye Brook VT shows highest probabilities for both local New England region and more distant 

wild fires in eastern Quebec. Shenandoah also shows relatively local influence, as well as more 

distant effects from an area of the Southeast, similar to that identified for Brigantine. The other 3

sites show influence from regions more offset from the receptors. This may reflect differences in 

regional forest fire locations, secondary aerosol formation (which requires time & distance), and/or 

high wind speeds often associated with the spread of large fires. 

Figure B-14 is based on a similar approach to that 

described for Figure B-13, except in this case, the results are 

aggregated for the New England, Mid Atlantic and Southeast 

regions. 

It should be cautioned that modeling and interpreting 

sources of primary and secondary carbonaceous sources can be 

problematic, and our understanding of these sources is currently 

evolving rapidly. The general grouping of sources into a 

category of “Mobile and Secondary Organics” – based on the 

current knowledge in the “dark ages” of 2002 - may be 

misleading in the sense that in some cases, especially at rural 

sites there may well be large secondary organic influences that 

are predominantly biogenic and unrelated to motor vehicle 

emissions. There may be misinterpretation or mixing of source 

influences from forest fires and biogenic emissions of gaseous 

organic precursors in the absence of fires. In the northern half 

of the MANE-VU region, local and Canadian wood smoke 

identification is relatively clear, but at southern MANE-VU and 

Figure B-13. Incremental probabilities for modeled wood smoke 

sources at selected sites (PMF sources from Coutant et al., 2002) 

Figure B-14. Woodsmoke 

Source Regional Aggregations 
Appendix B: Source Apportionment Methods Page B-28

more southeastern sites, the distinction between biogenic fire and nonfire emissions is less clear. 

Figure B-15 is adapted from an illustration presented by Husar 

(2005) in the CATT Illustrated Instruction Manual. Its based on a 

variation of the “upwind average” trajectory metric, also referred to in 

CATT parlance as the “Donna Kenski Method,” following her GIS 

development and expansion of the method to multiple sites as described 

in Kenski (2004). Husar (2005) used OC data for the 2001-2003 period 

for a large subset of 35 widely distributed IMPROVE sites (top of Figure 

B-15), and then calculated the “Kenski metric” using only values greater 

than high (98%) and lower (95%, 90%, 80%) percentiles of the data. He 

also showed the more traditional “upwind average” using all the OC data 

from these sites (Figure B-16). At the highest percentiles, locations of 

well documented large forest fires are evident. But such fires are very 

episodic in their emission patterns and are not likely to repeat in the 

same locations within a relatively short multi-year time period. At the 

80th percentile (highest 20% of OC measurements at each site), and for 

the average based on all OC data, the large fire effects are much less 

evident, and the Southeastern US shows up as a much more chronic OC 

source area. Although there are fires in the Southeast, they don’t burn 

continuously, and a gaseous biogenic emissions source category is 

perhaps a more logical interpretation for the “source.” This upwind 

average for all OC data from CATT is quite similar to the pattern 

displayed in Kenski, 2004 – who also noted a similar pattern for CAMx 

modeled biogenic secondary organic aerosol as displayed in Figure B-17. 

Figure B-15. Multi-site upwind 

average for high percentile OC 

Figure B-16. Multisite Upwind Average OC (from Husar, 2005). 
Appendix B: Source Apportionment Methods Page B-29

It may be noted that the Kenski (2004) analysis is based on 3-day backward HYSPLIT 

trajectories for 17 eastern IMPROVE sites 1997-2001, while the Husar analysis is based on 5-day 

ATAD trajectories for 35 nationally distributed sites, 2001-2003.

B.2.2.4. Nitrates 

The Battelle receptor modeling (Coutant et al., 2002) included both PMF and UNMIX 

analyses. The PMF results, constrained to 9-source solutions at all sites, generally appeared more 

reasonable than the UNMIX results (limited to 6-source solutions). But many of the UNMIX 

sources were quite similar to their PMF counterparts, and nitrate data were included as input to the 

UNMIX model runs, and therefore apportioned among the resulting sources (nitrate data were 

omitted from the Batelle PMF analyses). A generally common feature of the Battelle UNMIX 

results for nitrate was that it tended to mostly break out into a single “nitrate source” at most 

modeled sites, and this “source,” assumed to be mostly ammonium nitrate, accounted for a large 

fraction (70 to 90%) of the total measured nitrate, with relatively small fractions of other major 

mass-contributing species or tracer elements. This characteristic of a unique nitrate source, not 

specifically associated with any emission source category (such as motor vehicles, utilities or 

industrial sources) was also observed in a number of the other modeling studies - for example in 

Underhill, Potsdam, Brigantine, New York and Toronto. Fractional mass contributions from these 

nitrate sources are in the range of 5 to 10% at MANE-VU rural sites, but can be substantially higher 

in urban areas. In Toronto, an “ammonium nitrate source” identified by Lee et al. (2003) PMF 

modeling and Poirot and Brook (UNMIX) contributed 35% of the fine mass and accounted for over 

Figure B-17. Average upwind OC for Eastern IMPROVE sites vs. CAMx model 

results (Kenski, 2004). 
Appendix B: Source Apportionment Methods Page B-30

40% of the visibility impairment on worst 20% days. Trajectory analyses indicated influence of 

both agricultural emissions from the “corn belt” region, as well as local urban influences. 

