Airborne particulate matter (PM) air pollution is presently regulated by the National

Ambient Air Quality Standards (NAAQS)

using gravimetric mass as the particle metric

to assess air quality. However, an enormous

number of different chemical species are associated with the various types of ambient particles, depending upon their source origins

(e.g., Cooper and Watson 1980). For example, primary particles emitted from coal combustion are characteristically highly enriched

with arsenic and selenium, whereas residual

oil combustion particles are more enriched in

nickel and vanadium, and soil particles are

especially enriched in the crustal elements

(e.g., silicon, aluminum). In addition, secondary components of particles (e.g., sulfates,

nitrates, and organic compounds) are formed

in the atmosphere from gaseous pollutant

emissions. These secondary components can

either condense on primary particles or form

secondary particles that can then collide and

coagulate with primary particles. Individual

particles in an urban airshed can contain both

primary and secondary components, and the

composition of ambient aerosols have been

found to reflect source PM emission characteristics differences over space (e.g., between cities)

and time (e.g., across seasons) (e.g., Spengler

and Thurston 1983). Because the composition

of particle types varies greatly, it is probable

that some types of particles are more toxic than

others. Thus, treating all particles that contribute to the mass concentration equally in the

regulatory process may lead to inefficient protection of public health. A potentially more

effective regulatory approach would be to

address the individual types of particles independently, focusing control efforts on the most

toxic categories. However, because toxicities of

individual source components are not yet certain, and because virtually all published PM

health effects studies to date have used PM

mass (in various size categories) as the particle

pollution index, the current NAAQS for airborne PM use airborne particle mass as the

indicator for making air quality compliance

determinations. Equal treatment of all particles

that contribute to mass, irrespective of composition, may be leading to less-optimal control

strategies to avoid the adverse human health

effects of PM, potentially causing the present

PM ambient standard to be less protective of

health in some areas of the nation than in

others. There is a need for epidemiologic and

1768 VOLUME 113 | NUMBER 12 | December 2005 • Environmental Health Perspectives

Research

Address correspondence to G.D. Thurston, New York

University School of Medicine, Nelson Institute of

Environmental Medicine, 57 Old Forge Rd., Tuxedo

Park, NY 10987 USA. Telephone: (845) 731-3564.

Fax: (845) 351-5472. E-mail: thurston@env.med.

nyu.edu

We thank the individual researchers who participated in this workshop, often on their own time and

resources. We also thank Columbia University’s Arden

House Conference Center in Harriman, New York,

for hosting the May 2003 workshop that led to this

manuscript.

The workshop was organized under the auspices of

the participating U.S. Environmental Protection

Agency (EPA) PM Health Effects Research Centers

(grant R827351 at New York University, R827351 at

the University of Washington, R827353 at Harvard

University, and R927354 at the University of

Rochester). Support for the organization and administration of the workshop was also provided by the New

York State Energy Research and Development

Authority (grant 375-34215). Additional support was

provided by the New York University–National

Institute of Environmental Health Sciences Center

grant (ES00260).

The information in this document has been subjected

to review by the U.S. EPA National Health and

Environmental Effects Research Laboratory and

approved for publication. Approval does not signify

that the contents reflect the views of the agency, nor

does mention of trade names or commercial products

constitute endorsement or recommendation for use. 

The authors declare they have no competing

financial interests.

Received 31 January 2005; accepted 1 September

2005.

Workgroup Report: Workshop on Source Apportionment of Particulate

Matter Health Effects—Intercomparison of Results and Implications

George D. Thurston,1 Kazuhiko Ito,1 Therese Mar,2 William F. Christensen,3 Delbert J. Eatough,4 Ronald C. Henry,5

Eugene Kim,6 Francine Laden,7 Ramona Lall,1 Timothy V. Larson,8 Hao Liu,9 Lucas Neas,10 Joseph Pinto,11

Matthias Stölzel,12 Helen Suh,7 and Philip K. Hopke6

1Nelson Institute of Environmental Medicine, New York University School of Medicine, Tuxedo Park, New York, USA; 2Department of

Environmental Health, University of Washington, Seattle, Washington, USA; 3Department of Statistics, and 4Department of Chemistry

and Biochemistry, Brigham Young University, Provo, Utah, USA; 5Department of Civil and Environmental Engineering, Southern

California University, Los Angeles, California, USA; 6Center for Air Resources Engineering and Science, Clarkson University, Potsdam,

New York, USA; 7Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts USA; 8Department of

Civil and Environmental Engineering, and 9Department of Biostatistics, University of Washington, Seattle, Washington, USA; 10National

Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Chapel Hill, North Carolina, USA;

11National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA; 12Institute of Epidemiology, Focus Network Aerosols and Health, National Research Center for Environment and Health (GSF),

Neuherberg, Germany

Although the association between exposure to ambient fine particulate matter with aerodynamic

diameter < 2.5 μm (PM2.5) and human mortality is well established, the most responsible particle

types/sources are not yet certain. In May 2003, the U.S. Environmental Protection Agency’s

Particulate Matter Centers Program sponsored the Workshop on the Source Apportionment of

PM Health Effects. The goal was to evaluate the consistency of the various source apportionment

methods in assessing source contributions to daily PM2.5 mass–mortality associations. Seven

research institutions, using varying methods, participated in the estimation of source apportionments of PM2.5 mass samples collected in Washington, DC, and Phoenix, Arizona, USA.

