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: Workshop on UNMIX and PMF
I As Applied to PM2>5
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14-16 February 2000
U.S. EPA, RTP, NC
Final Report
by
Robert D. Willis
ManTech Environmental Technology, Inc.
Research Triangle Park, NC 27709
Contract No. 68-D5-0049
Project Officer
Portia Britt
Work Assignment Manager
Charles W. Lewis
National Exposure Research Laboratory
Human Exposure and Atmospheric Sciences Division
National Exposure Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
-------
Notice
The U.S. Environmental Protection Agency through its Office of Research and Development funded and
managed the research described here under Contract 68-D5-0049 to ManTech Environmental Technology. Inc.
It has been subjected to the Agency's peer and administrative review and has been approved for publication
as an EPA document.
-------
NERL-RTP-HEASD-00-161
TECHNICAL REPORT DATA
1. REPORT NO.
EPA/600/A-OQ/048
3.RECIPIENTS ACCESSION NO.
4. TITLE AND SUBTITLE
Workshop on UNMIX and PMF as Applied to PM2.5
3.REPORTDATE
6 PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
Robert D.Willis
8.PERFORMING ORGANIZATION
REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
ManTech Environmental Technology, Inc.
Research Triangle Park, NC 27711
10J»ROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
68-D5-0049
12. SPONSORING AGENCY NAME AND ADDRESS
National Exposure Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
13.TYPE OF REPORT AND PERIOD
COVERED
EPA Report; February 14-16,2000
14. SPONSORING AGENCY CODE
EPA/600/09
15. SUPPLEMENTARY NOTES
16. ABSTRACT
This report is the proceedings of a workshop convened to evaluate two new air quality receptor models,
Positive Matrix Factorization (PMF) and UNMLX. The workshop was held hi Research Triangle Park,
NC, during February 14-16,2000 and was sponsored jointly by EPA's Office of Research and Develop-
ment (ORD) and Office of Air Quality Planning and Standards (OAQPS). The workshop evaluation of
PMF and UNMLX was accomplished by examining the results of applying both models to two ambient
PM2.5 data sets, one real and one synthetically generated. Both data sets were supplied in advance to a
proponent of each model (UNMIX: Dr. Ron Henry, University of Southern California; PMF: Dr. Phil
Hopke, Clarkson University). Each brought to the workshop the results of independently applying their
model to both data sets. The report briefly summarizes the technical exchange and major conclusions
reached during the workshop.
17.
KEY WORDS AND DOCUMENT ANALYSIS
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18. DISTRIBUTION STATEMENT
RELEASE TQPUBLIC
19. SECURITY CLASS (This Report)
UNCLASSIFIED
21 .NO. OF PAGES
20. SECURITY CLASS /This Faze)
UNCLASSIFIED
22. PRICE
-------
GENERAL DISCLAIMER
This document may have problems that one or more of the following disclaimer
statements refer to:
• This document has been reproduced from the best copy furnished by the
sponsoring agency. It is being released in the interest of making
available as much information as possible.
• This document may contain data which exceeds the sheet parameters. It
was furnished in this condition by the sponsoring agency and is the best
copy available.
• This document may contain tone-on-tone or color graphs, charts and/or
pictures which have been reproduced in black and white.
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• Portions of this document are not fully legible due to the historical nature
of some of the material. However, it is the best reproduction available
from the original submission.
-------
Contents
Volume I: Workshop Proceedings
Agenda 1
Introduction 3
Session 1: 14 February, a.m 4
Opening Remarks 4
Session 1A: UNMIX Methodology 4
UNMIX Results on Synthetic Data Set 5
UNMIX Results on the Phoenix Data Set 6
Session IB: PMF Methodology 6
PMF Results on Synthetic Data Set 8
PMF Results on Phoenix Data Set 8
Session 1C: Overview of Synthetic Data Set Results 8
Session 2: 14 February, p.tn 10
Session 2A: Description of the Synthetic Data Generation Process 10
Session 2B: Processing of Synthetic Data and Resulting Solutions for PMF 10
Session 2C: Processing of Synthetic Data and Resulting Solutions for UNMIX 10
Session 2D: Description of Metric of the Goodness of Fits of the Solutions and the Results
of Applying the Metric 11
Session 3: 15 February, a.m 13
Session 3A: Phoenix Source Apportionment Studies 13
Session 3B: Phoenix NERL Platform Studies—Data Quality issues and
Supplementary Analyses 14
Session 3C: PMF Analysis of Phoenix Data 14
Session 3D: UNMIX Analysis of Phoenix Data 15
Session 4: 15 February, p.m 16
Session 4A: Reexamination of the Synthetic Data Results 16
Session 48: Demonstration of UNMIX Program 16
Session 4C: Demonstration of the PMF Program 17
Session 4D: Potential Effects of Data Artifacts on Receptor Modeling Results 17
Session 4E: Open Discussion 19
Session 5: 16 February- a.m 20
Session 5A: Application of PMF in the Northern Great Lakes: A Tale of Two Studies 20
Session 5B: Discussion of FPEAK, Open Discussions, and Workshop Conclusion 20
References 22
Attendees 24
in
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Volume II: Appendices
Appendix 1 A: UNMIX User's Manual and Presentation Materials for
UNMIX Theory ai>d Applications jA-J
Appendix IB: A Guide to Positive Matrix Factorization 1B-1
Appendix 1C: Presentation Materials forOverview of Synthetic Data Set Results 1C-1
Appendix 2A: Presentation Materials for Description of Synthetic Data Generation Process 2 A-1
Appendix 2B: Presentation Materials for Processing of Synthetic Data and Resulting
Solutions for PMF 2B-1
Appendix 2C: Presentation Materials for Processing of Synthetic Data and Resulting
Solutions for UNMIX 2C-1
Appendix 2D: Presentation Materials for Description of Metric of the Goodness of Fits of
the Solutions and the Results of Applying the Metric 2D-1
Appendix 3A Presentation Materials for Phoenix Source Apportionment Studies 3A-1
Appendix 3B Presentation Materials for Phoenix NERL Platform Studies 3B-1
Appendix 3C Presentation Materials for PMF Analysis of Phoenix Data 3C-1
Appendix 3D Presentation Materials for UNMIX Analysis of Phoenix Data 3D-]
Appendix 4D Presentation Materials for Potential Effects of Data Artifacts on
Receptor Modeling Results 4D-1
Appendix 5A Presentation Materials for Application of PMF in the Northern Great Lakes SA>1
Appendix 5B Presentation Materials for Discussion of FPEAK 5B-I
Appendix 6 Journal Article Excerpt Concerning EPA's PAMS Guidance on Reporting of
Low Concentration Data 6-1
iv
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Volume I
Workshop Proceedings
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Agenda for Workshop on UNMIX and PMF as Applied to 5»M25
Dates: 2/14/2000-2/16/2000
Location: EPA Administrative Building Auditorium, RTP, NC
February 14,8:30 a.m. - 5:00 p.m.