Incremental probability plots for the Battelle UNMIX nitrate sources are displayed in Figure 

B-18, and suggest a strong influence from Midwestern agricultural emissions at all rural 

northeastern sites. Note that Washington D.C., the only urban site, shows a markedly different 

pattern of influence suggestion more local contribution to nitrate at that site. 

A similar “corn belt” regional influence on average upwind nitrate at 17 eastern IMPROVE 

sites is also clearly indicated in the Kenski (2004) analysis seen in Figure B-19, particularly for the 

winter season, when concentrations are highest. During summer, the lower Mississippi valley and 

east coast urban corridor appear more important. Potentially there is some formation of sodium 

nitrate in coastal areas and/or competition from acidic sulfates in the Midwest and Southeast during 

Figure B-18. Incremental probability fields for high nitrate at selected northeastern sites. 

Figure B-19. Average upwind Nitrate (seasonal) at eastern IMPROVE sites (Kenski, 2004) 
Appendix B: Source Apportionment Methods Page B-31

summer. 

B.2.2.5. Oil 

Many of the referenced modeling studies identify a source identified as “oil combustion” 

which typically accounts for a high fraction of Ni and/or V at the receptor sites. These trace 

elements are present in crude oil and tend to become highly concentrated in the “residual” during 

the refining process. Consequently, “residual oil burning” (rather than distillate oil burning) is the 

most likely source of these trace (and “tracer”) elements. It may also be noted that an oil source is

most frequently identified and /or tends to have highest mass concentrations at sites in or near the 

northeast urban corridor, with the 

highest reported average source 

contribution of 4.2 ug/m3 at one 

of the NYC sites modeled by Ito 

et al. (2004). 

Figure B-20 displays an 

everyday wind rose based on 

local surface meteorology data 

for Brigantine, NJ - along with a 

similar rose constrained to days 

when the UNMIX-modeled Oil 

source from Poirot and Wishinski 

(2002) was highest. Also shown 

are ATAD trajectory-based 

probability fields for every day 

and for days when the modeled 

oil source was high. As indicated 

in Coutant et al. (2002, Appendix 

T) very similar oil sources were 

also identified at Brigantine in 

independent PMF modeling runs 

by the Battelle, Rutgers and 

Clarkson modeling groups 

respectively. 

In Figure B-21, the 

incremental probability for the 

Brigantine oil source is compared 

with similar oil sources identified 

by Polissar et al. (2001, PMF) 

and Poirot et al. (2001, UNMIX) 

at Underhill, VT; and by Coutant 

et al (2002, PMF) at Lye Brook, 

VT and Washington DC. The 

multiple results show a clear 

convergence on the Northeast 

urban corridor, where the density 

Figure B-20. Meteorological evaluation of Brigantine Oil source. 

Figure B-21. Incremental Probability for Oil sources at 4 NE sites. 
Appendix B: Source Apportionment Methods Page B-32

of residual oil combustion sources is highest. 

B.2.2.6. Coal 

Nearly all of the modeling studies summarized in Section B.1 identify a large “secondary 

sulfate” or “coal” source which typically accounts for 30 to 60% of the fine mass and 60 to 80% of 

the visibility impairment on the haziest days in the Northeast. A distinction should be drawn 

between “sulfate the source” (which is not composed entirely of sulfate) and “sulfate the species” 

(which is not associated entirely with the source). As indicated in previous discussions of aged 

windblown dust and sea salt, both of these “natural sources” are also often “contaminated" with 

anthropogenic sulfate, nitrate or OC. These aerosols of mixed origins raise interesting questions of 

“causality” (and resultant light extinction characteristics), but typically account for rather small 

fractions of the contaminant species. Oil burning, industrial sources and motor vehicles also 

contribute significant amounts of sulfate, and their mass compositions often contain high fractions 

of sulfate. However, the large, “secondary sulfate” or “secondary coal” sources identified at most 

sites in most of these receptor modeling studies are typically composed primarily of sulfates and 

account for high fractions of the total sulfate at the receptors. 

Sulfate characteristics of the large sulfate sources identified in the Battelle modeling at 

eastern IMPROVE sites are summarized in Figure B-22, which shows the percentages of sulfate at 

the site contributed by the source and the percentages of the sources composed of sulfates. 

Seventy to ninety percent of the sulfate at these sites is contributed by these large sources, 

which are in turn composed of fifty to ninety percent sulfate. The regional haze reconstructed 

extinction equations assume that sulfate is always present as ammonium sulfate, which would have 

a sulfate to mass ratio of 0.73 (for NH4HSO4 and H2SO4, the sulfate mass ratios would be 0.83 and 

0.98 respectively). At some of the southern sites - like Mammoth Cave, Shenandoah and Gt. 

Smokey Mts. - the sulfate:mass ratio is greater than 0.83, indicating a sulfate source composition 

Figure B-22. Sulfate characteristics of Battelle secondary sulfate/coal sources. 