Apportionments were evaluated for their respective associations with mortality using Poisson

regressions, allowing a comparative assessment of the extent to which variations in the apportionments contributed to variability in the source-specific mortality results. The various research

groups generally identified the same major source types, each with similar elemental makeups.

Intergroup correlation analyses indicated that soil-, sulfate-, residual oil-, and salt-associated mass

were most unambiguously identified by various methods, whereas vegetative burning and traffic

were less consistent. Aggregate source-specific mortality relative risk (RR) estimate confidence

intervals overlapped each other, but the sulfate-related PM2.5 component was most consistently

significant across analyses in these cities. Analyses indicated that source types were a significant

predictor of RR, whereas apportionment group differences were not. Variations in the source

apportionments added only some 15% to the mortality regression uncertainties. These results provide supportive evidence that existing PM2.5 source apportionment methods can be used to derive

reliable insights into the source components that contribute to PM2.5 health effects. Key words:

fine particles, health effects, mortality, particulate matter, source apportionment, sulfate, timeseries, uncertainty. Environ Health Perspect 113:1768–1774 (2005). doi:10.1289/ehp.7989

available via http://dx.doi.org/ [Online 1 September 2005]
toxicologic evaluation of the extent to which

the toxicity of ambient PM mass varies by

particle type and source.

Because source composition and/or physical properties of particles vary between different source categories, the mass can be

statistically apportioned into contributions

from various source categories, opening the

possibility of evaluating PM component

effects using epidemiologic methods presently

used on the PM mass. As discussed by Hopke

et al. (in press), this area of research, called

receptor modeling, has been active for over

3 decades. A number of accepted methods are

being used to apportion the total mass into

source categories, and these source apportionment methods can now be used as inputs to

epidemiologic models of the human health

effects of air pollution. However, to date only

a small number of published efforts have

related source-apportioned PM impacts to

human health effects (e.g., Laden et al. 2000;

Mar et al. 2000; Ozkaynak and Thurston

1987). The effect of the imputation of these

apportionments on the ability of epidemiologic methods to evaluate the health effects

associated with various PM components is

uncertain. Because a number of methods are

used to determine source contributions to PM

mass impacts, and application of these methods varies among researchers, their application,

although providing new insights, can also be

expected to introduce added uncertainty into

the derivation of estimates of PM toxicity [e.g.,

to the estimation of mortality relative risks

(RRs) per amount of mass of fine particulate

matter < 2.5 μm in aerodynamic diameter

(PM2.5)]. The scientific and regulatory community is uncertain whether meaningful and

reliable source apportionments of PM2.5

health effects are possible with today’s data

and methods. A workshop was therefore organized by a consortium of U.S. Environmental

Protection Agency (EPA) PM centers to assess

the extent to which variations in current

source apportionment methods and their

application may affect the ability of epidemiologic studies to discern PM health effects on a

source-specific basis.

On 29–30 May 2003, the U.S. EPA PM

centers sponsored the Workshop on the

Source Apportionment of PM Health Effects,

hosted by the New York University (NYU)

PM Research Center. The specific goal of this

workshop was to evaluate the variability of the

various PM source apportionment approaches

in assessing PM source contributions to ambient PM2.5 concentrations in real-world data

sets and to then assess the influence of this

variability on the ability of statistical timeseries analyses to discern which source categories contribute significantly to daily PM2.5

mass–mortality associations. No new health or

environmental data were generated by participants during this effort. Instead, the same preexisting reference PM mass and constituent

data sets from two cities (Washington, DC,

and Phoenix, AZ) were sent to various leading

source apportionment research groups in

advance of the workshop (in December 2002),

and each group individually analyzed the same

data sets for daily source PM2.5 contributions.

These various daily PM2.5 mass source apportionments were then independently submitted

before the workshop (in April 2003), and each

was individually evaluated for their respective

associations with daily mortality in each city in

a consistent manner across the various apportionment research groups/methods. The

PM–mortality health effects time-series modeling evaluations were conducted for the

Washington and Phoenix data sets by

researchers at the NYU and University of

Washington U.S. EPA PM Research Centers,

respectively. Washington and Phoenix were

selected for this workshop analysis because the

PM data available from these cities in past

years were collected and analyzed for trace

constituents in a manner similar to that used

by the U.S. EPA in the nationwide Speciation

Trend Network (STN), so as to come to

workshop conclusions relevant to that developing STN data set. In addition, the consideration of these two very different cities with

differing sources and weather provided a

broader test of the consistency of these methods than would a single city or two cities from

the same region of the country. Keeping the

health effects model consistent across the various source apportionment researchers and

methods allowed a separate discernment of the

extent to which variability in the source

apportionment step contributed to variability

in the ultimate health effects analyses results.

