Appendix B: Source Apportionment Methods Page B-1



# Appendix B: Source Attribution by Receptor-Based

Methods

Serpil Kayin Mid-Atlantic Regional Air Management Association (M ARAMA) Baltimore, MD.

Richard L. Poirot Vermont Department of Environmental Conservation (V T 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 mult ivariate mathematical models applied to speciated aerosol data from individual monitoring sites, as w ell as ensemble trajectory evaluations, applied to help evaluate and interpret the mathematical model resul ts, and to identify the most prominent regional ori gins of these sources. A number of common source categorie s were identified, which have discernable impacts o n average PM 2.5 mass concentrations and visibility impairment at m ost Northeastern monitoring sites. These
include:

Windblown Dust : a minor contributor to average fine mass, but cle arly identified at all sites, with highest short term impacts often associated with Sahara dus t transport.

Sea Salt : a minor contributor to fine mass, but clearly ide ntified at coastal and near coastal sites. It can have significant visibility impacts at coastal sites lik e Acadia and Brigantine on the best visibility days

.

Oil Burning : a minor contributor to fine mass, but clearly ide ntified at many MANE-VU sites, especially
sites within and downwind of the Northeast urban co rridor.

Ammonium Nitrate : a small to moderate contributor to average fine m ass, with regional influences at rural sites from upwind agricultural ammonia-emitting are as, and significant local source contributions in u rban

areas.

Wood Smoke : a small to moderate contributor to average fine m ass, with contributions typically higher in rural areas than urban areas, winter peaks in north ern areas from residential wood burning, and occasi onal large summer impacts at all sites from wildfires.

Motor Vehicles and Secondary Organics : a moderate to large contributor to average fine m ass, 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 si tes, 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 emiss ions inventories and act on them in a “forward” direction with atmospheric physical and c hemical process to predict downwind concentrations at multiple “receptor locations.” On ce validated by comparison to ambient measurement data, these models can be used for appo rtioning ambient pollutant concentrations to specific sources, as well as for evaluating potenti al effects of future changes in emissions or meteorology. Receptor-based models begin with ambi ent 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 app lied to long historical records, providing a longterm “climatological” indication of past source-rec eptor relationships and to evaluate the effects of historical emissions changes, thus providing a valu able complement to emission-based models for determining effective future emissions control stra tegies.

Two general categories of receptor methods include multivariate mathematical models and ensemble backward trajectory techniques. Multivari ate 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 a s Cluster Analysis, Potential Source Contribution Function (PSCF) and Residence Time Analysis (RTA) a re 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 measur ed source composition profiles, while PCA, PMF and UNMIX develop the source profiles duri ng 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 req uired 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 n eed not be known in advance, and the disadvantage that the resulting “sources” require s ubjective interpretation by the modelers to identify what these source influences actually repr esent. Resulting sources may represent an individual point source, source category, source re gion, meteorological influence, measurement or data processing artifacts or various combinations o f the above. An additional characteristic of these multivariate models is that the resulting sources h ave 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 p recursors (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 forma tion, which can result in a variable “virtual sourc e profile” at downwind receptor locations. Consequen tly, models like PMF and UNMIX may divide a source influence into 2 or more “source component s”, each with constant but different chemical compositions, representing different degrees of sec ondary aerosol formation.

Because of these complexities in source interpretat ion, and because the multivariate models typically rely entirely on the measured chemical co mpositions without regard to meteorology, the ensemble trajectory techniques (which rely on meteo rology-only) are especially useful for interpreting and/or evaluating the multivariate mod el results.Appendix B: Source Apportionment Methods Page B-3



### B.1. Multivariate Mathematical Results Relevant to the M ANE-VU Region

B.1.1. Introduction

This section summarizes source apportionment studie s conducted for sites within the MidAtlantic/Northeast Visibility Union (MANE-VU) regio n, and a few neighboring sites within the upwind or down-wind influence area of MANE-VU. The in tent is to provide a general overview of the source categories identified in these various studi es, and to provide an indication of their relative
contributions to fine mass concentration and visibi lity impairment in the MANE-VU region. For additional detail, the reader is encouraged to cons ult the original references, as well as the traject ory interpretations in Section B.2 of this report.

Many of the results presented here were from an exp loratory “Phase 1” PM 2.5 source apportionment study previously sponsored by MANE-VU and the Midwest RPO (Coutant et al. 2002) which was also extensively used in a subseque nt U.S. Environmental Protection Agencysponsored review report (Coutant et al. 2003). Thes e 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, Janua ry 2004) and in a recent Clean Air Act Advisory, Science and Technology Subcommittee report (Synthes is of Air Quality Assessments: Identification of Important Contributors to Ozone and PM Nonattain ment, and Regional Haze, October 15, 2004). Other studies and peer reviewed journal articles ar e also cited in this summary and referenced at the
end of the document.