0

10

20

30

40

50

60

70

80

90

100

ACAD BRIG DOSO GRSM JARI LYBR MACA SHEN WASH

Percent Sulfate at Site or in Source

% SO4 at site % SO4 in source
Appendix B: Source Apportionment Methods Page B-33

more acidic than ammonium bisulfate. At other sites such as Brigantine, Lye Brook and 

Washington DC, with SO4:mass ratios of 0.55 to 0.60, the sulfate sources must contain species in 

addition to sulfate and ammonium in order to account for their mass contributions, and there is often 

a significant organic matter component – in the range of 10 to 15 % in these “sulfate” or “coal” 

sources. 

A number of the studies summarized in section B.1 identified two or more separate “sulfate” 

or “coal” sources or “source components” which are typically distinguished from each other by very 

different ratios of sulfate (or sulfur) to selenium. Both species are emitted in the US predominantly

by the same sources – coal burning utilities, but Se is a primary aerosol (emitted in particle phase or 

condensing shortly after emission) while SO4 is primarily a secondary species (formed at varying 

rates in the atmosphere). Consequently, the S:Se ratio at downwind receptors varies considerably as 

a function of the efficiency of secondary aerosol formation in the atmosphere, and S and Se are not 

well correlated at ambient monitoring sites. This is illustrated in the left panel of Figure B-23, 

which plots S vs. Se for all IMPROVE sites from 2000 through 2003 (about 50,000 samples) and 

shows a poor correlation (R2 = 0.21). However, when the incremental probabilities for these same 

2000-2003 S and Se data are calculated in CATT, their upwind locations are highly correlated (R2

 = 

0.88), as displayed on the right side of Figure B-23(based on 4000 common grid locations), and 

also displayed graphically in Figure B-24. 

Figure B-23. Sulfur vs. Selenium at IMPROVE sites (left) 

and by Incremental Probability (right), 2000-2003 

Figure B-24. Incremental Probabilities for High (top 10%) SO4 (left) and Se (right), 2000-2003 
Appendix B: Source Apportionment Methods Page B-34

Receptor models like PMF and UNMIX identify sources of fixed, constant chemical 

composition, and so can’t find a single source with a variable S:Se ratio at the receptor sites. They

can however, break this source influence into two or more “source components” which do have 

constant ratios. This is illustrated in the Figure B-25 scatter plots of S vs. Se at the Underhill, VT 

and Brigantine NJ sites, over the time periods of PMF and UNMIX modeling reported by Polissar 

et al (2001) and Poirot et al (2001) at Underhill (1989-95), and reported by Lee et al. (2003) and 

Poirot and Wishinski (2002) at Brigantine (1991-1999). Plotted points are colored to summer and 

winter measurements, showing generally much more efficient segondary formation in the warm 

season. The solid and dashed lines represent the S:Se ratios of the 2 fixed ratio source components 

identified by the UNMIX and PMF models respectively. Note that they bound the outer extremes 

of the varying S:Se ratios of the data (“hard edges” in the scatter plots, which UNMIX specifically 

seeks and quantifies). These “sources” are then interpreted as representing the primary aerosol 

component (minimal secondary transformation) and secondary aerosol component (maximal 

secondary transformation) from coal burning and the daily total source impact is determined by the 

sum of these components, which are combined in different proportions on different sample days. 

In the Toronto PMF and UNMIX modeling, additional species were measured, including 

(good quality) NH4 and several organic acids. The PMF modeling at that site identified 3 coalrelated source components: primary, secondary neutral ((NH4)2SO4), and secondary acidic 

(NH4HSO4). The acidic component also included a significant OC content (while the other 2 

components did not). The authors suggested this might be indicative of the “acid-catalyzed 

secondary organic aerosol formation” mechanism, identified by Jang et al. (2002). Further support 

for this hypothesis is provided by the surface wind and trajectory analyses which showed the 3 coalrelated components tended to come from the same location (of high SO2 emissions, but not high 

VOC emissions), and from different locations than other nitrate and motor vehicle source 

influences. 

Figure B-25. Illustration of S:Se ratios in "Primary Coal" and Secondary Coal" Source 

Components by PMF and UNMIX models at Underhill, VT and Brigantine, NJ. 
Appendix B: Source Apportionment Methods Page B-35

The secondary OC content of the 

acidic sulfate component in Toronto may 

help explain the significant OC fraction 

identified in “secondary sulfate” sources 

identified in other modeling studies using 

less detailed measurement data. For 

example, a single large “secondary sulfate” 

source was identified in the Coutant et al. 

(2002) modeling at Washington, DC (at the 

southeastern corner of the MANE-VU 

domain), which included an organic matter 

content of about 10%. In Figure B-26 the 3 

“coal-related” source components in 

Toronto (at the northwestern corner of 

MANE-VU) and the single “secondary 

sulfate” source in Washington DC show 

upwind incremental probabilities 

overlapping a common region of source 

influence, which coincides to the region of 

the highest density of coal burning and SO2

emissions. 

As results from additional monitoring sites and receptor modeling studies are added (Figure 

B-27), it becomes clear that many Class 1 and urban sites throughout the eastern US are influenced 

by this common source region, and that reductions in coal-related SO2 emissions would have 

substantial benefits for improved visibility and reduced PM concentrations throughout much of the 

eastern US (and eastern Canada). 