Morning Session: (Session 1)
General presentations on the methodology behind the tools and a brief presentation of the solutions found
for both the Phoenix and the synthetic data set. This session is geared toward a general audience with the
purpose of giving an overview of the tools and the results from their applications. The following 4 sessions
will go into the details and will be at an advanced technical level, thus not for a general audience.
8:30-8:45 Welcome and Introductions (Chuck Lewis, ORD, and John Bachmann, OAQPS)
8:45-10:00 Presentation on UNMIX methodology and results for Phoenix and synthetic data set (Dr.
Ron Henry)
10:00-10:15 Break
10:15-11:30 Presentation on PMF methodology and results for Phoenix and synthetic data set (Dr. Phil
Hopke)
i 1:30-12:00 Overview describing the synthetic data set and a pictorial presentation of how close the tools
reproduce the "known" profiles (OAQPS)
12:00-1:00 Lunch
Afternoon Session: (Session 2)
Thorough discussions of the results from the synthetic data set analysis. Includes description of the data
generation, the metric used by EPA to determine how well the tools reproduced the "known" profiles, data
preprocessing (e.g., outlier identification), selection criteria for which species to use in the models and the
number of sources to try to fit, and a description of the solutions (identification of the fined sources and
the uncertainties with these solutions).
1:00-1:15 Description of the data generation process (OAQPS)
1:15-2:00 Presentation of processing of synthetic data and resulting solutions for PMF (Dr. Phil
Hopke)
2:00-2:45 Presentation of processing of synthetic data and resulting solutions for UNMIX (Dr. Ron
Henry)
2:45-3:00 Break
3:00-4:00 Description of metric of the goodness of fits of the solutions and the results of applying the
metric (OAQPS)
4:00-5:00 General discussion topics such as what it means to say that one solution is better than
another, how to use "known" profiles to compare with derived solutions for source
identification, and whether it is realistic to have an automated source identification process
(General discussion)
February 15,8:00 a.m.-5:00 p.m. * •
Morning Session: (Session 3)
Thorough discussions of the results from the Phoenix analysis. Includes steps used to preprocess the data
to identify' potential outliers, selection of species and number of sources used in the model, estimates of
confidence (error bars) in the source compositions and contributions, and degree of fit obtained.
8:00-8:45 Results from other recent source apportionment studies in Phoenix (Mark Hubble, Arizona
Department of.Environmental Quality')
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8:45-9:00 Data quality issues associated with Phoenix measurements used in current analyses, and
supplementary analyses (SEM and trajectory analyses) performed to confirm sources (ORD)
9:00-12:00 (Break when needed.) Presentations by Hopke and Henry on their respective Phoenix
analyses, addressing the issues listed above.
12:00-1:30 Lunch
Afternoon Session: (Session 4)
Thorough discussions on how the tools really work. In trying to use the tools over the past few months,
EPA has had some questions about operating the tools and interpreting the output. This session will be
a "question and answer" session, where many of the questions will have examples to illustrate them.
1:30-1:45 Reexamination of the synthetic data results (OAQPS)
1:45-2:15 Demonstration of UNMIX Program (Dr. Ron Henry)
2:15-2:45 Demonstration of PMF Program (Dr. Phil Hopke)
2:45-3:15 Potential effects of MDL on modeling results (Rich Poirot, Vermont Department of
Environmental Conservation)
3:15-5:00 Open discussions on how the tools really work. Questions of interest include:
(1) Can the tools identify a source that has a discrete profile change? How different do the before and
after profiles have to be for the tools to find two unique sources? (OAQPS has constructed an
example.)
(2) Should the measured total mass or the reconstructed mass (PM, 5) be included as a fining species or
not?
(3) How to identify and handle outliers?
(4) UNMIX specific questions: What are the equations behind RA2 and strength/nofce? What do they
measure? How are "edges" fit, especially in light of errors? Do the interior (non-edge) points have
any influence on the solution? Why is it that UNMIX uses at most -15 species and finds at most ~6
sources? Why does UNMIX often find no feasible solution? How does a user wisely use the new
feature in UNM1X2 that allows for source compositions with very negative entries? Implications of
not using MDLs and uncertainties (which is a continuation of the discussion started in (3))?
(5) PMF specific questions: What is rotmat and how can it be used to understand better how much
rotation freedom there is in the solution? What is the appropriate FPEAK to use? Should multiple
passes be made using various FPEAKS: one pass to improve source identification at the expense of
the contribution component, and the second pass to accurately reflect the contribution component at
the expense of source identification? How are FPEAK, FKEY, and GKEY implemented? Are they
pan of the regularization component of Q? (OAQPS has constructed an example that shows slightly
negative FPEAKs are preferable.)
February 16,8:30 a.m. -12:00 p.m.
Morning Session: (Session 5)
Discussion of general problems and potential solutions regarding issues such as treatment of secondary
sources, regional vs local source identification, and recommendations for further research and testing of
methods. Discuss why factor analysis is "ill-posed" (i.e., produces infinitely many solutions) and begin
a discussion about how to use multiple receptors with these tools.
8:30-9:15 Results from applying PMF to data from the Lake Michigan area (Dr. Kurt Paterson,
Michigan Technological University)
9:15-12:00 Work on issues listed above.
12:00 End of workshop
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Introduction
This report provides a summary of the Workshop on
UNMIX and Positive Matrix Factorization (PMF) as Applied to
PM. .. This 2'/i-day workshop was held at the EPA administra-
tive building auditorium in Research Triangle Park, NC, during
14-16 February 2000. Sponsored jointly by EPA's Office of
Research and Development (ORD) and Office of Air Quality
Planning and Standards (OAQPS), the workshop was intended
to facilitate an exchange of technical information on the use of
two source apportionment tools as applied to paniculate matter
(PM). PMF and UNMIX represent the current state of the art in
multivariate receptor modeling. Both methodologies attempt to
generate source contribution estimates as well as source compo-
sitions using only the ambient data.
The workshop evaluation of PMF and UNMIX was
accomplished by examining the results of applying both models
to two ambient PM:, data sets, one real and one synthetically
generated. Both data sets were supplied in advance to a
proponent of each model (UNMIX: Dr. Ron Henry, University
of Southern California: PMF: Dr. Phil Hopke, Clarkson
University). Each brought to the workshop the results of
independently applying their model to both data sets. The source
contributions underlying the synthetic data set were of course
known to the EPA personnel who generated the data set, but this
information was not made available prior to the workshop.
Approximately 40 attendees representing primarily EPA.
universities, and state environmental agencies attended the work-
shop. A list of attendees is provided at the end of this volume.
The purpose of this report is to briefly summarize the
technical exchange and major conclusions reached during the
workshop. The organization of the report follows the workshop
agenda. The text of the report is intentionally brief to spare the
reader from overwhelming detail. Interested readers who seek
more detailed information are referred to the appendices
(Volume II) for hard copies of individual presentations and
supporting materials.
The references given at the end of this report are intended
to provide a complete list of all known publications relating to
the theory and application of PMF and UNMIX.
In addition to this report, the workshop was recorded on
videotape and the tapes are available for loan on request from
Dr. Charles Lewis, EPA (tel: 919-541-3154; e-mail: lewis
.charlesw@epa.gov).