### B.1.2. Background

A number of mathematical receptor models and ensemb le trajectory analysis techniques have commonly been used in the studies summarized b elow in order to apportion the PM 2.5 mass concentrations (and, hence, the light extinction va lues) 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 Fa ctorization), UNMIX, APCA (Absolute Principal Component Analysis), and CMB (Chemical Ma ss Balance). Some studies also used these tools in conjunction with trajectory transport anal ysis such as PSCF (Positive Source Contribution Function), RTA (Residence Time Analysis), Cluster A nalysis and other ensemble back-trajectory techniques, to assist in interpreting and identifyi ng primary locations of these sources. An extensive literature explaining these ensemble trajectory tec hniques is available, but these explanations are no t referenced in this summary. A recently developed “C ombined Aerosol Trajectory Tool” (CATT) described by Husar et al. (2004) includes options t o 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 si tes

The main goal of the studies summarized here is to describe and quantify the major source categories contributing to the observed concentrati ons of fine particulate matter in the atmosphere. This is achieved by mathematically modeling the day to day variation in the PM 2.5 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 t hat each source contributing to the PM 2.5 concentration contributes to the observed species c oncentrations with approximately fixed ratios. The list of the ratios of the species mass to the t otal 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 contribut ion), and the magnitude of the source. Further
evaluation of the results with local surface meteor ological data (for local source influences) andAppendix 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 orig inal source interpretations.

Typically, the studies included in this summary use d 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, a nd overall regionally, the results are generally consistent when comparable data sets were used. How ever, methodologies vary considerably among studies. In addition, the primary goals of apportio ning total PM 2.5 or visibility are quite different. Hence, there are difficulties with making direct co mparisons. For example, the modeled source profiles typically include mixtures of several diff erent aerosol species with different extinction efficiencies and/or hygroscopic growth functions. Several of the reviewed studies have included
estimates of source contributions to PM 2.5 mass on best 20% and worst 20% visibility days, bu t 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 PM 2.5 mass (and percentage of PM 2.5 mass). Quoting from the recent Clean Air Act Advisory, Science and Technology Subcommittee r eport (Synthesis of Air Quality Assessments: Identification of Important Contributo rs to Ozone and PM Nonattainment, and Regional Haze, dated October 15, 2004):

“Because regional haze is linked so closely to PM 2.5 , few source apportionment studies look at haze impacts separately. Nevertheless, some obse rvations about the relationship between

PM 2.5 and haze are appropriate. Light extinction is a fu nction of the particular components
of PM 2.5 as well as relative humidity. While the sources re sponsible for PM 2.5 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 IMPROV E over the past decades has shown that light extinction almost everywhere across the count ry is primarily due to sulfate; organic carbon is next most important almost everywhere. Re gional 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 impo rtance of regional haze species is the same (for most of the country) as that for PM 2.5 , and because impaired visibility has adverse effects on public welfare in both urban and rural a reas, control measure priorities for reducing human health and visibility effects of PM 2.5 are strongly convergent.”

This compilation summarizes several reports and art icles 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 n ation as a whole can be found in Coutant et al. (2003). In addition to those, the summary below add s a few new articles for the MANE-VU region. Figure B-1 below shows the locations of all sites r eferred to in this summary. Tables provided throughout this summary reference the original stud y, 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 w ere 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 ne tworks. Typically, more detailed measurement
data leads to more detailed source characterization . For example, inclusion of the thermally stratified TOR carbon fractions (available for IMPR OVE sites only), has allowed for separate detection of gasoline and diesel motor vehicle sour ces – especially in urban areas like Seattle (Maykut et al., 2003), Toronto (Poirot and Brook, 2 004), Atlanta and Washington DC (Kim and Hopke, 2003, 2004), and at the “near urban” Brigant ine 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 co mponents, with a substantial “secondary organic aerosol” component associated with the acidic sulfa tes (Lee et al, 2003, Poirot and Brook, 2004).

The general results from many of the studies were f ound to be similar as the methodologies
have become more mature and data sets broader and m ore comparable, and the collective experience and knowledge of the modeling community advances. Following Coutant (2003) the

Figure B-1. Locations of the Monitoring Sites Revi ewed in This ReportAppendix B: Source Apportionment Methods Page B-6

sources identified grouped into seven categories: s ulfate/coal, secondary organic compounds/mobile, nitrate, biomass burning, industr ial, 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 sulfa te primarily attributable to coal-fired power plant s in the ‘Midwest” (indicated by presence of S and Se ). Some studies found a single large “sulfate” or “coal” source influence, accounting for high fracti ons of both total Se (primary) and SO 4 (secondary) aerosol concentrations. Several other s tudies tended to split an overall coal burning influence into two “primary” and “secondary” compon ents, 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) histo rical records, the sulfate/coal sources typically
showed reductions over time.

A large “secondary organic matter/mobile” source wa s 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 fo r nearly all sites. Generally, the sources of carbonaceous aerosols have not been clearly identif ied in many of these studies, especially at rural sites, and initial interpretations of “mobile sourc e” 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 int o 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 i n the exploratory Coutant et al., 2002 studies.

“Nitrate” was also found to be a significant source (in those studies that included NO 3 as an input variable). Typically, most of the NO 3 tends to break out into a separate general “source ” category, assumed to be ammonium nitrate, and the m odel 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 accumul ation of ammonium nitrate in the ambient air in the Northeast. The receptor models have more di fficulty differentiating between sources of complex secondary pollutants than primary pollutant s. In the Eastern US, regional ammonium nitrate concentrations are highest in and immediate ly 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 si milar 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 th e biomass burning source varies considerably from site to site, but it is usually higher in rura l areas, as expected. Winter residential wood burn ing impacts can also be substantial in small northeaste rn villages located in deep mountain valleys like Rutland, VT (Allen et al., 2004).