Figure B-26. Inc. Probs. for "Coal-related 

sources” at Toronto, Ont. (green) and 

Washington DC (red) 

Figure B-27. Incremental Probabilities for "Secondary Sulfate" (Coal) sources at Eastern US sites. 
Appendix B: Source Apportionment Methods Page B-36

Figure B-28. Comparison of emissions data with oil and coal sources at Underhill and Brigantine 
Appendix B: Source Apportionment Methods Page B-37

Additional demonstration that the modeled “sulfate sources” are predominantly coal-related 

is provided in the Figure B-28 comparison of (utility) emissions data from the 1998 EPA EGRID 

inventory. The top panel shows SO2 emissions data from oil and coal fired utilities, respectively. 

These data are gridded and interpolated to show the general northeastern regions where these 

different fuels predominate. The second panel shows incremental probabilities for “oil” sources, 

identified by separate PMF and UNMIX model runs (and modeling groups) at the Underhill, VT 

and Brigantine, NJ sites respectively. These are averaged for the two sites to “triangulate a more 

accurate source location. The third panel displays similar results for the sum of “primary” and 

“secondary” coal sources identified in these model runs. The oil source comes from where oil is 

burned, and the coal source comes from where coal is burned. 

Figure B-29 compares a 

gridded trajectory aggregation of 

trajectory endpoints from a CATT 

query of all IMPROVE sites for the 

past 4 years, for sites and dates where 

SO4 exceeded 15 ug/m3 (top panel). 

A very similar pattern results 

from an aggregation of the highest 

10% of days for the Battelle 

“secondary sulfate” sources at the 7 

northeastern sites indicated in the 

middle panel. 

The third panel is based on all 

IMPROVE sites and dates where 

Deciview values (reconstructed 

extinction) exceeded 30. Coal sources 

that most affect MANE-VU sites are 

associated with the highest sulfate and 

poorest visibility conditions 

throughout the eastern US. 

This influence is not limited to 

the Eastern US (and Canada). The 

consensus results from a variety of 

dispersion and receptor model results 

during the BRAVO study (Pitchford et 

al. (2004) indicated 30% of sulfur at 

Big Bend National Park in Western 

TX came from the “Eastern US.” 

While Green et al. (2004) showed 

occasional eastern sulfate source 

impacts as far west as the Colorado 

Plateau. 

Figure B-29. Comparative probability fields for 

Sulfate, Sulfate Sources and Deciviews 
Appendix B: Source Apportionment Methods Page B-38

Figure B-30 and Figure B-31 are limited to 2000-2003 data from the MANE-VU Class 1 

areas only, and show (Figure B-30) incremental probabilities for high sulfate (by the “Mark Green 

method)”, and (Figure B-31) upwind average sulfate (with calculations constrained to grid cells 

with > 40 trajectory endpoints). The colored contour lines are for the indicated individual sites, 

probability levels and/or sulfate concentrations, and the grey shaded areas depict linear averages 

aggregated for the five MANE-VU Class 1 sites. 

Figure B-30. Incremental Probabilities for High SO4 at MANE-VU Class 1 Sites, 1999-2002 

Figure B-31. Average Upwind SO4 at MANE-VU Class 1 Sites, 1999-2002 
Appendix B: Source Apportionment Methods Page B-39

While the patterns appear “similar” in this case, the two trajectory metrics address different 

questions. Incremental probability asks “where was the air if sulfate was high at the receptor?” and 

the upwind average asks “how high is the sulfate at the receptor if the air was over specific upwind 

locations?” Neither of these provides a quantitative answer to the question “what fraction of the 

receptor sulfate comes from which upwind locations?” The can however provide a qualitative 

indication of the relative importance of different contributing regions. 

Figure B-32 shows the results of the incremental probabilities and upwind averages in 

Figure B-30 and Figure B-31 disaggregated into state totals (for the 31 easternmost states) and 

ranked. The ranks generated for the two different metrics were averaged, and the states on the Y 

axis are ordered from highest (left) to lowest (right) for these average ranks. It may be noted that 

the ranking for the two different metrics show largest differences for the physically smallest and 

nearby (and/or downwind) states in the MANE-VU region (MD, NJ, DE, DC, CT, MA, RI, VT & 

NH). One reason for this is that the state aggregations were summed for incremental probabilities 

(which reflects both concentration and frequency) and averaged for the upwind averages (which do 

not reflect frequency) and this causes the metrics to differ most for the smallest states. In addition, 

individual grid locations within these small, nearby states are frequently upwind (lots of trajectory 

endpoints) and the “upwind averages” for locations in these states are moderate (similar to the 

average concentrations at the receptor sites), but when sulfate concentrations are high, these states 

are less likely to be upwind than they are on an everyday basis. 

It may be noted that the top 10 states are distributed among the MANE-VU, Midwest and 

VISTAS RPO planning regions, and that coordinated inter-regional strategies will be needed to 

assure future progress toward the national visibility goals. 