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Session 1
14 February, a.m.
Opening Remarks
Chuck Lewis (ORDj. John Bachmann (OAQPS). and Shelly
Eberly (OAQPSj
Chuck Lewis opened the workshop by acknowledging the
efforts of Shelly Eberly who was the primary organizer of the
workshop and who alternated with Chuck Lewis as session
moderator. Lewis stressed that the workshop was not intended
as a "shoot-out" between two competing receptor modeling
approaches in order to declare a winner. Rather, the intent was
to provide researchers with a better understanding of the
methods in order to assess the potential of these tools for
regulatory and research applications.
Lev. is provided the following definition of receptor models:
Receptor models are mathematical procedures for identi-
fying and quantifying the sources of ambient air pollutants
and their effects at a site (receptor)
• primarily on the basis of concentration measurements
at the receptor, and
• generally without need of emissions inventories and
meteorological data.
The two multivariate receptor models that are the subject of the
workshop are much more complicated to understand and use
than those presently in common usage. The potential reward for
the complexity is that these models "do it all." That is, they
generate both source contributions and source profiles, all from
ambient data.
John Bachmann. Associate Director of OAQPS, stressed the
importance of receptor modeling from the regulatory perspec-
tive. Receptor models can provide important scientific support
for current (or future) PM standards. In addition, receptor
models can be an important tool in understanding the associa-
tions among PM, visibility, and health effects, and in developing
regulatory control strategies. State-of-the-art tools such as
UNMIX and PMF, as well as experienced users of these tools,
will be needed to interpret the large quantity of data expected
from the PM,, Speciation Monitoring Network.
Shelly Eberly had members of the audience introduce
themselves and briefly describe their experience in receptor
modeling.
The remainder of Session 1 consisted of overviews of the
UNMIX and PMF models and results by their principal
proponents, Drs. Ron Henry and Phil Hopke, respectively, and
an overview of the synthetic data set. Session 1 was intended as
a less technical summary of the methods and results for the
benefit of managers and others who were unavailable for the
entire workshop.
Session 1A: UNMIX Methodology
Dr. Ron Henry, University of Southern California
(Full presentation is in Appendix 1 A.)
Dr. Henry presented the theory1 of the UNMIX model from
a geometric perspective. The fundamental problem tor receptor
models is posed as follows: Given an ambient data set,
find—with as few assumptions as possible—the number of
sources, the composition and contributions of the sources, and
the uncertainties. However, the problem as presented in the
conventional mass balance formulation is statistically ill-defined,
i.e., there exist an infinite number of solutions that have the same
root mean squared error and that satisfy the non-negativity
requirement for source compositions and contributions. The keys
to finding a unique solution are therefore (1) to determine the
number of sources in the data that are above the noise level, and
(2) to find additional constraints that limit the number of
solutions.
-------
The UNMIX model takes a geometric approach to these two
key problems that exploits the covarjance of the ambient data.
Simple two-element scatterplots of the ambient data provide a
basis for understanding the UNMIX model. For example, a
straight line and high correlation for Al versus Si can indicate a
single source for both species (soil), while the slope of the line
gives information on the composition of the soil source. In the
same data set, iron does not plot on a straight line against Si,
indicating other sources of Fe in addition to soil. More
importantly, the Fe-Si scatterplot reveals a lower edge. The
points defining this edge represent ambient samples collected on
days when the only significant source of Fe was soil. Success of
the UNMIX model hinges on the ability' to find these "edges" in
the ambient data from which the number of sources and the
source compositions are extracted. UNMIX uses principal
component analysis to find edges in m-dimensional space, where
m is the number of ambient species. The problem of finding
edges is more properly described as finding hyperplanes that
define a simplex. The vertices at which the hyperplanes intersect
represent pure sources from which source compositions can be
determined. However, there is measurement error in the ambient
data that "fuzzes" the edges making them challenging to find.
UNMIX employs an "edge-finding" algorithm to find the best
edges in the presence of error. Once the edges are found, the
major issue remains of estimating the number of sources.
UNMIX finds the number of sources using a resampling
technique (NUMFACT algorithm) in which random subsets of
samples are successively fit with UNMIX. Results for major
sources change little during the resampling, while minor sources
show considerable variability. NUMFACT calculates a signal-
to-noise (S'N) ratio for each factor, and results with real data
sets indicate that a S/N ratio >2 is an effective rule of thumb in
estimating the number of quantifiable sources.
Using only ambient data, UNMIX outputs the following
information:
Number of sources
• Composition of each source
• Source contributions to each sample
• Uncertainties in the source compositions
• Apportionment of the average total mass, if total mass
is included in the model
The major assumptions employed in UNMIX are as follows:
• Source compositions remain approximately constant.
• There are at least N • (N-1) points that have low or no
impact from each of the N sources, i.e., need some
points with one source missing or low.
Advantages of the UNMIX tool were given as the
following:
• No assumptions about the number or compositions of
sources are needed.
• No assumptions or knowledge of errors in the data are
needed.
• UNMIX automatically corrects source compositions for
effects of chemical reactions.
A major difference between UNMIX and PMF is that
UNMiX does not make explicit use of errors or uncertainties in
the ambient concentrations. This is not to imply that the UNMIX
approach regards data uncertainty as unimportant, but rather that
the UNMIX model results implicitly incorporate error in the
ambient data.
UNMIX Results on Synthetic Data Set
Henry summarized his seven-source UNMIX model for the
synthetic data set. UNMIX source apportionment results are
summarized in the following table:
Source
Soil
Vehicles
Steel sinter
Residual oil
Combustion
Palladium source
Asphalt roofing
Mean Source Contribution
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Steel Sinter
3.5
2.5
1.6
0.5-
0 .
-50
50 100 150 200 250 300 350 400
wind directions for these samples are normalized to the hourly
wind-direction data for all sanr-1 s and the relative frequency is
then plotted for each 10-degree wind sector. The plot shows that
on days when the steel sinter source has a high expected source
contribution, the winds are three times more likely to be from
200 to 220 degrees than the average frequency over all samples.
UNMIX Results on the Phoenix Data Set
Dr. Henry presented a six-source UNMIX solution for the
Phoenix PM: < data set as summarized in the following table:
Mean Source Contribution
Source
Vehicles
Secondaries
Soil
Diesels
Vegetative burning
Unexplained
4.7
2.6
1.8
1.2
0.7
1.6
Secondaries include sulfaies and organic carbon. Source
compositions are shown in Appendix I A. It should be noted that
the "unexplained" source represents a real source (or mixture of
real sources) that was extracted by UNMIX but could not be
specifically identified.
The identification of the "diesel" source hinged on the high
Mn concentration and the high OC and EC concentrations, as
well as the fact that this source contributed only one-fourth as
much on the weekends as on weekdays. Henry speculated that
the Mn is a fuel additive used (probably illegally) by diesel truck
operators to prevent engine fouling. Time-series plots for the
different sources are consistent with their identification, e.g.,
vehicle source peaks during the winter months, while the
secondary source peaks during the summer.
Session 1B: PMF Methodology
Dr. Philip Hopke, Clarkson University
(Full presentation is in Appendix IB.)