The goals of the workshop were to bring

together key researchers to assess the reliability

of source apportionment–health effects methods by analyzing daily mortality with existing

PM2.5 data sets similar to those now being collected by the U.S. EPA Speciation Network

and to identify key future research needs for

source apportionment health effects evaluation.

As noted in Table 1, research groups from

seven institutions, using various source apportionment approaches, participated in this

workshop. Most of the groups were affiliated

with one of the five U.S. EPA PM centers.

Materials and Methods

Particulate matter data sets. The two PM2.5

mass and composition data sets employed

in the source apportionments were selected

based on their ready availability for analysis,

the similar availability of a compatible daily

mortality record for health effects analysis, and

the fact that their PM2.5 composition analyses

were similar in many ways to those characteristics available to researchers from the new U.S.

EPA PM2.5 STN. In this way, analyses of existing data sets could be accomplished quickly

and would provide information relevant to

future analyses that might be conducted with

the rapidly expanding U.S. EPA STN database. Brief descriptions of these databases are

provided below, and more detailed descriptions are provided in the companion workshop

papers (Hopke et al., in press; Ito et al., in

press; Mar et al., in press).

In Phoenix, daily integrated 24-hr samples were collected with a dual fine-particle

sequential sampler (URG Corp., Chapel Hill,

NC, USA) on 37-mm diameter Teflon and

quartz filter media for fine particle mass and

species measurements. A total of 981 samples

were collected from March 1995 through

June 1998. Each sample was characterized by

the measured concentrations of the following

46 chemical elements: sodium, magnesium,

aluminum, silicon, phosphorus, sulfur, chlorine potassium, calcium, scandium, titanium,

vanadium, chromium, manganese, iron,

cobalt, nickel copper, zinc, gallium, germanium, arsenic, selenium, bromine, rubidium,

strontium, yttrium, zirconium, molybdenum,

rhodium, palladium, silver, cadmium, tin,

antimony, tellurium, iodine, cesium, barium,

lanthanum, tungsten, gold, mercury, lead,

organic carbon, and elemental carbon (EC).

The analytical uncertainty estimates associated with each measured concentration and

the detection limits for both instruments were

also included.

In Washington the PM2.5 samples were collected on Wednesdays and Saturdays at the

Interagency Monitoring of Protected Visual

Environments (IMPROVE) monitoring site

located in downtown Washington. A total of

718 samples were collected between 31 August

1988 and 31 December 1997. Integrated

24-hr PM2.5 samples were collected on Teflon,

Source apportionment of particle health effects

Environmental Health Perspectives • VOLUME 113 | NUMBER 12 | December 2005 1769

Table 1. Summary of workshop goals and participating research institutions.

Workshop goals Participating research institutions

To bring together key researchers to assess the reliability Brigham Young University (BYU)

of source apportionment–health effects methods by Clarkson University (CU)

analyzing daily mortality with existing PM2.5 data sets Harvard University (HU)

similar to those now being collected by the U.S. EPA New York University (NYU)

Specialization Network. University of Rochester and GSF (UR/GSF)

To identify key future research needs for source apportionment– University of Southern California (USC)

health effects evaluation. University of Washington (UW)

GSF, German National Research Center for Environment and Health.
nylon, and quartz filters. The Teflon filters

were used for mass concentrations and analyzed

via particle-induced X-ray emission for the elements Na, Mg, Al, Si, P, S, Cl, K, Ca, Sc, Ti,

V, Cr, Mn; via X-ray fluorescence for elements

Fe, Co, Ni, Cu, Zn, Ga, Ge, As, Se, Br, Rb, Sr,

Y, Zr, Mo, Rh, Pd, Ag, Cd, Sn, Sb, Te, I, Cs,

Ba, La, W, Au, Hg, and Pb; and via proton

elastic scattering analysis for elemental hydrogen concentration. The nylon filter was analyzed by ion chromatography for sulfate,

nitrate, and chloride. The quartz filters were

analyzed by the IMPROVE method for temperature-resolved organic and EC fractions

(IMPROVE/TOR).

Daily mortality data sets. Washington

death records were extracted from the National

Center for Health Statistics database for the

period from 31 August 1988 to 31 December

1997, and daily counts were aggregated for the

District of Columbia and surrounding six

areas: Montgomery County, Maryland; Prince

George’s County, Maryland; Fairfax County,

Virginia; and Alexandria, Fairfax, and Falls

Church, Virginia. Three categories of deaths

were analyzed: total nonaccidental; cardiovascular; and cardiovascular plus respiratory.