Figure B-32. Ranked State Contributions for Incremental Probability and Upwind 

Average Sulfate for MANE-VU Class 1 Receptors. 
Appendix B: Source Apportionment Methods Page B-40

B.3. References 

1. Allen, G. A., Babich, P. and R. Poirot (2004) Evaluation of a New Approach for Real Time 

Assessment of Wood Smoke PM, Regional and Global Perspectives on Haze: Causes, 

Consequences and Controversies, A&WMA Visibility Specialty Conference, Asheville,

NC, October 25-29, 2004. 

2. Brook, J.R., R.L. Poirot, T. F. Dann, P.K.H. Lee, C.D. Lillyman, and T. Ip (2003), 

Assessing Sources of PM2.5 in Cities Influenced by Regional Transport, Proceedings: 

AIRNET and NERAM Conference on: Strategies for Clean Air and Health, Rome, Italy. 

3. Chen, L.-W.A., Doddridge, B.G., Dickerson, R.R., Chow, J.C., Henry, R.C., 2002. Origins 

of fine aerosol mass in the Baltimore-Washington corridor: implications from observation, 

factor analysis, and ensemble air parcel back trajectories. Atmos Environ. 36, 4541-4554. 

4. Chueinta, W., Hopke, P.K., Paatero, P., 2004. Multilinear model for spatial pattern analysis 

of the measurement of haze and visual effects project. Environ. Sci. Technol. 38(2), 544-

554. 

5. Coutant, B.W., Engel-Cox, J., Swinton, K.E., 2003. Compilation of existing studies on 

source apportionment for PM2.5: second draft technical report (revision 1). Prepared for 

U.S. Environmental Protection Agency, Contract No. 68-D-02-061, Work Assignment No: 

1-05. 

6. Coutant, B., Holoman, C., Swinton, K., and Hafner, H. 2003. Eight-site source 

apportionment of PM2.5 Speciation Trends Data: final report. Prepared for U.S. 

Environmental Protection Agency, Contract No. 68-D-02-061, Work Assignment No: 1-05. 

7. Coutant, B., Kelly, T., Ma, J., Scott, B., Wood, B., and Main, H. 2002. Source 

apportionment analysis of air quality monitoring data: Phase I final report. Prepared for the 

Mid-Atlantic/Northeast Visibility Union and Midwest Regional Planning Organization by 

Battelle Memorial Institute and Sonoma Technology, Inc. 

8. Dorling, S.R.; Davies, T.D.; Pierce, C.E., “Cluster analysis: A technique for estimating 

synoptic meteorological controls on air and precipitation chemistry – Method and 

Application,” Atmos. Env., 1992, 26A, 2575-2581. 

9. Draxler, R.R. and Rolph, G.D. (2003) HYSPLIT (HYbrid Single-Particle Lagrangian 

Integrated Trajectory) NOAA Air Resources Laboratory, Silver Spring, MD. 

10. Gao, N., Krumhansl. K., Lafferty, K., Hopke, P.K., Kim, E., Poirot, R.L., 2004. Sources of 

fine particulate species in ambient air over Lake Champlain Basin, Vermont. Journal of Air 

& WMA Association. Special Conference on Regional and Global Perspective on Haze: 

Causes, Consequences and Controversies, Asheville, NC, October 25-29, 2004. 
Appendix B: Source Apportionment Methods Page B-41

11. Green, M.C., D. Dubois, J. Xu and M.L. Pitchford, 2004. Westward Transport of Sulfate 

Rich Haze from the Eastern United States, Paper No. 60, A&WMA Specialty Conference on 

Regional and Global Perspective on Haze: Causes, Consequences and Controversies, 

Asheville, NC, October 25-29, 2004. 

12. Grell, G.A., Dudhia, J. and Stauffer, D.R.; A description of the fifth generation Penn 

State/NCAR Mesoscale Model (MM5); NCAR technical Note, NCAR TN-398-STR, page 

138, 1994. 

13. Heffter, J.L., Air Resources Laboratory Atmospheric Transport and Diffusion Model (ARLATAD), National Oceanic and Atmospheric Administration, Technical Memo, ERL-ARL81, 1980. 

14. Henry, R.C., Chang, Y.-S., Spiegelman, C.H., 2002. Locating nearby sources of air pollution 

by nonparametric regression of atmospheric concentration on wind direction. Atmos 

Environ. 36, 2237-2244. 

15. Holland, D.M., Cox, W.M., Scheffe, R., Cimorelli, A.J., Nychka, D., Hopke, P.K., 2003. 

Spatial prediction of air quality data. EM, 31-35. 

16. Hopke, P.K., 1991. An introduction to receptor modeling. Chemometrics and Intelligent 

Laboratory Systems. 10, 21-43. 

17. Husar, R.B., Höijärvi, K., Poirot, R.L., Kayin, S., Gebhart, K.A., Schichtel, B.A., Malm, 

W.C. Combined aerosol trajectory tool, CATT: Status Report on Tools Development and 

Use, 2004. A&WMA Specialty Conference on: Regional and Global Perspectives on Haze: 

Causes, Consequences and Controversies, Asheville, NC, October 25-29, 2004. 

18. Husar, R.B., 2004. Combined Aerosol Trajectory Tool, CATT: An Analytical Tool 

Extending VIEWS Data System, Final Report Prepared by Center for Air Pollution Impact 

and Trend Analysis (CAPITA)Washington University in St. Louis to the MARAMA contract 

on behalf of Mid-Atlantic/Northeast Visibility Union (MANE-VU) and the Inter-RPO Data 

Analysis Workgroup. 