PMF is a recently developed least squares formulation of
factor analysis with built-in non-negativity constraints. PM F was
developed by Dr. Pemti Paatero in Finland in the mid-1990s.
The tool is currently being refined jointly by Paatero and Hopke.
The following is excerpted from Hopke and Song. Appendix 2B:
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"Suppose X is a n by m data matrix consisting of
the measurements of n chemical species in m samples.
The objective of multivariate receptor modeling is to
determine the number of aerosol sources, p, the chemi-
cal composition profile of each source, and the amount
that each of the p sources contributes to each sample.
The factor analysis model can be written as:
(1)
where G is a n by p matrix of source chemical
compositions (source profiles) and F is ap by m matrix
of source contributions (also called factor scores) to the
samples. Each sample is an observation along the time
axis, so F describes the temporal variation of the
sources. E represents the pan of the data variance un-
modeled by the p-factor model.
In PMF, sources are constrained to have non-
negative species concentration, and no sample can have
a negative source contribution. The error estimates for
each observed data point were used as point-by-point
weights. The essence of PMF can thus be presented as:
min Q(X,o,G.F)
G,F
(2)
where
l(X-GF)
(3)
(4)
with g,t 2 0 and fu 2 0 for k = l,...,p, and o is the known
matrix of error estimates of X. Thus, this is a least squares
problem with the values of G and F to be determined.
That is. G and F are determined so that the Frobenius
norm of E divided by o (point-wise) is minimized. As
shown by Paatero and Tapper [1], h is impossible to
perform factorization by using singular value decompo-
sition (SVD) on such a point-by-point weighted matrix.
PMF uses a unique algorithm in which both G and F
matrices are varied simultaneously in each least squares
step. The algorithm was described by Paatero [2].
Application of PMF requires that error estimates for
the data be chosen judiciously so that the estimates
reflect the quality and reliability of each of the data
points. This feature provides one of the most important
advantages of PMF, the ability to handle missing and
below-detection-limit data by adjusting the correspond-
ing error estimates. In the simulated data, there were
some below-detection-limit values for different chemical
species. As the input to the PMF program, the con-
centration data and the associated error estimates were
constructed as follows: For the measured data (above
detection limit), the concentration values were used
directly, and the error estimates were built as the
analytical uncertainty plus a quarter of detection limit.
For the below-detection-limit data, half of the detection
limit was used as the concentration value, and as the
error estimate as well. This strategy [3] appeared to
work well in the present study."
Excerpt from Appendix IB:
"Another important aspect of weighting of data
points is the handling of extreme values. Environmental
data typically shows a positively skewed distribution and
often with a heavy tail. Thus, there can be extreme values
in the distribution as well as true "outliers." In either
case, such high values would have significant influence
on the solution (commonly referred to as leverage). This
influence will generally distort the solution and thus an
approach to reduce their influence can be a useful tool.
Thus, PMF offers a "robust" mode. The robust factori-
zation based on the Huber influence function [Huber,
1981] is a technique of iterative reweighing of the
individual data values."
A critical step in PMF analysis is the determination of the
number of sources. Plots of the scaled residuals for all species
can help determine the number of factors. It is desirable to have
symmetric distributions and to have all the residuals within ±3
standard deviations. If there is asymmetry or a larger spread in
the residuals, then the number of factors should be reexamined.
Note: The definition of F and G are interchanged throughout
this report. In some places F represents the source compositions
and G represents the source contributions and in other places F
represents the source contributions and G represents the source
compositions. From a mathematical perspective, this is permis-
sible, although it may lead to confusion for the reader. Most of
the current literature refers to F as the source composition matrix
and G as the source contribution matrix.
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PMF Results on Synthetic Data Set
Hopke presented a nine-factor solution for the simulated
data set as summarized in the following table:
Mt an Source Contribution
Source (ug'm')
Area source
Inner highway
Residual oil combustion
Steel sinter
Asphalt roofing
Municipal incinerator
Petroleum refinery
Lime kiln
Extra area source
26
24
6
1.5
2
1
1
5
2
The major sources were the area source and the inner
highway source. All factors showed a reasonable relationship to
the true source profiles provided to the modelers: For many
factors, concentrations of most species were within 1-sigma
uncertainty of the synthetic concentrations. Plots of residuals for
selected species were generally symmetric and were contained
within =2 sigma. Residual plots are a useful aid in deciding how
man> factors are optimal. In the case of the synthetic data set,
residual peaks for some species were relatively broad and
asymmetric when fewer than nine factors were used. A
scatterplot of the modeled mass versus the synthetic mass
showed excellent agreement.
PMF Results on Phoenix Data Set
PMF yielded a six-source model for the Phoenix PM,} data
set as summarized in the following table:
Source
Biomass burning
Motor vehicles
Coal-fired power plant
Soil
Smelter
Sea salt
Mean Source Contribution
4.4
3.5
2.1
1.9
0.5
0.1
Motor vehicle emissions and biomass burning were the
major sources. It is noteworthy that PMF was able to extract the
sea-salt factor even though concentrations for the key
determining species (Na and Ci) were mostly below their
respective detection limits. This source was not found with the
UNMIX model because the Ma and Cl were not good-fining
species. Time-series plots for the six factors showed that most
source contributions generally peaked during the winter; how-
ever, the sea-salt source showed aperiodic episodes. Modeled
mass and observed mass were generally in good agreement. PMF
was also applied to the PM^ data and a five-factor model gave
best results. The five sources were identified as (1) soil, (2) con-
struction, (3) road dust, (4) sea salt, and (5) coal-fired power
plant. Soil and construction were the major sources.
In summary, Hopke cited the following advantages of PMF:
• PMF allows optimal weighting of individual data
points. This in turn makes it possible to include less
robust species (those with many missing values or
,f values below the detection limit) that may nevertheless
define real sources.
• PMF provides for natural inclusion of non-negativity
and other constraints.
• The PMF approach will allow future inclusion of better
algorithms for finding die optimal number of factors.
Session 1C: Overview of Synthetic
Data Set Results
Shelly Eberly, OAQPS
(Full presentation is in Appendix 1C.)
Ms. Eberly provided a brief overview of the synthetic data
and a comparison of the PMF and UNMIX results to the
synthetic data. Eberly's remarks addressed the following topics:
• A description of how the synthetic data set was
generated.
• Discussion of the 16 distinct sources that were input
into the model. (Temporal modulation of the synthetic
sources was critical in being able to resolve individual
sources.)
• The geographic layout of "Palookaville."
• A summary of the average source contributions used to
generate Palookaville's ambient data.
• Summary of the materials provided to the analysts
(Hopke and Henry).
• Summary of the materials received from the analysts.
• Side-by-side comparison of the sources identified
by UNMIX and PMF and the source contribution
estimates.