Phoenix mortality data from 1995 to 1997

were obtained from the Arizona Center for

Health Statistics. In this analysis, we included

only mortality counts for residents ≥ 65 years

of age from ZIP code regions thought to be

most represented by the U.S. EPA monitoring

platform (Mar et al. 2000). We evaluated total

nonaccidental mortality [International

Classification of Diseases, 9th Revision (ICD-9)

codes < 800.00; World Health Organization

1978] and cardiovascular mortality (ICD-9

codes 390.00-448.99) from 9 February 1995

to 31 December 1997. From 1995 to 1997

there were a total of 9,081 nonaccidental

deaths and 4,109 cardiovascular deaths.

Source apportionment modeling. The

above-described PM2.5 mass and composition

data sets were provided to each participating

research group in December 2002 for independent analysis, using each group’s preferred

source apportionment technique(s). To allow

a consistent intercomparison of results across

research groups, participants were requested to

submit results in a standardized format and

with a list of items describing the details of

source apportionment analysis (e.g., type and

extent of rotation, treatment of outliers, criteria used to include species in the analysis). Of

the 11 potential participants to whom the data

were sent, eight participant/teams from seven

institutions submitted source apportionment

results by the required deadline (April, 2003).

As described in more detail in the companion paper by Hopke and collaborators (in

press), the fundamental principle of source

apportionment (receptor) modeling is that

mass conservation can be assumed, and a mass

balance analysis can be used to identify and

apportion sources of airborne PM in the

atmosphere. If the number and nature of the

sources affecting the air-monitoring station are

known, then the only unknown is the mass

contribution of each source to each sample, sjk.

These values can be estimated using regression. This approach was first independently

suggested by Winchester and Nifong (1971)

and Miller et al. (1972) and is now called the

chemical mass balance (CMB) model (Chow

and Watson 2002, Cooper and Watson 1980;

Cooper et al. 1984). In general, CMB models

assume that the recorded aerosol mass (Mk ) in

micrograms per cubic meter is due to the sum

of impacts by individual sources (Sjk ):

[1]

where k = 1,2, ....m days; j = 1,2, ....p sources,

and the total concentration of aerosol property Cik (i.e., element i’s ambient concentration on day k at a site) is

[2]

where fij = the mass fraction of property i in

emissions from source j.

Thus, if the source profiles (fij) are

known, the source contributions (Sjk) can be

determined from the linear regression of the

Cik on the fij.

However, if (as is more usually the case),

the source emission “signatures” are not

known exactly, but only qualitatively (e.g.,

that vanadium is enriched in residual oil combustion particles, but the exact percentage is

not known), then factor analysis (FA) methods are applied to identify and quantify the

sources and their impacts. The FA approach

to source apportionment assumes that the

total concentration of each observable (element) is made up of the sum of contributions

from each of p pollution source components:

[3]

where

[4]

(the standardized z-score of element i’s kth

observation), and Pjk = the jth factor component’s value on the kth day; Wik = the scoring

coefficient matrix of the components; and si =

the standard deviation of element i.

With respect to CMB models, the Pjk are

equivalent to the Sjk source impacts, and the

Wij are equivalent to the Fij source profiles.

However, the Pjk and Wij are derived by the

FA from the correlation matrix and are outputs of the FA (instead of inputs, as is the case

for CMB). Such FA approaches generally have

a major advantage, in that they can identify

and quantify nontraditional aerosols such as

secondary aerosols (formed in the atmosphere)

and can incorporate non-PM tracers such as

the gaseous pollutants. Such FA and principal

components analysis (PCA) models attempt to

simplify the description of a system by determining a minimum set of basis vectors that

span the data space to be interpreted. In other

words, a new set of variables is found as linear

combinations of the measured variables so that

the observed variations in the system can be

reproduced by a smaller number of these

causal factors. This approach has been widely

used in studies of airborne PM composition

data (Gao et al. 1994; Hopke et al. 1976;

Roscoe et al. 1982).

Traditional FA and PCA are useful for

identifying source components contributing to

the PM mass but do not directly provide an

apportionment in the form presented above.

However, the solutions can be manipulated

to provide such a quantitative solution. One

approach is specific rotation FA (Koutrakis

and Spengler 1987), which uses a targeted

Procrustes factor rotation. An alternative

approach, absolute principal-component

analysis (APCA) (Thurston and Spengler

1985), has also been used to produce quantitative apportionments. Two more-recent

approaches are Unmix (Henry and Kim 1999;

Kim and Henry 1999, 2000a, 2000b) and

positive matrix factorization (PMF) (Paatero

1997; 1999; Paatero et al. 2002). These and

similar multivariate techniques, described and

documented in more detail in Hopke et al. (in

press), have been applied by the different

research groups to achieve source apportionments of the Washington and Phoenix PM2.5

data sets (Table 2).