The industrial source category includes a variety o f 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 w ith known local sources. Residual oil burning impacts from both industrial and utility sources ar e especially evident along the East Coast urban
corridor.Appendix B: Source Apportionment Methods Page B-7

A “crustal” (dust, soil, etc.) source category is i ndicated 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 la rge fraction of the trace elements listed above (which are also assumed to result predominantly from soil in the IMPROVE soil formula). Highest dust imp acts 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, B rigantine) and at near-coastal sites (NYC, Washington DC, Shenandoah, Dolly Sods, Gt Smokey Mt ns.). 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 emis sions. A curious feature of the sea salt sources identified (at 6 sites) in the Coutant (2002) studi es was that it that it showed a significant increas e over time at all sites. Road salt sources, often m ixed 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) u sed, mean PM 2.5 mass, source apportioned PM 2.5 mass according to the identified source types and f ractions of the total mass (in parenthesis). The
source of the information is referenced. In additio n, 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 con tributing greater than 20 percent but less than 40 percent of the total mass are shaded yellow; source s 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 repor ted 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/m 3 )

(% 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 th e southern slope of McFarland Hill. Upwind of the receptor, there are four small towns with resid ents that have home heating needs. Park visitation is three million people/year, with more than two-th irds visiting during June-September, creating high volume vehicle traffic. More than 50 large cru ise ships visit Bar Harbor during summer and fall, and the number of cruise ships is increasing. PM 2.5 concentrations at this site tend to be low, averaging 7.5 μ g/m3. Initial results from Coutant et al. (2002) in dicated that mobile sources dominate at this site, followed by the regional sec ondary sulfate. However, the reconstructed
masses of these modeled sources are inconsistent wi th the model-apportioned masses, suggestingAppendix B: Source Apportionment Methods Page B-8

that the mobile source contribution has been overes timated 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/m 3 )

(% 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 mountaino us and forested areas and these areas are sparsely populated for about 20 miles in all direct ions from the site. The area has some home heating activity (in nearby valleys), but no indust rial sources of pollution in the vicinity. This is a popular tourist region with substantial vehicle tra ffic. Coutant et al. (2002) estimated that regiona l secondary sulfate was the most important source typ e, 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/m 3 )

(% 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 proto col. The first three studies summarized above (Poirot et al. 2001, Polissar et al. 2001, an d Song et al. 2001) used data from the same period but employed different analysis techniques. The las t study (Gao et al. 2004) used a more recent data set. Results were very consistent, showing simila r chemical composition profiles and temporal variations, providing confidence in the results obt ained by the different techniques. The most important source type was found to be regional seco ndary sulfate, followed by wood smoke. This is
a rural northern latitude area with substantial woo d burning activities. The effects of oil burning an d
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 sec ondary aerosol; in Table B-3 the total of theseAppendix B: Source Apportionment Methods Page B-9

two were used. They also separated subsets of the r esults for the best and worst 20% visibility days. Secondary sulfate was found to be significantly mor e 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/m 3 )

(% 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 studie s of three sites in New York City.

The first and second studies in the table above loo ked at data from New York Botanical Gardens in the Bronx (an urban Speciation Trends Ne twork 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 e ffect on the site. These include mobile emissions, fuel oil (particularly in the winter), two oil-fire d power plants, street cleaning, and marine
influences. The difference between these two studie s is probably attributable to the fact that only th e first study distinguished nitrate as a source type.

The third study in the table above looked at I.S. 5 2 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 sampl er). These three sites are within six miles of
each other.

Results obtained from the four studies summarized a bove 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 PM 2.5 species, indicating that the precision of population exposur e estimates for specific elements can vary depending on the species. However, as expected, th e constituents of secondary aerosols showed consistently high temporal correlations across the monitors. Other PM 2.5 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/m 3 )

(% 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 a t 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 Phil adelphia, 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 analys es, included eight temperature-resolved carbon fractions. This helped distinguish between the dies el and gasoline mobile source types.

Sulfate-rich secondary aerosols are the largest PM 2.5 (and hence regional haze) source, consistently accounting about 60% of the mass in al l the studies of Brigantine that included high PM days. The Poirot-Wishinski analysis of the 20% b est visibility days showed much lower sulfate
influence. The Potential Source Contribution Functi on (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 el evated 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 re sults 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/m 3 )

(% 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 M eade (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 a rea and the highly industrialized Midwest.Appendix B: Source Apportionment Methods Page B-11

The PM 2.5 at this site is expected to be from both local and regional sources. Measurements over a two year period include eight seasonally representa tive months.

The authors conclude the predominate source affecti ng this site is an aged sulfate source more regional in character, resulting from emission s in the Midwest. The aged sulfate moves into Maryland from the north, while the fresh SO 2 /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 i ndustrial/smelter source comes from the
industrial corridor to the northeast of the site.