-------
Comparison of UNMIX and PMF results to the known
results. This comparison is shown below:
Comparison to Known Profiles
(Amended)*
Sources identified by both tool*
(known / UNMIX / PMF;
- Area /Soil /Area
- mm? Hwr/WucH/ Inner Hwx
- fto««u*! Oil Combustion
- Mitn. Inon
26/28/26
26/25/24
5/5/6
1/4/1
OB/ 6/ IS
04/2/2
- Asphah Roofing
Source Identified by UNMIX only
- Palladium fourct ;-3>
Sources identified by PMF only
(known /PMF)
- P«tro Refm / Petro Refm 06/1
- Urn* kim/ lime krin 06/5
- Coal Comb / Enta Area 15/2
•Note: Originally the above chart did not have the "muni-
cipal incinerator" source in the category of "Sources identi-
fied by both tools." UNMIX had identified the source, but
under the label "Combustion source located toNE of site."
• Comparison of UNMIX and PMF residual oil com-
bustion source profiles to the synthetic source profile.
• Scanerplots of UNMIX source strength versus true
source strength and PMF source strength versus true
source strength for the residual oil combustion source.
Eberly offered the following conclusions:
• The largest three known sources were correctly
identified by both tools and the modeled mass was
close to the simulated mass for all three sources.
• The fourth largest source (coal combustion, presence of
source withheld from analysts) was not identified by
either tool. PMF found a source similar to the coal
combustion source but identified it as an extra area
source. UNMIX did not find the source.
chosen between 5% and 10%. These numbers were used as the
coefficients of variation (CVs) for log-normal distributions of
the measurement errors of the species. Daily random measure-
men^ error drawn from this distribution was applied after the
"true'' species concentration at the receptor was computed.
An MDL for each species was provided. These MDLs were
computed as a function of the average concentration and the
species' measurement error CV. Specifically, the MDL for each
species was computed as the maximum of 1.5 x CV * (mean
concentration) and 0.001 ug/m'. The data below the MDL were
not modified in any way.
As a consequence of not modifying the data below MDL,
Henry pointed out that scatterplots of certain species revealed an
unrealistic structure of sub-MDL data in the synthetic data set.
For example, although all values of iodine were below the MDL,
scanerplots of iodine values versus other selected species
showed high r values, indicating that the synthesized iodine data
were not truly noise.
• Three to four smaller known point sources were
identified but the estimated source contributions were
larger than the true source strengths.
Following Eberly's presentation, the session was opened
for questions to any of the previous presenters. Eberly was asked
how the synthetic uncertainties and minimum detection limits
(MDLs) were determined. Response: Each of the 50 species had
a single MDL and a single uncertainty, which were fixed across
the entire data set. For each species a number was randomly
-------
Session 2
14 February, p.m.
Drs. Hopke and Heniy described in more detail their PMF
and UNMIX solutions for the synthetic data set. Dr. Basil
Coutant discussed goodness of fit (GOF) metrics for evaluating
receptor model solutions and the results of applying GOF
metrics to the PMF and UNMIX solutions.
Session 2A: Description of the
Synthetic Data Generation Process
Dr. Basil Coutant, OAQPS
(Full presentation is in Appendix 2A.)
Dr. Coutant provided a more detailed description of how
the synthetic data set was generated. Sixteen distinct source
profiles were used in Palookaville—nine point sources, four
industrial complexes, one area source, and two highways. The
area profile was a mixture of dust and road profiles. All source
profiles with the exception of the petroleum refinery were
fixed. The latter profile had some built-in variability (coef-
ficient of variation of approximately 25%). Temporal modula-
tion of the source strengths (50% CV for most) was found to
be essential in being able to resolve the sources by PMF or
UNMIX. A total of 366, 24-h samples were generated at the
receptor site.
There was further discussion regarding MDLs. Data below
the MDL should be noise with no structure. What does it mean
to quote a value below the MDL? Some laboratories report
values and uncertainties only for data above the MDL, while
other labs (and the IMPROVE network on occasion) report
values below MDL. Lewis presented EPA documentation
reflecting the EPA view that it is perfectly allowable to report
sub-MDL values (at least in the AIRS database for VOCs). See
Appendix 6, quote from JAWMA 4£, 71 (1998).
Session 2B: Processing of Synthetic
Data and Resulting Solutions for PMF
Dr. Phil Hopke
(Full presentation is in Appendix 2B.)
Dr. Hopke described how the synthetic data set was analyzed.
Initial trials with PMF yielded low Q values indicative of incorrect
weighting of the data. Alternative data weights were evaluated
until the Q values became more reasonable (approximately equal
to the sample size). At this point, plots of residuals are very
helpful in determining the optimum number of factors. Generally.
residual peaks that are broad for a whole suite of elements imply
die need for more factors; residual peaks that are positively
skewed imply the need for another factors); residual peaks that
are negatively skewed imply the need for fewer factors. PMF with
nine factors seemed to yield the best results. Trials with eight
factors left some residual peaks with positive tails, while PMF
with 10 factors failed to extract a physically interpretable 10th
factor. Scatterplots of predicted mass versus the actual mass reveal
whether PMF results consistently underpredict or overpredict the
known mass and may provide additional guidance on whether the
optimal number of factors has been used. The PMF model was run
multiple times starting with totally random source profiles to
ensure there was a robust solution.
Session 2C: Processing of Synthetic Data
and Resulting Solutions for UNMIX
Dr. Ron Henry
(Full presentation is in Appendix 2C.)
Dr. Henry typically begins an UNMIX analysis with
graphical analysis of the data. UNMIX provides the ability to
10
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view scanerplots of the data. Scatterplots of all species versus
mass are very useful in choosing those species that influence the
mass and should be included in the analysis. Henry looks for
straight lines between species, which can suggest a common
source. He also tries to select species whose scanerplots yield
well-defined edges. Scanerplots can also be used1 to identify
outliers in the data, which can be removed if desired.
Henry typically runs UNMIX multiple times, varying the
fitting species and'oi the number of factors. UNMIX will
consistently extract the major sources, but the minor'sources
come and go during successive runs. Wind-frequency plots can
be helpful in locating and identifying sources, even weak sources
that cannot be quantified. Based on these plots, Henry located
his Palookaville sources as follows: residual oil combustion
(10-30 degrees): incineration combustion (broad, 30-50 and
60-80): Se source (broad, 20-40); steel sinter (200-220);
aircraft jet fuel (200-220): asphalt roofing (210-230); Pd source
(260-280): Mg source (215-235). Interestingly, the location for
the airport (aircraft jet fuel source) determined by Henry
disagreed with the airport location as shown on the Palookaville
map (see Appendix 1C), which placed the airport north of the
receptor. Subsequent examination of the synthetic data set
simulation by OAQPS revealed that the airport, asphalt roofing
manufacture, and steel sinter sources were, in fact, inadvertently
located in the same place—about 200 degrees from north, just as
found by Henry and in subsequent wind-direction analyses by
Hopke.
Session 20: Description of Metric of
the Goodness of Fits of the Solutions
and the Results of Applying the Metric
Dr. Basil Coutani, OAQPS.
(Full presentation is in Appendix 2D.)
Dr. Coutant discussed goodness of fit (GOF) metrics
developed by EPA to determine how well the tools reproduced
the "known" profiles and'br contributions. Ideally, one would
like a single GOF number that can indicate how closely the
model results approximate the profile matrix or the contribution
matrix. Two GOF metrics were described—a mean based and a
median based, both of which measure the relative error in the
apportioned species mass from a source. Both metrics sum these
relative errors for the largest three sources only.