After all the estimated source-specific

impact assessments were submitted by workshop participants, the agreement across source

apportionment analyses was evaluated. This

was first evaluated by an intercomparison

of the various analyses’ respective mean

Z C C

s ik

ik i

i

= −

Z WP ik ij j

p

jk = ∑

=1

,

C Sf ik jk j

p

ij = ∑

=1

,

M S k jk j

p

= ∑

=1

,

Thurston et al.

1770 VOLUME 113 | NUMBER 12 | December 2005 • Environmental Health Perspectives

Table 2. Summary of the source apportionment

analyses performed by each participating group.

Research

institutions Phoenix, AZ Washington, DC

BYU Unmix Unmix, iterated,

confirmatory FA

CU PMF2 and expanded PMF2

model (ME)

HU Target rotated PCA Target rotated PCA

NYU PMF, APCA PMF, APCA, singleelemental multiple

regression

UR/GSF APCA

USC Unmix Unmix

UW PMF
estimates of source-specific mass impacts in

each city. In addition, as the various source

apportionment results were to be employed as

inputs into a daily time-series mortality analyses, the time-series intercorrelations of their

respective daily estimates of source impacts

were also evaluated and intercompared across

source categories in each city.

Health effects modeling. After the source

apportionments were submitted, all Washington and Phoenix daily source apportionments

were provided to K. Ito of the NYU PM

Center and T. Mar of the University of

Washington PM Center, respectively, for

inclusion in time-series mortality models to

assess the resulting variations in their sourcespecific health effects estimates (RRs). The

city-specific mortality models employed are

described below.

The model-building steps of the

Washington time-series mortality model

development used in these analyses (Ito et al.,

unpublished data) were designed to be similar

to those used in past studies of PM2.5 mass, as

follows:

• We first developed the base mortality model

as a function of season and other temporal

trends in Poisson generalized linear models

(GLMs) (McCullagh and Nelder 1989).

Using natural splines, we fit a smooth function of time to mortality to adjust the model

for seasonal trends and unmeasured seasonal

confounders, such as influenza epidemics.

The inclusion of this term also reduces

undesirable residual autocorrelation and

overdispersion in the mortality regression, so

the choice of the spline degrees of freedom

(df) for smoothing of time (df = 38, or 4 per

year) was based both on the fit to the mortality series and minimization of autocorrelation of the model residuals.

• Weather variables and a day-of-week variable

were then also incorporated into the base

model, consistent with past general practice

in PM2.5 modeling, including a) natural

splines of the same-day temperature with 4 df

to fit “hot” temperature effects; b) natural

splines of the average of lags 1 through 3 of

daily temperature (i.e., up to 3 days before

the date of death) to fit “cold” temperature

effects; and c) an indicator for “hot” (daily

mean temperature > 80°F) and “humid”

(daily relative humidity > 70%) days to fit

the interaction. The end result of this step

was a base model to which air pollutant variables could be added and evaluated.

• To the base model, each of the alternative

source components was individually added

(for each research group/method) to separately test the individual associations of each

source category with mortality, after controlling for the variables considered in the

base model. The RR associated with both

an interquartile (25th to 75th percentile)

and a 5th- to 95th-percentile increase in the

source estimate was computed for lag days

0 to 5 for each of the source apportionment

analyses. This approach provided directly

comparable mortality effect estimates for

each source category and for apportionment

modeling results of participating groups.

The basic steps of time-series model

development used in the Phoenix analyses

(Mar et al., in press) were the same as for

Washington. Similarly, associations between

source contributions and cardiovascular and

total nonaccidental mortality were analyzed

using Poisson GLMs in S-PLUS 2000

(Insightful Inc., Seattle, WA, USA). The same

Phoenix base mortality model was applied to

source apportionment analyses of all groups

to provide a consistent basis for comparison

across source components and groups (i.e.,

to eliminate model specification variability

from the analysis). The base model controlled

for extreme temperatures using an indicator

variable, mean temperature, relative humidity,

day of week, and time trends. Natural spline

smoothers were used for time trends, temperature, and relative humidity. We applied

12 df for the smoothing of time trend (i.e.,

4 df per year). The degrees of freedom for the

natural splines for time trends were selected to

minimize autocorrelation in the residuals and

the Akaike Information Criterion (AIC)

(Akaike 1974). For the analysis of cardiovascular mortality, 5 spline df and 2 days lag for

temperature were incorporated, based on past

experience with models of PM2.5 and mortality in this city. For the total mortality analysis,

5 spline df and 1 day lag for temperature were

employed, and 2 df for the smoothing of relative humidity with 0 days lag for both the

cardiovascular and total mortality analyses.

The degrees of freedom and the lags were

chosen to minimize the AIC. As in the case for

the Washington analyses, the respective estimated source contributions of the various

research groups were added to this base model,

in turn, as the particle pollution variable. The

RR associated with both an interquartile (25th

to 75th percentile) and a 5th- to 95th-percentile increase in the source estimate was

computed for lag days 0 to 5 for each of the

source apportionment analyses. Again, this

consistent mortality analysis approach across

source apportionments allowed a direct comparison of the daily mortality effect estimates

across the various source apportionment

analyses in each city.