Chen et al. 2002, indicate summertime PM 2.5 at Fort Meade is dominated by the regional sulfate source. The local contribution (mobile sour ces plus local sulfate) increases from less than 30% in summer to more than 60% in winter. Though hi gh PM 2.5 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/m 3 )

(% Mass) Regional

Secondary Transport General Diesel Gasoline Wood
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 collec ted at the McMillan IMPROVE monitoring site in Washington, DC. This site is loc ated (at 39 N, 77 W) near the Potomac River, 2 km southeast of the Lincoln Memorial, 3 km northeas t of the Ronald Reagan Washington National Airport, and 30 m above sea level. Highways are clo sely 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 differentiat ed, 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/m 3 )

(% 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) surrou nded by forested mountain areas located in the Blue Ridge Mountains. This site is l ocated 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 n ight and early morning.

Results obtained at this elevated site showed the d ominating effect of regional secondary sulfate sources. Mobile or other secondary OC sourc es, secondary organics and vegetative burning sources are also important.

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

Sulfate/ Coal Nitrate Mobile Biomass Burning Industrial Crustal
and Salt Miscell.

Reference Data Period

(Analysis Method) Total

Mass (ug/m 3 )

(% 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 a n 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 gra vel 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/m 3 )

(% 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 f orested 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 withi n 10-15 km of the site and a quarry charcoal plant at 0-20 km. Results obtained at this rural si te showed the dominating effects of regional secondary sulfate sources followed by “mobile sourc es” 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/m 3 )

(% 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 a nd agricultural area with a major highway withinAppendix B: Source Apportionment Methods Page B-13

10 km. Various types of agriculture surround the si te. Immediately adjacent to the south are hay
fields. Some of the farms within 10 miles of the s ite have small hog operations, providing some
local sources of ammonia. There are also two comme rcial chicken barns near the site. Nearly all of the former gravel roads in the area are, at a minim um, chip-sealed with asphalt. The terrain is on a level ridge-top area adjacent to forested valley/ri dge terrain within the park.

This is one of the rare sites included in this summ ary where the regional secondary sulfate was not identified as the dominant source type. How ever (as with the Acadia results), several aspects of the Mammoth Cave model results appear il logical and indicate problems with the initial source interpretations, modeling procedures or inpu t data. For example, the largest modeled source, interpreted as “mobile sources/ secondary OC”, is c omposed of less than 5% OC, and the reconstructed source mass totals only 10% of the ap portioned source mass. The source’s sulfur:
sulfate ratio – which should be 1:3 -- is an irrati onal 40:1. Using sulfur, rather than sulfate ion fo r defining the source composition would increase its ammonium sulfate fraction from 0.01% to 60% (compared to 4% OC). This source also correlates s trongly with the secondary sulfate source, especially after 1994, where the inter-source corre lation (R 2 ) is 0.91. All of the above suggest major problems with the input measurement data, mod eling procedures or source interpretation.

Table B-12. Great Smoky Mountains National Park, T N

Sulfate/
Coal Nitrate Mobile Biomass Burning Industrial Crustal and
Salt Miscell.

Reference Data

Period (Analysis Method) Total

Mass (ug/m 3 )

(% 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 re sidential areas, and an aluminum plant. There are several local roads in the vicinity of the site. Si milar to Mammoth Cave, the largest source category is “mobile sources/secondary organics.” As with th e Mammoth Cave and Acadia results there are inconsistencies with the source profiles and mass a pportionment, 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/m 3 )

(% 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 loc al paved and unpaved roads. There are several large power plants within 60 miles. Sulfate/Coal a nd 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/m 3 )

(% 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 2 30 m, on the campus of Garinger High School in Charlette, NC. The area surrounding the s chool is primarily residential, but contains some
commercial land uses that would be associated with densely populated residential areas (convenience stores, restaurants, and other small b usinesses) near intersections along the main thoroughfares. The area also contains some light in dustrial 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/m 3 )

(% 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. Sulfat e 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/m 3 )

(% 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, locat ed 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 dete ction limit species. However analyses were also conducted for a number of specific PAH compounds, p roviding additional detail to the input data.

On average secondary sulfate sources contribute ove r 50% to the summertime mass and the authors identify the Ohio River Valley as the prima ry source region for both sites. Soil sourceAppendix 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 a t Potsdam, while at Stockton, the smelter source area was further east in Ontario trailing into west ern Quebec. For Potsdam, the main potential source areas for the zinc smelter were in eastern Q uebec. 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 t rajectories such as the facilities at Flin Flon and Thompson, Manitoba. A nickel smelter source was als o obtained at Potsdam. The results showed this source most likely included the smelters at No randa, 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/m 3 )

(% 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 o f Toronto in Canada’s largest urban area and just off the Northwest corner of the MANE-VU re gion. 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, su ccinate, etc.). These additional species
allowed for more detailed source resolution than fo r many of the other sites summarized above. PMF and UNMIX modeling were initially conducted ind ependently 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 excep tion that the PMF results divided a total Motor Vehicle influence into 2 components (MV exhaust + r oad dust, and MV exhaust + road salt), while
the UNMIX results broke a similar total MV influenc e into Diesel MV, Gasoline MV and Road Dust. Both models also identified 2 “secondary sul fate” sources: one fully neutralized (NH 4 ) 2 SO 4 and one acidic (approximately NH 4 HSO 4 ) which also included a large fraction of several o rganic acids and other secondary organic matter (consisten t 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 NH 4 NO 3 was identified and of similar mass magnitude to th e combined
sulfate + SOA sources.