Both metrics were applied to the PMF and UNMIX
synthetic data set solutions. The mean- and median-based GOFs
yielded substantially different results. In particular, the mean-
based metric is very sensitive to the largest relative errors. In
these metrics developed by Coutant, all species are treated
equally (no weights). There was some discussion as to the merits
of (1) unequal weighting of species and (2) making the metrics
independent of the number of fitting species. In addition to GOF
merries for the source profiles, Coutant described GOF metrics
for (1) the source contribution matrix and (2) the raw data, and
discussed the results of applying these metrics to the PMF and
UNMIX solutions. Coutant presented an algorithm intended to
automatically identify source profiles generated by UNMIX or
PMF. For a given source profile, the algorithm finds the best
match from a list of candidate profiles. (These might come from
the SPEC1ATE source profile library, for example). The
automated profile identification algorithm was applied to the
PMF source profiles with promising results. The algorithm
works better as more species are included. A minimum of 30
species is recommended. Some of the audience expressed
concern about making such a too) available to inexperienced
receptor modelers, while others felt that such a tool could assist
even experienced receptor modelers in coming up with a short
list of potential source identifications. There followed some
discussion of the quality and reliability of SPEC1ATE source
profiles. SPEC1ATE profiles certainly have error associated with
them; are these errors considered in the spectral matching
algorithm? In some cases, automated source identification using
the SPECIATE library might be a step backward compared to
reliance on knowledge of local sources. Coutant concluded that
"the profile GOF metrics have worked well: they let one
objectively identify sources, [and] they provide a systematic way
of measuring the overall quality of the fit."
Session 2 concluded with a general discussion and questions
for the presenters. Henry responded to a question about physical
constraints in UNMIX. UNMIX presently does not allow the
user to impose constraints on the source profiles (e.g., the user
may know from experience that a certain species is absent from
a source), but this could be implemented in future versions. PMF
presently has only non-negativity constraints built-in, but it is
possible through the regularization functions to force specific
source contributions or profile components toward zero. Henry
expressed his concern that the errors in both tools are not being
properly estimated. As a next step in model validation, Henry
proposed development of a synthetic data set with variable
source profiles and more realistic error structure. UNMIX and
PMF should be run on 1000 different data sets and the errors
estimated by the models should be compared with the standard
error of the synthetic data set to see if the model error estimates
are realistic.
There was some discussion regarding how well the models
deal 'with secondary' aerosols. Basically, secondaries are a
challenge for the models. In the case of regional transport, one
might be able to combine UNMIX or PMF with back-trajectory
methods or regional transport models. Stratifying the ambient
data set by season and'or wind direction may improve the
apportionment of secondaries; however, one must be careful not
to make the data sets too small in the process.
Asked how Henry and Hopke view each other's model,
Henry reiterated his philosophy that it is best to do as little as
possible to the data and let the data speak for itself. He
11
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expressed his concern that by weighting the data as PMF does,
one runs the risk of puning additional distance between the
statistical model and the physical reality. Hopke argues that the
ability to weight individual data points allows the modeler to
extract the most information from the data.
12
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Session 3
15 February, a.m.
Session 3 began with a description of the Phoenix area and
results from three earlier source apportionment studies. This was
followed by results from an independent analysis of the same
Phoenix data set to which the UNMIX and PMF models were
applied. The session concluded with thorough discussions of the
UNMIX and PMF results from Phoenix, including steps used to
preprocess the data to identify potential outliers, selection of
species and number of sources used in the model, estimates of
confidence (error bars) in the source compositions and con-
tributions, and degree of fit obtained.
Session 3A: Phoenix Source
Apportionment Studies
Mark Hubble, Arizona Department of Environmental Qualirj,'
(Full presentation is in Appendix 3A.)
Mark Hubble described the Phoenix geography, meteor-
ology, and major emissions sources. Hubble also presented
results from three source apportionment studies carried out in the
Phoenix area:
1. 1989-1990 Urban Haze Study (principal investigators:
John Watson and Judith Chow, Desert Research
Institute)
2. 1994-1995 Maricopa Association of Govemments/DRJ
Brown Cloud Analysis (principal investigators: Tom
Moore et al.. Arizona Department of Environmental
Quality, and Eric Fujita, Desert Research Institute)
3. 1994-1996 ADEQ/ENSR Analysis (principal investi-
gators: Tom Moore et al, Arizona Department of
Environmental Quality, and Steven Heisler, ENSR)
The first two studies were conducted during the fall and winter,
while the last study was conducted during all seasons. The
Urban Haze Study used conventional chemical mass balance
(CMB7) to apportion fine mass (PM2S) and light extinction to
source categories. Local motor vehicle and geological source
profiles were generated. The Brown Cloud Study used con-
ventional and extended CMB to apportion fine mass only. The
extended CMB included selected semivolatile organic com-
pounds and polycyclic aromatic hydrocarbons to separately
apportion gasoline and diesel combustion. The ADEQ/ENR
Study used conventional CMB to apportion fine mass and light
extinction.
Results from the first two studies were in general agree-
ment and showed that motor vehicles contributed the bulk of
PM, 5 (in the range of 44-75%) and that geological sources
were typically the second most abundant source of (PMj,),
accounting for approximately 10-20% of PMi5. Ammonium
nitrate and ammonium sulfate were smaller but significant
contributors to PMJS. The third study differed from the first
two studies in that it was conducted year-round and it
attempted to apportion vegetative burning using soluble
potassium. The apportionment results showed a significant
increase in vegetative burning (11-17% of PM25) and
geological sources (26-33%) at the expense of motor vehicles
(typically <40%of PMJ5). However, the vegetative burning
source is probably overestimated since the model indicates that
it contributes 15-20% of PM35 during the summer months,
when there should be little vegetative burning.
In conclusion:
• ' All studies show that most fine mass comes from
combustion.
• All show similar proportions between geological and
combustion source categories.
• All show rather low contributions from secondary
nitrate and sulfate.
13
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Session 3B: Phoenix NERL Platform
Studies—Data Quality Issues and
Supplementary Analyses
Dr. Can- Harris. KERL
(Full presentation is in Appendix 3B.)
Dr. Morris discussed the following topics in regard to the
Phoenix NERL platform data:
• NERL Platform data (measurements, sampling equip-
ment)
• Receptor modeling results
• Scanning electron microscopy results
• Health effects studies
The NERL monitoring platform in Phoenix provided data
that was submitted to Drs. Henry and Hopke for the UNMIX and
PMF analyses. The data consisted of collocated measurements
from a dual fine-panicle sequential sampler(DFPSS), a dichoto-
mous sampler, TEOMs, and a 10-m meteorological tower. The
DFPSS data were the subject of analysis unless otherwise noted.
The data were collected between 1 February J995 and 30 June
1998. Norris et al. carried out their own chemical mass balance
receptor modeling study, which has recently been submitted for
publication. This study attributes 42.2% of PM2} to motor
vehicles, 24.5% to road dust, 17% to secondary organics, 9.5%
to ammonium bisulfate, 5.4% to wood smoke, and 1.4% to
marine aerosol. Norris suggested that secondary organics may
represent a positive artifact on the quartz filter, which may
accour for some of Hopke's "biomass burning" source and
Henry ,* "secondary" source.