Finally, we evaluated the size and significance of the additional variability introduced

to the PM–mortality, time-series analysis by

variations in the source apportionment process

across groups and methods, consistent with the

primary goal of this workshop. To this end, the

various source apportionments’ resulting mean

mass contributions and estimated percent

excess deaths per 5th- to 95th-percentile increment increase by source-apportioned PM2.5

were intercompared and then analyzed (within

each city) by analysis of variance (ANOVA)

and a GLM. This allowed us to compare variations in model estimates that were due to

“between-source” versus “within-source” (i.e.,

variation due to different analyses).

Results

Source apportionment intercomparisons. As

described in Hopke et al. (in press), the various source apportionment analyses from each

of the participating research groups were intercompared in two ways: a) by comparing the

mass contributions attributed to each of the

sources; and b) by calculating the correlation

coefficients between the source contributions

from PM2.5 from the various groups within

source groups. The solutions of the various

groups were compared with each other on an

equal basis, because an accepted “gold standard” method does not exist at this time for

source apportionment. Table 2 notes the

source apportionment analyses performed on

these data sets. Figures 1 and 2 present the

means and distributions of the resulting PM2.5

mass source apportionment for each source

category in Washington and Phoenix, respectively. Most groups were able to identify the

same major sources in their source apportionment analyses of the trace constituent data.

However, not all sources were identified by all

researchers; some groups did not provide

impacts for all possible source categories. Two

researchers from BYU contributed separate

analyses: Eatough (BYU1) and Christensen

(BYU2). In this plot, when researchers broke

out the source impacts differently from other

researchers (e.g., when secondary sulfates were

broken into sulfates 1 and sulfates 2, or traffic

was subdivided into categories, such as diesel

vs. gasoline fueled motor vehicles), the results

have been grouped to provide more directly

comparable totals. The mass apportionment

uncertainties included in Figures 1 and 2 visually indicate an overall consistency in impacts

by source category, as they provide confidence

intervals (CIs) that overlap across analyses of

the various groups, especially for the larger mass

contributors. To be more quantitative, we conducted an ANOVA F-test of the within-source

versus between-source variations for each of the

major source categories in Figures 1 and 2. The

results indicated significantly greater variability

(p < 0.001) across source categories than across

investigators/methods (i.e., investigator/method

variations were small compared with source-tosource variations). Overall, these plots and statistical analyses indicate that, although the

estimated mass impact results vary across analyses and not all sources were identified by all

investigators (especially in the case of the

smaller mass impact sources), there is both

Source apportionment of particle health effects

Environmental Health Perspectives • VOLUME 113 | NUMBER 12 | December 2005 1771
qualitative and quantitative consistency in the

major PM2.5 contributing sources identified

and their mass impacts across the independent

analyses of these data by the various research

groups and apportionment methods.

Because these apportionment results were

to be applied in time-series analyses, another

evaluation of the consistency of the source

apportionments across research groups and

apportionment methods was conducted.

Variability was examined in the paired correlations of estimated daily source apportionment

mass contributions in the various analyses

over time and within each city. As shown in

Figure 3A for Washington and Figure 3B for

Phoenix, the sulfate-containing, crustal, and

nitrate components exhibited among the highest mean intercorrelations across the various

research groups in these cities. Among the chief

PM2.5 mass contributors (Figures 1 and 2), the

weakest cross-analyses correlations in Figure 3

were usually found for the sources with the

greatest uncertainty in their composition (i.e.,

lacking unique constituents for unique identification), notably, traffic and wood burning in

Washington, and wood burning and metals in

Phoenix.

Time-series mortality effect estimate

intercomparisons. The source apportionment

results for each group were combined with the

mortality data in Washington and Phoenix,

and time-series mortality regressions were then

run (Ito et al., in press; Mar et al., in press).

Figure 4 displays the resulting mean RR estimates and 95% CIs of cardiovascular (CV)

and total daily mortality for each major source

category identified in Washington and

Phoenix for the overall workshop estimate,

with source apportionment interanalysis variation excluded and interanalysis variation

included. Results were derived using the lag of

maximum association in each analysis. It is

clear from the comparisons that the variability

introduced by the uncertainty of the acrosssource apportionment groups and analyses is

small, relative to the overall uncertainty of

these estimates. In quantitative terms, the percent increase in the uncertainty (i.e., in the

CI) for the mortality RR of each displayed

source category in Washington added by the

interanalysis variability was as follows: soil

(23% for CV, 18% for total); traffic (12% for

CV, 16% for total); and sulfate (25% for CV,

26% for total). In the Phoenix mortality

analyses, the percent increase in the uncertainty (i.e., in the CI) for the mortality RR of

each displayed source category added by the

interanalysis variability was as follows: soil

(4% for CV, 7% for total); traffic (6% for

CV, 33% for total); and sulfate (7% for CV,

5% for total). Thus, while the uncertainty

added by the differences in source apportionments varies from source to source in these

cases, the overall average increase is about

15%, which suggests that the error added by

variability in source apportionment approach

is quite small relative to the baseline uncertainty inherently associated with making these

time-series pollution RR estimates.