19. Husar, R. B., 2005. CATT Links Page and Illustrated Instruction Manual, 

http://datafed.net/projects/CATT/CATT_Links.htm

20. Ito, K., Xue, N., Thurston, G., 2004. Spatial variation of PM2.5 chemical species and sourceapportioned mass concentrations in New York City. Atmos Environ. 38, 5269-5282. 

21. Jang, M.; Czoschke, N. M.; Lee, S.; Kamens, R. M. 2002. Heterogeneous Atmospheric 

Aerosol Production by Acid-Catalyzed Particle-Phase Reactions. Science: 298, 814-817. 

22. Kenski, D., 2004. Quantifying Transboundary Transport of PM2.5: A GIS Analysis. Control 

# 247, AWMA Annual Meeting, Indianapolis, June 2004. 

23. Kim, E., Larson, T.V., Hopke, P.K., Slaughter, C., Sheppard, L.E., Claiborn, C., 2003. 

Source identification of PM2.5 in an arid Northwest U.S. City by positive matrix 

factorization. Atmos res. 66, 291-305. 
Appendix B: Source Apportionment Methods Page B-42

24. Kim, E., Hopke, P.K., 2004. Comparison between conditional probability function and 

Nonparametric regression for fine particle source directions. Atmos Environ. 38, 4667-4673. 

25. Kim, E., Hopke, P.K., Paatero, P, Edgerton, E.S., 2003. Incorporation of parametric factors 

into multilinear receptor model studies of Atlanta aerosol. Atmos Environ. 37, 5009-5021. 

26. Kim, E., Hopke, P.K., 2004. Source apportionment of fine particles in Washington, DC, 

utilizing temperature-resolved carbon fractions. J. Air & Waste Manage. Assoc. 54, 773-

785. 

27. Kim, E., Hopke, P.K., Edgerton, E.S., 2003. Source identification of Atlanta aerosol by 

positive matrix factorization. J. Air & Waste Manage. Assoc. 53, 731-739. 

28. Kim, E., Hopke, P.K., 2003. Source identification of airborne fine particles at multiple sites, 

AAAR 2003, Anaheim, CA October 20, 2003. 

29. Kim, E., Hopke, P.K., 2004. Improving source identification of fine particles in a rural 

northeastern U.S. area utilizing temperature-resolved carbon fractions. J. Geophys. Res. 109, 

D09204. 

30. Kim, E., Hopke, P.K., Larson, T.V., Covert, D.S., 2004. Analysis of ambient particle size 

distributions using UNMIX and positive matrix factorization. Environ. Sci. Technol. 38, 

202-209. 

31. Laden., F., Neas, L., Dockery, D., Schwartz, J., 2000. Association of fine particulate matter 

from different sources with daily mortality in six U.S. cities. Env. Health Perspectives 108: 

941-947. 

32. Lee, P.K.H, Brook, J.R., Dabek-Zlotorzynska, E., Mabury, S.A., 2003. Identification of the 

Major Sources Contributing to PM2.5 Observed in Toronto, Environ. Sci. Technol. 37: 4831-

4840. 

33. Lee, J., Yoshida, Y., Turpin, B., Hopke, P., Poirot, R., Lioy, P., and Oxley, J., 2003. 

Identification of sources contributing to the mid-Atlantic regional aerosol. J. Air & Waste 

Manage. Assoc. 52: 1186-1205. 
Appendix B: Source Apportionment Methods Page B-43

34. Liu, W., Hopke, P.K., Han, Y.-J., Yi, S.-M., Holsen, T.M., Cybart, S., Kozlowski, K., 

Milligan, M., 2003. Application of receptor modeling to atmospheric constituents at 

Potsdam and Stockton, NY. Atmos Environ. 37, 4997-5007. **NEED MASS/emailed 

Hopke** 

35. Maykut, N.N., Lewtas, J., Kim, E., Larson, T., 2003. Source apportionment of PM2.5 at an 

urban IMPROVE site in Seattle, Washington. Environ. Sci. Technol. 37(22), 5135-5142. 

36. NESCAUM Technical Memorandum #7 prepared for MANE-VU. March 2003. Review of 

Speciation Trends Network and IMPROVE Chemically Speciated Data. 

37. Perry, K. D., T. A. Cahill, R. A. Eldred, and D. D. Dutcher, Long-range transport of North 

African dust to the eastern United States, J. Geophys. Res., 102, 11,225– 11,238, 1997. 

38. Pitchford, M.L.; Tombach, I.H.; Barna, M., Gebhart, K.A.; Green, M.C.; Knipping, E.; 

Kumar, N.; Malm, W.C.; Pun, B.; Schichtel, B.A.; Seigneur, C.; 2004. Big Bend Regional 

Aerosol and Visibility Observation Study (BRAVO Study) Final Report; Available on the 

IMPROVE web site http://vista.cira.colostate.edu/improve/; September 2004.

39. Poirot, R. and P. Wisinski, 2002. Application of Combined Mathematical and 

Meteorological Receptor Models (UNMIX & Residence Time Analysis) to 1991-99 

IMPROVE Aerosol Data from Brigantine NWR, NJ. VT Dept. Env. Cons. 