Scanning electron microscopy was used to validate the
receptor model results and to provide evidence for additional weak
sources. For example.back-trajectories pointing toward the Pacific
combined with SEM images of sah aerosols provided confirmation
of the marine source. SEM also identified particles suggestive of
smelting operations and an unrelated source(s) of Pb particles.
Health effects associated with the Phoenix aerosol were
analyzed in a recent study by Mar et al. (Associations between
Air Pollution and Mortality in Phoenix, 1995-1997). Cardio-
vascular mortality was significantly associated with PM;},
coarse PM, and elemental carbon. Factor analysis revealed that
combustion-related pollutants (motor vehicle exhaust and
vegetative burning) and secondary aerosols (sulfates) were
associated with cardiovascular mortality'.
Session 3C: PMF Analysis of Phoenix Data
Dr. Phil Hopke
(Full presentation is in Appendix 3C.)
Dr. Hopke discussed his PMF analysis of the Phoenix data.
Hopke found a six-source model for Phoenix. In order of
descending mass contribution, these sources were biomass
burning, motor vehicles, coal-fired power plant, soil, Cu smelter,
and sea salt. Time-series plots of the six sources showed reason-
able seasonal trends. Sea salt and soil were episodic in nature;
motor vehicles, biomass burning, and perhaps the Cu smeller
source appear to peak in winter. Wind-directional analysis of the
copper smelter source might clarify whether this is being
transported across the Mexican/US, border. Because PMF
allows the user to fill in missing data or replace sub-MDL data.
Hopke was able to use Na, CI, and Cu species to advantage in
extracting the sea-salt and copper smelter sources, in contrast to
the UNMIX solution.
Determining the number of factors to include in the model
is a multistep process. After obtaining a trial PMF solution, the
total mass (PM.,) is regressed on the source contributions to
apportion the mass to each of the sources. If any of the
coefficients in this regression are negative, then there likely are
too many factors in the model. Another technique for evaluating
the number of factors is to examine the standardized residuals by
species. If these residuals are not symmetric or if there are a
number of residuals more than three standard deviations from
the mean, this may indicate there are too many or too few factors
(although it may also indicate that the uncertainties provided to
PMF by the user are not appropriate).
Once the number of factors has been determined, then the
correct rotation for the solution needs to be determined. One
easy way to rotate the solution is through the parameter
FPEAK. Graphing Q against different values of FPEAK is a
useful diagnostic for selecting the appropriate rotation. As a
general rule of thumb, one should increase FPEAK until Q
starts to rise.
Although the selection of the number of factors and the
appropriate rotation are presented here as independent steps,
they, in fact, interact. For example, after selecting FPEAK, one
should reexamine the residuals to be sure they are still small and
symmetric and reexamine the regression coefficients to be sure
they are still non-negative.
As an aside, Hopke separately applied PMF to data col-
lected with the DFPSS and to data collected with the collocated
dichot sampler. The results lent support to the modeling results
since the resulting source profiles for the two samplers looked
very similar with the exception of sea salt and soil. These
typically represent coarse-fraction intrusion and were affected by
the different inlet efficiencies for the two sampling systems.
14
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Note: An eight-source model, whose results differ con-
siderably from the six-source model presented at the workshop
and are much more similar to the UNMIX results, has been
submitted for publication (Ramadan et al., JAW MA, in press).
Session 3D: UNMIX Analysis of Phoenix Data
Dr. Ron Henry
(Full presentation is in Appendix 3D.)
Dr. Henry discussed his six-source solution for Phoenix
using UNMIX. He excluded Na, Cl, and Cu from the list of
fining species because scatterplots versus mass indicated that
little mass was associated with these species. Also, there were a
large number of measured values below the detection limit.
Henry's six sources in order of decreasing mass contribution
were non-diesel vehicles (37%), secondaries (20%), soil (15%),
diesels (10%), vegetative burning (5%), and unexplained (12%).
In contrast to Hopke, Henry used soil-corrected potassium as a
fining species. The correction was made by using the lower edge
in the potassium versus silicon scatterplot as an estimate of the
soil potassium. Non-soil potassium proved to be very important
in being able to extract the weak vegetative burning source. The
secondaries source was high in S and organic carbon. The
unexplained source, distinguished by Br and OC, is probably a
mixture of sources according to Henry- (Phoenix has a surprising
number of local OC sources according to Henry, although
regional transport of OC is another possibility'.) Several factors
supported Henry's labeling of the diesel source. First was the
high EC component. Second, Henry compared the diesel
contributions on weekdays versus weekends and found nearly a
factor of 4 decrease on the weekends, consistent with com-
mercial truckers' reluctance to work on weekends. (The other
sources, if anything, may have shown a tendency toward higher
contributions on the weekends.) Third, some research on the
Internet indicated that it is common practice among truckers
(though possibly illegal) to add MMT(an octane-enhancing fuel
additive) to their fuel to minimize engine fouling. This could
then explain the targe Mn component in the diesel source profile.
Unfortunately, no traffic count data were available in the
Phoenix area showing the number of diesel vehicles on
weekends versus weekdays. Henry also presented the 1-sigma
source composition errors that can be generated by UNMIX. By
dividing each contribution in the source profile matrix by its
associated error, one calculates the normalized signal-to-noise
values for the source profiles. With the exception of vegetative
burning (the weakest source), the great majority of these values
are greater than 2.
Time-series plots of the six sources showed reasonable
seasonal cycles. Vegetative burning and non-diesel vehicle
sources peaked in winter, while the secondaries peaked in
September-October. "Unexplained"' had no discernible pattern.
In contrast to the synthetic data set, wind-directional plots
showed little directionality to the sources, and any directional
trends that did show up were probably driven by seasonal
changes in wind direction. (Winds are more likely to come from
the north during the winter and the top 10% samples for the
vehicle source are most likely to occur in the winter, so the
wind-direction plot for the vehicle source will be skewed toward
the north.)
As an aside, Henry included PM10 and PM2 5 masses from
collocated TEOM samplers in the UNMIX model and generated
a seven-source solution. Six of the sources reproduced the
.previous six-source solution very well. In addition, the DFPSS
fine mass and the TEOM fine mass apportioned to each of the
six sources were in remarkable agreement. The additional
seventh source appeared to be associated with PM,0.