The visibility impacts of the sources were also est imated by applying IMPROVE formulae to the individual source species and adding hygrosc opic growth functions for the (NH 4 ) 2 SO 4 , NH 4 HSO 4 and NH 4 NO 3 fractions, using nearby Toronto airport (hourly) R H 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/m 3 )

(% 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 includ e only major mass-contributing species, selected crustal elements, and trace elements (K and Sr). T he objective was to develop a chemical fingerprint for the large Quebec forest fire impact s centered on July 2002, while also accounting for other regional source influences. This required re gionally consistent source compositions (the same for all sites in each network), but also yielded va riable source contributions for each site and sampl e date. Results were surprisingly consistent across the MANE-VU sites in the two different networks, and included a large regional sulfate source, a woo d smoke source, a (primarily local) “mixed urban” source and a crustal source. The 7/7/02 Que bec 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 w as detected on the preceding sample day. Averaged over the 2-month summer period, however, t he regional sulfate source predominated, but both the wood smoke and crustal sources were propor tionately 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 MA NE-VU IMPROVE sites, Summer 2002Appendix 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 objec tive 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 norma lized daily source contributions vs. reconstructed extinction (calculated by IMPROVE for mulae), stratified by month (for application of monthly mean f(RH) factors).

Figure B-3. Estimated Visibility Impacts from Batt elle PMF sources on IMPROVE sites on Best 20% (top) and Worst 20% (bott om) 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 vehicl e 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 SO 2 -emitting sources than are the MANE-VU and VISTAS r egions.



### B.2. Ensemble Trajectory Evaluation of Multivariate Mode l Results

B.2.1. Background

Trajectory models employ meteorological data to cal culate paths of airmass motion over space and time. Commonly employed models include NO AA HY-SPLIT, CAPITA Monte Carlo, and ATAD, which can in turn be driven by gridded wi nd-field data such as from the ETA, FNL, or MM5 meteorological models, or for the older ATAD mo del, driven by rawinsonde measurement data. Run in a backward direction from a given star ting location and time, they can be considered as a form of “meteorological receptor model” and are c ommonly employed to evaluate potential origins and transport routes of pollution events o bserved at ambient monitoring sites.

Compared to more sophisticated photochemical grid m odels, computational requirements for trajectory calculations are modest, facilitatin g the generation of long-term (multi-year) trajecto ry databases. Trajectory databases developed for ambi ent monitoring sites can then be employed in various “ensemble trajectory techniques” to evaluat e 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 comm only employed ensemble trajectory techniques include: Cluster Analysis, Quantitative Transport Bias Analysis, Residence Time Analysis, and Potential Source Contribution Functio n. Generally, the above ensemble techniques (and their many variations) involve sorting (screening and/or aggregating) traj ectories as a function of resultant receptor concentrations, or sorting re ceptor 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 ens emble 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 & UNMI X) from the Battelle “Phase I” analysis of
IMPROVE and CASTNet data 1 from 16 eastern US sites. An initial set of CATT t ools 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 sinc e 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, C ATT allows combined aerosol/trajectory analyses of all speciated aerosol data from the IMP ROVE and EPA STN networks for their respective periods of record. The aerosol data acc ess is directly linked to VIEWS 2 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/viewsAppendix 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,
2005 3 and in the more recent CATT Illustrated Instruction Manual 4 . The following section presents example applications of several of the CATT tools f or 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 predom inate. CATT is a tool designed to facilitate these analyses. One of the basic CATT tools allows plotting of Multi-Site, Single Day Trajectories 5
& Aerosol Species Concentrations for all IMPROVE an d 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 assoc iated with low and high concentrations of the selected species. The Gao et al. (2004) PMF modeli ng of 2001-2003 IMPROVE data from Underhill, VT and the Poirot & Husar (2004) multi-s ite UNMIX modeling of July and August 2002
data from all MANE-VU IMPROVE and STN sites identif ied 3 regional source influences which were interpreted similarly by the two modeling grou ps, 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 m aximum influence in both model runs) included “windblown dust” (7/4/02), “wood smoke” (7/7/02) an d “secondary sulfates” (8/12/02). The 7/4/02
dust and 7/7/02 wood smoke have also been identifie d 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-weig hting to reflect highest concentrations of Al (7/4/02), OC (7/7/02) and SO4 (8/12/02) respectivel y. 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 trajector y calculations and with clearly identified “tracers of opportunity.”



### B.2.2.1. Dust

The 7/4/02 “Sahara Dust” event illustrated by highe st 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 conce ntrations are higher in, and trajectories come from, the West and Southwest of MANE-VU – in a pparent conflict with the interpretation of a Sahara dust origin far to the Southeast. While v isibility and fine mass concentrations in the MANE-VU region are typically less influenced by soi l dust than any other RPO region, the highest MANE-VU dust concentrations are typically associate d with Sahara dust events, and their transport route into the Northeast typically comes from a sou thwesterly 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/AWMA97 CATT.pdf

4 http://capita.wustl.edu/capita/capitareports/0411CATTReport/Tut orial/CATT_Manual.htm

5 http://webapps.datafed.net/dvoy_services/datafed.aspx?page=CAT T/CATT_RSAppendix B: Source Apportionment Methods Page B-20

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

concurrent NAAPS 6 model calculations of global dust emissions, trans port and effects on aerosol optical depth.