40. Poirot, R. L. and J. R. Brook (2004) Receptor Model Perspectives on Local and Regional 

PM2.5 Sources in Toronto, Chapter 4.1.3.2 in: McMillan, A., Schultz, L. et al. (2004) 

Transboundary Transport, Trends and Analysis of Fine Inhalable Particles in the 

Transboundary Region: Science Assessment, Report by the Canada-United States Air 

Quality Committee Subcommittee 2: Scientific Cooperation. 

41. Poirot, R.L., Husar, R.B., 2004. Chemical and physical characteristics of wood smoke in the 

northeastern US during July 2002 impacts from Quebec forest fires. Paper #94, A& WMA 

Specialty Conference on: Regional and Global Perspectives on Haze: Causes, Consequences 

and Controversies, Asheville, NC, October 25-29, 2004. 

42. Poirot R.L., Wishinski, P.R., Hopke, P.K., Polissar, A.V., 2001. Comparative application of 

multiple receptor methods to identify aerosol sources in northern Vermont. Environ. Sci. 

Technol. 35, 4622-4636. 

43. Polissar A.V., Hopke, P.K., Poirot, R.L., 2001. Atmospheric aerosol over Vermont: 

Chemical composition and sources. Environ. Sci. Technol. 35, 4604-4621. 

44. Song, X.H., Polissar, A.V., Hopke, P.K., 2001. Sources of fine particle composition in the 

northeastern US. Atmos Environ. 35, 5277-5286. 

B.3.1. Other References 

1. Kleiman G., Graham, J., Kheirbek, I., Hamel, N., Ulbrich, I., Lehner, J. Comparison of 

trajectory clustering techniques with source-tagging chemical transport models. A& WMA 
Appendix B: Source Apportionment Methods Page B-44

Specialty Conference on: Regional and Global Perspectives on Haze: Causes, Consequences 

and Controversies, Asheville, NC, October 25-29, 2004. 

2. Lee, T., Yu, X.-Y., Ayres, B., Carrillo, J., Carrico, C., Herckes, P., McMeeking, G.R., 

Engling, G., Kreidenweis, S.M., Collett, J.L.Jr., Day, D.E., Malm, W., 2004. Characteristics 

of aerosol nitrate at several IMPROVE monitoring sites. Paper #38, A& WMA Specialty 

Conference on: Regional and Global Perspectives on Haze: Causes, Consequences and 

Controversies, Asheville, NC, October 25-29, 2004. 

3. Lowenthal, D.H., and Rahn, KlA., 1989. The relationship between secondary sulfate and 

primary regional signatures in northeastern aerosol and precipitation. Atmos Environ. 23, 

1511-1515. 

4. Lucey, D., Hadjiiski, L., Hopke, P.K., Scudlark, J.R., Church, T., 2001. Identification of 

sources of pollutants in precipitation measured at the mid-Atlantic US coast using potential 

source contribution function (PSCF). Atmos Environ. 35, 3979-3986. 

5. Lupu, A., Maenhaut, W., 2002. Application and comparison of two statistical trajectory 

techniques for identification of source regions of atmospheric aerosol species. Atmos 

Environ. 36, 5607-5618. 

6. Mattye, W. 2002. Compendium of existing back-trajectory analyses relating to US-Canada 

trans-boundary impacts of fine particulate matter and regional haze. Draft report for U.S. 

Environmental Protection Agency, Contract No. 68-D-98-006, Work Assignment No: 5-07. 

7. Moody, J.L.; Munger, J.W.; Goldstein, A.H.; Jacob, D.J.; Wofsy, S.C., “Harvard Forest 

regional-scale air mass composition by Patterns in Atmospheric Transport History (PATH),” 

J. Geophys. Res., 1998, 103, 13181-13194. 

8. Paatero, P., Hopke, P.K., Eberly, S.I., 2003. Advanced factor analysis of spatial distribution 

of PM2.5 in the eastern United States. Environ. Sci. Technol. 37, 2460-2476. 

9. Paatero, P., Hopke, P.K., Hoppenstock, J., Eberly, S.I. Supporting information for advanced 

factor analysis of spatial distribution of PM2.5 in the eastern United States. Environ. Sci. 

Technol. Supporting Information pp1-6. 

10. Park, E.S., Spiegelman, C.H., Henry, R.C., 2002. Authors’ response. Environmetrics. 13, 

807-809. 

11. Park, E.S., Spiegelman, C.H., Henry, R.C., 2002. Bilinear estimation of pollution source 

profiles and amounts by using multivariate receptor models. Environmetrics 13, 775-798. 

12. Parrish, D., Purser, J., Rogers, E., Lin, Y. The regional 3D variational analysis for the Eta 

Model., 11th Conf. On Numerical Weather Prediction, Norfolk, VA, Amer. Meteor. Soc., 

454–455, 1996. 

13. Poirot, R.L., Wishinski, P.R., Hopke, P.K., and Polissar, A.V. (2000), “Sources of Fine 

Particle Concentration and Composition in Northern Vermont,” Proc. AWMA International 
Appendix B: Source Apportionment Methods Page B-45

Symposium on Measurement of Toxic and Related Air Pollutants, Research Triangle Park, 

North Carolina, September 2000. 