The between-source variation in these

daily mortality RRs was also compared with

within-source variations (variation due to different analyses). As shown in Table 3, significantly larger variation was found between

Thurston et al.

1772 VOLUME 113 | NUMBER 12 | December 2005 • Environmental Health Perspectives

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PM2.5 (μg/m3) PM2.5 (μg/m3) PM2.5 (μg/m3)

PM2.5 (μg/m3) PM2.5 (μg/m3) PM2.5 (μg/m3)

Research group

Research group

Research group

Research group

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Figure 2. Mean, interquartile range (box), and range (maximum–minimum) of mass impacts predicted by each

research group’s source apportionment analysis of the Phoenix PM2.5 data. (A) Soil; (B) secondary SO4; (C)

traffic; (D) metals/industry/smelter; (E) vegetation/wood burning; (F) sea salt. PM2.5 (μg/m3) PM2.5 (μg/m3) PM2.5 (μg/m3) PM2.5 (μg/m3) PM2.5 (μg/m3) PM2.5 (μg/m3) PM2.5 (μg/m3) PM2.5 (μg/m3)

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Figure 1. Mean, interquartile range (box), and range (maximum–minimum) of mass impacts predicted by each

research group’s source apportionment analysis of the Washington PM2.5 data set. MR, multiple regression.

(A) Soil; (B) nitrates; (C) traffic; (D) wood burning; (E) secondary SO4; (F) residual oil; (G) sea salt; (H) incinerator.
sources than between research groups in

reported RRs (p < 0.001) using an ANOVA

(in a GLM) of the individual investigator estimates and variances (for each death category

in each city) (Ito et al., in press; Mar et al., in

press). In the GLM, between-group variation

was a nonsignificant predictor for both death

categories in both cities (with p-values ranging

from 0.38 to 0.65 for between-group differences), whereas the between-source variation

was a statistically significant predictor of RR

in both cities and death categories (p < 0.001).

Overall, these results indicate that a) variations

in choice of research group or source apportionment method have only a small effect on

variations in the RR estimates for identified

sources, relative to the variations in RR caused

by different source components and the mortality regression process, and b) researcher variations in source apportionment applications

should not be a barrier to comparing the

source-specific PM2.5 RRs.

The size of the source-specific RR estimates

from these analyses can also be compared with

other published source-category effect estimates, although very few are available currently. The most consistently significant

category was secondary sulfates, which have

been widely examined before in the published

literature. In this case, the total mortality RR

estimates for the secondary sulfate component

were 5.2% change per 10 μg/m3 in Phoenix

and 3.8% per 10 μg/m3 in Washington. This

is somewhat larger than the sulfate-dominated

coal component reported by Laden et al.

(2000), but much smaller than that derived

from Ozkaynak and Thurston (1987). Their

research indicated 8% per 10 μg/m3 for this

component, but that study was of annual

mortality associated with long-term exposures,

rather than the daily mortality considered

here. It is interesting, however, that the

Washington component estimate from this

work (3.8% per 10 μg/m3 for the sulfate component) is very close to the sulfate-related coal

component value derived by Laden and colleagues for Boston, Massachusetts (2.8%).

Motor vehicles, another component that

approached significance in this work, yielded

0.9% per 10 μg/m3 RR in Phoenix, and 4.2%

in Washington. These results are similar to the

3.4% per 10 μg/m3 found by Laden and colleagues (2000), and the 2% per 10 μg/m3

derived from the work of Ozkaynak and

Thurston (1987). Thus, these source-specific

estimates appear reasonable when compared

with the limited source-specific mortality

analyses done in the past, but much more

work of this type must be done before broadbased comparisons with the RR results from

this workshop are possible.

Discussion and Conclusions

With regard to the PM2.5 mass apportionments, the findings of this intercomparison

among results from some of the leading source

apportionment research groups indicate that

the same major source types (those that contribute most of the PM2.5 mass at each site),

with similar elemental makeups (i.e., key tracers), are consistently identified by different

groups in each city. Methods generally yielded

the most consistent results (i.e., the highest

correlations across groups over time) for

sources with the most definable (unique) tracers or combinations of tracers in each city.