6 http://www.nrlmry.navy.mil/aerosol/Docs/globaer_model.htm l 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 d ata and Poirot & Husar (2004) Multisite UNMIX for a ll
MANE-VU IMPROVE and STN Sites.

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

A number of the other mathematical model results su mmarized in Section A.2.1 also identify “crustal dust” sources at Northeastern sit es, 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, foll owed by northwesterly transport from the Gulf

Coast across the central US into the Northeast. Fo r 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 d ocumented by Perry et al. (1997). The same date also saw the highest “crustal” source contribu tion 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 mo deled soil results of Kim and Hopke (2004) at Brigantine, NJ and Gao et al (2004) at Underhill, V T.

A clear distinction should be drawn between fine “s oil” as calculated by the IMPROVE formula (2.2[Al]+2.49[Si]+1.63[Ca] +2.42[Fe]+1.94[T i] ) 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 ox ides of the elements used in the equation (all of which are assumed to result predominantly from crus tal sources) plus additional minor adjustments to account for oxides of K, Mg, Na and water and ca rbonate (which also have non-soil sources). Receptor modeled soil sources typically include mos t 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 c alcarious soils, as well as by more distant sources like Sahara dust, which tends to be relatively depl eted in Ca and enriched in Al. The above “crustal” elements are also often partially distrib uted 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 proces ses, kicked up by motor vehicle traffic, etc. In addition, modeled soil sources are frequently “aged ” or “contaminated” by other pollutants like

SO 4 , NO 3 and/or OC, all of which can react with or “coat” f ine soil particles during transport, or which may be present in the soil emission source, a s might be anticipated for reentrained road dust, enriched by deposited motor vehicle exhaust, oil le aks, 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, su lfate often accounts for up to half of the mass of these soil modeled soil sources, which is not surpr ising considering the lengthy transport routes. However, even with this “anthropogenic enhancement” fine soil typically accounts for < 5% of

PM 2.5 mass, and an even smaller fraction of extinction o n 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 especi ally 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, Dol ly Sods WV and Charlotte, NC. Like the crustal sources, sea salt can be a useful “tracer o f opportunity,” as its origin should be unambiguous (from the sea). Figure B-8 illustrates application s 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-l eft: Upwind averageAppendix B: Source Apportionment Methods Page B-23

trajectory methods to identify the most common orig ins of the Brigantine sea salt source from UNMIX modeling by Poirot and Wishinski (2002). Thi s 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 Son g et al. (2001), Lee et al. (2003) and Kim et al. (2004). See Appendix T in Coutant et al. (2002) fo r 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 % deviati ons 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 eve ryday 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 diffe rences in the questions they pose. Incremental probability asks: “if the source contribution was h igh, where did the air previously reside?” The upwind average asks: “if the air was previously ove r this location, how high was the source contribution at the downwind receptor?” Incrementa l probability is influenced by the frequency of contribution, while the upwind average emphasizes a reas with the greatest potential to contribute. In the above plot, the very high upwind average off th e coast of the Carolinas coincides with the area
where the Gulf Stream comes closest to the east coa st and where sea salt emissions might be anticipated to be highest. These analyses were cond ucted 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 IM PROVE site.

Like the crustal sources, “sea salt” sources identi fied by receptor models often reflect an “aged” composition. Over time, chlorine is lost fr om airborne NaCl and replaced with “excess”

NO 3 and SO 4 . The Brigantine PMF results from Lee et al. (2003 ) illustrate this most clearly, as the
modelers identified 2 sea salt sources identified a s “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, NO 3 and Na but no Cl. The aged source contributed substantially more mass, averaging 8 times higher t han the fresh source. Results from all the other Brigantine receptor modeling analyses found a singl e sea salt source with relatively aged composition.

In all of the Brigantine (and Acadia) modeling stud ies, 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 a t coastal sites, which are in turn most likely to b e 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 condit ion was met. It can be noted in the top half of Figure B-9 that these tend to be coastal or near-co astal sites. For the bottom half of Figure B-9, th e “messy” individual trajectories, which for such a l arge query tend to pile up on top of each other, were further processed by a gridder, which counts a nd displays the sums of all trajectory endpoints within prespecified grid cells (in this case 1x1 de gree 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 te nds to correctly identify the “known” source region(s) by “triangulation,” even though that regi on 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 occ ur at highest concentrations when anthropogenic pollut ants (from within the US) are lowest. Thus itsAppendix 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 curren tly 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 mas s 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 site s and dates with Na > 1.5 ug/m 3 (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 id entified 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 N YC, Toronto and Boston and none in 3 of 4 studies in Washington DC. This makes sense conside ring 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 vegetat ion types the MANE-VU region is not especially prone to forest fires, compared to other regions fu rther west or south. A notable exception to these generalizations occurred during the 7/7/02 Quebec f ires 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 da ta) are not able to distinguish between wood smoke due to residential wood burning and fore st 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) combin ed 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 Und erhill wood smoke source in comparison with the 7/02 fires and historical pattern of large Cana dian fires.