14. Poirot, R., P. Wishinski, B. Schichtel and P. Girton (1999) Air Trajectory Pollution 

Climatology for the Lake Champlain Basin, AGU Water Monograph on Lake Champlain 

Research and Implementation, T Manley and P. Manley, Ed., American Geophysical Union, 

Washington, DC. 

15. Poirot, R.L. and Wishinski, P.R. (1998) Long-Term Ozone Trajectory Climatology for the 

Eastern US, Part 2: Results, 98-A615, Proceedings 91st Annual A&WMA Meeting, San 

Diego, CA. 

16. Poirot, R. L., Wishinski, P. R. Flocchinu, R.G., Artz, R. A., Keeler, J, Schichtel B.A. and 

Husar R.B. (1992) Transboundary implications of visibility-impairing aerosols in the 

Northeastern United States (Part I): Space/time patterns from NESCAUM aerosol network. 

Spring, 1992 Meetings of American Geophysical Union and Canadian Geographical Union,

Montreal, Canada. 

17. Poirot, R.L., and Wishinski, P.R. (1986), “Visibility, Sulfate, and Air Mass History 

Associated with the Summertime Aerosol in Northern Vermont,” Atmos. Envir., 20(7), pp 

1457-1469. 

18. Sato, M., 2002. Importance of identification of the model: some comments on the article 

‘Bilinear estimation of pollution source profiles and amounts by using multivariate receptor 

models’. Environmetrics 13, 799-800. 

19. Schauer, J., Cass G.R., 2000. Source Apportionment of wintertime gas-phase and particlephase air pollutants using organic compounds as tracers. Environ. Sci. Technol. 34, 1821-

1832. 

20. Schauer, J.J., 2003. Evaluation of elemental carbon as a marker for diesel particulate matter. 

J. Expo. Anal. Environ. Epidemiol.13 (6), 443-453. 

21. Schichtel, B.A. and Husar, R.B.; 1997. Regional Simulation of Atmospheric pollutants with 

the CAPITA Monte Carlo Model; J. Air and Waste Management Association, 47, 331-343. 

22. Schwartz, J., F., Zanobetti, A. 2002. The concentration-response relation between PM2.5 and 

daily deaths. Environ. Health Perspectives 110: 1025-1029. 

23. Thomas, G., Hidy, G., 2003. Using factor analysis to attribute health impacts to particulate 

pollution sources. Special issue of information toxicology on particulate air pollution and 

health. 

24. U.S. Environmental Protection Agency. January 2004. Air Quality Data Analysis Technical 

Support Document for the Proposed Interstate Air Quality Rule. 

25. U.S. EPA and Environment Canada (2004) United States-Canada Air Quality Agreement, 

Progress Report 2004. 
Appendix B: Source Apportionment Methods Page B-46

26. Wishinski, P. and R. Poirot (1986) Source/receptor relationships for a number of factors 

contributing to summertime variation in light extinction in northern Vermont, pp. 807-822, 

Transactions APCA Spec. Conf. on Visibility Protection: Research and Policy Aspects, P.S. 

Bhardwaja, Ed., Jackson Hole, WY. 

27. Wishinski, P.R. and Poirot, R.L. (1998) Long-Term Ozone Trajectory Climatology for the 

Eastern US, Part 1: Methods; 98-A615, Proceedings 91st Annual A&WMA Meeting, San 

Diego, CA. 

28. Wishinski, P.R., Poirot, R.L., Flocchini, R.G., Artz, R. A., Keeler, J, Schichtel B.A. and 

Husar R.B. (1992) Transboundary implications of visibility-impairing aerosols in the 

Northeastern United States (Part II): air trajectory assessment of the NESCAUM aerosol 

data, Spring, 1992 Meetings of American Geophysical Union and Canadian Geographical 

Union, Montreal, Canada. 

29. Yli-Tuomi, T., Hopke, P.K., Paatero, P., Basunia M.S., Landsberger, S., Viisanen, Y., 

Paatero, J., 2003. Atmospheric aerosol over Finnish Arctic: source analysis by the 

multilinear engine and the potential source contribution function. Atmos Environ. 37, 4381-

4392. 

30. Yu, K.N., Cheung, Y.P., Cheung, T., Henry, R.C., 2004. Identifying the impact of large 

urban airports on local air quality by nonparametric regression. Atmos Environ. 38, 4501-

4507. 

31. Zheng, M, Cass, G.R., Schauer, J.J., Edgerton, E.S., 2002. Source apportionment of PM2.5 in 

the southeastern United States using solvent-extractable organic compounds as tracers. 

Environ. Sci. Technol. 2361-2371. 

32. Zhou, L., Hopke, P.K., Liu, W., 2004. Comparison of two trajectory based models for 

locating particle sources for two rural New York sites. Atmos Environ. 38, 1955-1963. 

33. Zhou, L, Kim, E., Hopke, P.K., Stanier, C.O., Pandis, S., 2004. Advanced factor analysis on 

Pittsburgh particle size-distribution data. Aerosol Sci. Technol. 38(S1), 118-132.