In Washington, soil, secondary sulfate and

nitrate, oil burning, and incineration were

most unambiguously identified by various

methods; wood burning, salt, and traffic were

less well correlated across analyses. In Phoenix,

soil, traffic, secondary sulfate, and sea spray

were most highly correlated across analyses;

wood and vegetative burning, metals industry

particles, and coal fly ash were less well correlated. Based on the relative sizes of these

intergroup intercorrelations for each of the

source types in these two cities, the soil-, sulfate-, residual oil-, and salt-associated mass

components were generally seen to be most

unambiguously identified by the various

source apportionment methods, while vegetative burning and traffic were less well correlated across groups. However, the source mass

Environmental Health Perspectives • VOLUME 113 | NUMBER 12 | December 2005 1773

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PM2.5 Soil Traffic Sulfate PM2.5 Soil Traffic Sulfate

PM2.5 Soil Traffic Sulfate PM2.5 Soil Traffic Sulfate

Percent excess deaths per

5th–95th concentration increment

Percent excess deaths per

5th–95th concentration increment

Percent excess deaths per

5th–95th concentration increment

Percent excess deaths per

5th–95th concentration increment

95% CI including interanalysis variation

95% CI without interanalysis variation

Figure 4. Mean RR estimates and 95% CIs for each major source category in Washington (A) cardiovascular and (C) total nonaccidental mortality, and Phoenix (B) cardiovascular and (D) total nonaccidental mortality for the overall workshop estimate, with source apportionment interanalysis variation excluded and

with the interanalysis variation included.

Table 3. ANOVA analysis of source-specific mortality RR estimates.

ANOVA Source category Research group

Mortality category p-value variance (%) variance (%)

Washington CV < 0.001 47.5 9.5

Washington total < 0.001 80.0 2.6

Phoenix CV < 0.001 76.3 4.5

Phoenix total < 0.001 64.8 6.3

A

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wood burning

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Primary coal

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Correlation (r)

Correlation (r)

Figure 3. Box and whisker plots of the distributions of temporal correlation coefficients (r) between all possible pairs of similar source contributions resolved for (A) Washington and (B) Phoenix.

Source apportionment of particle health effects
Thurston et al.

1774 VOLUME 113 | NUMBER 12 | December 2005 • Environmental Health Perspectives

impacts predicted for the various source categories were generally not significantly different

from one another across the research groups,

indicating consistency in the source apportionment results. The addition of further

tracers/analyses may be required to improve

the consistency of the less well-discriminated

sources. For example, the measurement of

low-volatility organic compounds has been

suggested as one way to better discern trafficrelated PM components (Schauer et al. 1996;

Schauer and Cass 2000). Overall, however,

although there are no gold standard correct

answers for the source identification and

apportionments in the real-world data sets

considered in this workshop, the apportionment consistency in the largest PM2.5 source

contributors across researchers in these cities,

often using differing statistical methods, indicates reliability in the source apportionment

approach.

With regard to the health effects apportionments to the different source components

of PM2.5, the between-source variation in

daily mortality RR was significantly larger

than the between-research group variation in

reported RRs. Thus, analysis-to-analysis variability in the source apportionments was small

compared with the overall uncertainty in the

mortality RR estimates. In addition, betweengroup variation in RR estimates was nonsignificant, whereas the between-source type

variation was statistically significant. This result

indicates that variations in choice of research

group or source apportionment method have

only a small effect on variations in the RR estimates, relative to the variations in RR caused

by different source components. Indeed, in

mortality categories where significant PM2.5

mass–daily mortality associations were detected

in these cities (e.g., for cardiovascular deaths in

both cities), most source categories were nonsignificant contributors. However, the most

strongly associated source categories showed

statistically significant contributions. Across

these two cities, the most consistently associated PM2.5 source category was sulfate-associated mass. The source RR estimates generally

had overlapping confidence bands, indicating

that larger numbers of observations will be

required in each of these cities to have enough

power to significantly differentiate the impacts

of the various source impacts. The overall

source-specific RR estimates derived in this

work appeared reasonable when compared

with the limited source-specific mortality analyses published in the past, but many more source

apportionment–mortality analyses of this type

must be done before broad-based comparisons

with the source-specific RR results from this

workshop are possible.

Overall, the results of this intercomparison

of the health effects apportionments found

that variations in PM source apportionment

research group or method introduced relatively little uncertainty into the evaluation of

differences in PM toxicity on a source-specific

basis, adding an average of only approximately

15% to the overall source-specific mortality

RR uncertainties. Variations in these apportionment modeling choices do not prevent the

consistent discernment of variations in the relative strengths of source-specific PM2.5 mortality associations. However, the uncertainty

added by the source apportionment estimation suggests that longer data records may be

required for significant effects to be detectable

in source-specific analyses than for PM2.5.

The conduct of daily speciation sampling

(rather than every third day) in major U.S.

cities would be one way to rapidly improve the

power of future source apportioned PM timeseries health effects analyses. Daily sampling

would also better clarify the potentially differing distributed-lag natures of the various

source-specific impacts identified in this workshop. Although further research and the

possible addition of more key tracers to the

speciation of PM2.5 are needed to better characterize ambient tracer profiles for sources

with less well-defined compositional characteristics (e.g., for vegetative burning and traffic),

the results of this workshop indicate that

present-day PM2.5 source apportionment

methods can provide valuable insights into the

source components that contribute most to

PM2.5–health effects associations.

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