It may be noted that the incremental probability fi eld 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 Cana dian wild fire influences.

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

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

Gao et al. (2004) also identified a wood smoke sour ce in Underhill, VT, based on 2001-2003 data. It was similar in magnitude to the 1989-95 U nderhill 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 sh ould also be noted that Gao et al. (2004) intentionally excluded the 7/7/02 sample day from t heir model input, as the source was clearly known and the huge 1-day impact tended to produce i llogical PMF results. However, the chemical composition of Gao's PMF smoke source was very simi lar to the composition of the Quebec fire smoke identified by the multi-site UNMIX analysis i n 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 incr emental 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 probabilit y field for Brigantine (where the air most frequent ly Figure B-11. Incremental probability for Lye Brook wood smoke source (PMF modeling by Coutant et. Al. (2002 )

Figure B-12. Everyday probability, incremental pro bability 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 north ern 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 s ite for that event, exceeding 100 ug/m3. Also unlike the VT results, there’s no indication o f strong local wood burning source influences, and a much different regional origin – in the South eastern 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 bo th local New England region and more distant wild fires in eastern Quebec. Shenandoah also show s relatively local influence, as well as more distant effects from an area of the Southeast, simi lar 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 f ormation (which requires time & distance), and/or high wind speeds often associated with the spread o f 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 So utheast regions.

It should be cautioned that modeling and interpreti ng sources of primary and secondary carbonaceous sourc es 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, especia lly at rural sites there may well be large secondary organic inf luences that are predominantly biogenic and unrelated to motor v ehicle emissions. There may be misinterpretation or mixin g 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 smok e 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 AggregationsAppendix B: Source Apportionment Methods Page B-28

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

Figure B-15 is adapted from an illustration present ed 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,” follow ing her GIS development and expansion of the method to multiple sites as described in Kenski (2004). Husar (2005) used OC data for th e 2001-2003 period for a large subset of 35 widely distributed IMPROVE sites (top of Figure B-15), and then calculated the “Kenski metric” usin g only values greater than high (98%) and lower (95%, 90%, 80%) percentil es of the data. He also showed the more traditional “upwind average” u sing all the OC data from these sites (Figure B-16). At the highest per centiles, locations of well documented large forest fires are evident. Bu t such fires are very episodic in their emission patterns and are not lik ely to repeat in the same locations within a relatively short multi-year time period. At the

80 th percentile (highest 20% of OC measurements at each site), and for the average based on all OC data, the large fire ef fects are much less evident, and the Southeastern US shows up as a much more chronic OC source area. Although there are fires in the South east, they don’t burn continuously, and a gaseous biogenic emissions sour ce category is perhaps a more logical interpretation for the “sour ce.” This upwind average for all OC data from CATT is quite similar to the pattern displayed in Kenski, 2004 – who also noted a simila r pattern for CAMx modeled biogenic secondary organic aerosol as displ ayed in Figure B-17. Figure B-15. Multi-site upwind average for high percentile OC

Figure B-16. Multisite Upwind Average OC (from Hu sar, 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 sit es, 2001-2003.



### B.2.2.4. Nitrates

The Battelle receptor modeling (Coutant et al., 200 2) 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-sou rce solutions). But many of the UNMIX sources were quite similar to their PMF counterpart s, and nitrate data were included as input to the UNMIX model runs, and therefore apportioned among t he resulting sources (nitrate data were omitted from the Batelle PMF analyses). A generall y common feature of the Battelle UNMIX results for nitrate was that it tended to mostly br eak out into a single “nitrate source” at most modeled sites, and this “source,” assumed to be mos tly 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 ca tegory (such as motor vehicles, utilities or industrial sources) was also observed in a number o f the other modeling studies - for example in Underhill, Potsdam, Brigantine, New York and Toront o. Fractional mass contributions from these nitrate sources are in the range of 5 to 10% at MAN E-VU rural sites, but can be substantially higher in urban areas. In Toronto, an “ammonium nitrate s ource” identified by Lee et al. (2003) PMF modeling and Poirot and Brook (UNMIX) contributed 3 5% 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” re gion, as well as local urban influences.

Incremental probability plots for the Battelle UNMI X nitrate sources are displayed in Figure B-18, and suggest a strong influence from Midwester n 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 contribu tion 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 th e winter season, when concentrations are highest. Du ring summer, the lower Mississippi valley and east coast urban corridor appear more important. P otentially there is some formation of sodium nitrate in coastal areas and/or competition from ac idic sulfates in the Midwest and Southeast during Figure B-18. Incremental probability fields for hi gh 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 becom e highly concentrated in the “residual” during the refining process. Consequently, “residual oil burning” (rather than distillate oil burning) is th e most likely source of these trace (and “tracer”) el ements. It may also be noted that an oil source is most frequently identified and /or tends to have hi ghest 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 Briganti ne Oil source.

Figure B-21. Incremental Probability for Oil sourc es 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 Se ction 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 th e 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 aeroso ls of mixed origins raise interesting questions of “causality” (and resultant light extinction charact eristics), 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 thei r 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 ar e typically composed primarily of sulfates and account for high fractions of the total sulfate at the receptors.

Sulfate characteristics of the large sulfate source s 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 percenta ges of the sources composed of sulfates.

Seventy to ninety percent of the sulfate at these s ites is contributed by these large sources, which are in turn composed of fifty to ninety perce nt 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 NH 4 HSO 4 and H 2 SO 4 , 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 tha n 0.83, indicating a sulfate source composition Figure B-22. Sulfate characteristics of Battelle s econdary sulfate/coal sources.

0 10 20 30 40 50 60 70 80 90 100

ACAD BRIG DOSO GRSM JARI LYBR MACA SHEN WASHPercent Sulfate at Site or in Source

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

more acidic than ammonium bisulfate. At other sites such as Brigantine, Lye Brook and Washington DC, with SO 4 :mass ratios of 0.55 to 0.60, the sulfate sources m ust contain species in
addition to sulfate and ammonium in order to accoun t for their mass contributions, and there is often a significant organic matter component – in the ran ge of 10 to 15 % in these “sulfate” or “coal”

sources.

A number of the studies summarized in section B.1 i dentified 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 S e is a primary aerosol (emitted in particle phase o r condensing shortly after emission) while SO 4 is primarily a secondary species (formed at varyin g rates in the atmosphere). Consequently, the S:Se r atio at downwind receptors varies considerably as a function of the efficiency of secondary aerosol f ormation 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 200 0 through 2003 (about 50,000 samples) and shows a poor correlation (R 2 = 0.21). However, when the incremental probabiliti es for these same 2000-2003 S and Se data are calculated in CATT, the ir upwind locations are highly correlated (R 2 = 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 (t op 10%) SO4 (left) and Se (right), 2000-2003Appendix 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 o r 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, V T 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 (1991-199 9). Plotted points are colored to summer and winter measurements, showing generally much more ef ficient 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 edge s” in the scatter plots, which UNMIX specifically seeks and quantifies). These “sources” are then in terpreted as representing the primary aerosol component (minimal secondary transformation) and se condary 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 diff erent proportions on different sample days.

In the Toronto PMF and UNMIX modeling, additional s pecies were measured, including (good quality) NH4 and several organic acids. The P MF modeling at that site identified 3 coalrelated source components: primary, secondary neutr al ((NH 4 ) 2 SO 4 ), and secondary acidic (NH 4 HSO 4 ). The acidic component also included a significan t OC content (while the other 2 components did not). The authors suggested this mi ght be indicative of the “acid-catalyzed secondary organic aerosol formation” mechanism, ide ntified 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 loc ation (of high SO 2 emissions, but not high VOC emissions), and from different locations than o ther nitrate and motor vehicle source
influences. Figure B-25. Illustration of S:Se ratios in "Prima ry 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 SO 2 emissions.

As results from additional monitoring sites and rec eptor 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 i n coal-related SO 2 emissions would have substantial benefits for improved visibility and re duced 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 "Second ary 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 BrigantineAppendix 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 (utili ty) emissions data from the 1998 EPA EGRID inventory. The top panel shows SO 2 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 show s incremental probabilities for “oil” sources, identified by separate PMF and UNMIX model runs (an d modeling groups) at the Underhill, VT and Brigantine, NJ sites respectively. These are a veraged 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 i s 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

SO 4 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 DeciviewsAppendix B: Source Apportionment Methods Page B-38

Figure B-30 and Figure B-31 are limited to 2000-200 3 data from the MANE-VU Class 1 areas only, and show (Figure B-30) incremental prob abilities 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 contou r lines are for the indicated individual sites, probability levels and/or sulfate concentrations, a nd the grey shaded areas depict linear averages aggregated for the five MANE-VU Class 1 sites.

Figure B-30. Incremental Probabilities for High SO 4 at MANE-VU Class 1 Sites, 1999-2002

Figure B-31. Average Upwind SO4 at MANE-VU Class 1 Sites, 1999-2002Appendix B: Source Apportionment Methods Page B-39

While the patterns appear “similar” in this case, t he 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 quantitati ve 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 pr obabilities and upwind averages in Figure B-30 and Figure B-31 disaggregated into stat e 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 (rig ht) for these average ranks. It may be noted that the ranking for the two different metrics show larg est differences for the physically smallest and nearby (and/or downwind) states in the MANE-VU regi on (MD, NJ, DE, DC, CT, MA, RI, VT & NH). One reason for this is that the state aggrega tions were summed for incremental probabilities (which reflects both concentration and frequency) a nd averaged for the upwind averages (which do not reflect frequency) and this causes the metrics to differ most for the smallest states. In additio n, individual grid locations within these small, nearb y 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 ev eryday basis.

It may be noted that the top 10 states are distribu ted among the MANE-VU, Midwest and VISTAS RPO planning regions, and that coordinated i nter-regional strategies will be needed to assure future progress toward the national visibili ty goals. Figure B-32. Ranked State Contributions for Increm ental Probability and Upwind

Average Sulfate for MANE-VU Class 1 Receptors.Appendix B: Source Apportionment Methods Page B-40



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