Session 3 concluded with a brief comparison of the UNMIX
and PMF solutions to the Phoenix data as summarized in the
following table:
PMF
Biomass burning
Motor vehicles
Coal-fired power
plant
Soil
Smelter
Sea salt
35%
28%
17%
15%
4%
1%
UNMIX
Non-diesel
Secondary
Soil
Diesel
Vegetative burning
Unexplained
37%
20%
15%
10%
5%
12%
There were some major differences in the two solutions. The
largest source in the PMF solution was biomass burning,
accounting for nearly 35% of the mass. By comparison,
UNMIX's vegetative burning accounted for only 5% of the
mass. It is worth noting that Henry used non-soil K to extract his
vegetative burning source, while Hopke did not. Hopke
speculates that his biomass burning source may be a combination
of Henry's diesel and unexplained sources, which account for
about 22% of the mass. Motor vehicles account for about 28%
of the mass in PMF versus 47% in UNMIX (combining diesels
plus non-diesel). Based on profile similarities, Hopke's coal-
fired power plant source, accounting for about 17% of the mass,
appears to be equivalent to Henry's secondaries source, repre-
senting 20% of the mass. Hopke's soil source accounts for about
15% of the mass, the same as Henry's soil source estimate.
15
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Session 4
15 February, p.m.
Session 4 included a reconsideration of the synthetic data
results, discussions of how the tools really work, and live
demonstrations of PMF and UNMIX by Hopke and Henry.
Session 4A: Reexamination of
the Synthetic Data Results
Shelly Eberly
Eberly reviewed the PMF and UNMIX results for the
synthetic data set and made some corrections. Specifically,
UNMIX identified four sources larger than noise, including the
municipal incinerator source (identified by Henry as solid
material combustion) with an estimated strength of 4 ug/m'.
Both tools tended to overestimate the contributions from the
minor sources. Henry explained that this is simply a con-
sequence of the fact that both tools attempt to explain all of the
observed mass with only seven or nine sources rather than the 16
sources that were used to generate the synthetic data. Therefore,
some of the source contributions will necessarily be over-
estimated. Henry emphasized the need to put error bars on
estimated source contributions when comparing results from
different tools.
Other issues pertaining to the synthetic data results included
the actual location of the airport in Palookaville. With regard to
putting labels on sources, Henry encouraged modelers to provide
a one-sentence justification for each source label so that readers
will understand how the sources were identified.
Session 4B: Demonstration
of UNMIX Program
Dr. Ron Henry
Dr. Henry presented a live demonstration of the UNMIX
program. UNMIX is copyrighted to Henry. The current version
(UNMIX2.1) is available at no charge from Dr. Henry, v/no
requests that users not distribute the program to others. E-mail
Dr. Henry at rhenry@usc.edu to request a copy. In addition to
the program, users will receive a user's manual (PDF format)
and some test input files. Users must have MatLab 5.3 in order
to run UNMIX.
Ambient data is input to UNMIX as a flat ASCII text file with
column headings. UNMIX has a user-friendly Windows interface.
UNMIX provides some statistical measures to guide the user
toward the best solution. These include minimum r-square (r) and
minimum signal-to-noise (S *N). Recommended values are r3 > 0.8
and S/N > 2. UNMIX allows the user to set one species as a
"tracer" if desired. This forces all measured mass for that species
into one source. UNMIX has an option for displaying scatterplots
of any species against any other species. This is very usefiil in
selecting fining species. In the same plots one can identify outliers
and remove them (temporarily) from the data set. One can also
display "edge" plots. Henry recommends this as a good way to
find out which species are important. In selecting fitting species,
Henry had the following suggestions: (1) Major species must be
included or die model won't be able to find a solution. (2) Select
"robust" species—i.e., those with few missing or sub-MDL values.
(3) Use as few species as possible, since each additional species
adds error to the analysis and usually degrades the S/N. UNMIX
outputs include the source composition matrix and the source
contributions. Additionally, UNMIX can estimate errors in the
UNMIX source compositions using a bootstrap approach in which
the model is applied to 100 random subsets of the data. "UNMIX
overnight" is another useful feature that allows the user to try all
possible subsets of a selected set of fitting species in order to find
the optimal solution. This can be a lengthy process and the user
will probably want to limit the number of candidate species to
seven or less.
Future improvements that Henry would like to see include
(1) a stand-alone version that would not require MatLab and
could potentially run much faster, (2) the ability to input
constraints on source compositions, and (3) the ability to save
16
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"fining sessions" with all pertinent information so users can
remember where they've been or reproduce earlier analyses.
Asked whether the quoted uncertainties in the ambient data
could be used to some advantage, Henry reiterated his philo-
sophy that it is best to assume that you know nothing about the
data and that, in his experience, uncertainties are often meaning-
less. Nevertheless, Henry did not entirely rule out the possibility'
that future versions of UNMIX may try to use the information
present in the quoted uncertainties.
Session 4C: Demonstration
of the PMF Program
Dr.PhilHopke
The PMF programs are available from Dr. Pentti Paatero via
the ftp site rock.helsinki.fi/pub'misc/pmf. First-time users can
get PMF for a 6-month free trial period after which there is a
license fee. PMF is still primarily a research tool and does not
have a nice graphical interface. Researchers interested in learn-
ing to use PMF are invited to spend a week with Hopke at
Clarkson University.
PMF can be run through a programmer's file editor (PFE),
which is free shareware downloadable from the Internet. Every
PMF job begins by setting up an '.INI file, which contains all
the parameters needed for the analysis, including the file names
and paths for input data files.
The output of PMF includes three matrices: the G matrix of
source contributions, the F matrix of source compositions, and
the matrix of residuals. PMF also outputs a text file containing
a log of the current analysis session. The G matrix can be input
to a statistics program in order to cany out the regression versus
mass to get the scaled source contributions. The PMF program
has no built-in diagnostic tools (e.g., for displaying residual
plots).
Looking to the future, the PMF program may not be refined.
Instead, programming efforts may be directed entirely into the
Multilinear Engine (ME) program, which Hopke sees as
replacing PMF (Paatero, 1999). ME is considered more flexible
in its ability to handle the imposition of physical constraints. A
wish list for future versions of ME includes a much more user-
friendly graphical interface, the ability to input fixed source
profiles or ratio constraints (e.g., Al:Si ratio), and a stand-alone
version with built-in diagnostics (e.g., residual plots), which will
obviate the need to export results to other software packages.
Hopke speculated that it might be possible to automate to some
extent the search for the optimal FPEAK by, for example,
increasing FPEAK until there is a substantial rise in Q.
Further discussion of MDLs revealed a general consensus
that there is considerable lack of agreement on the meaning of
MDLs and how they are reported by various labs. Lewis
provided the following definitions of the limit of detection
(equivalent to the MDL) and limit of quamitation:
From Lloyd Currie, pg. 289, in uX-Ray Fluorescence
Analysis of Environmental Samples," T.G. Dzubay,
ed., Ann Arbor Science (1977):
Limit of Detection = 3.29 o0
(fdse positive risk = 5%,
false negative risk = 5%)
Limit of Quantitation *=*= 10 f O0
(RSD of measured
concentration = J0%)
where o0 «= (1.0 - 1.4) x standard deviation of blank
and f - 1
It was noted that the above definitions define methodltmhs,
as distinguished from sample limits. The latter vary from sample
to sample and are more realistic limits because they include the
effects of spectral interferences due to other analytes present in
the particular sample. Some labs report the fixed-method MDLs,
and some report variable-sample MDLs. Also, some labs report
values below the MDL, while others do not. Some statisticians
argue for reporting only raw values plus uncertainties and
dispense with the concept of MDLs. Hopke is currently
investigating the use of a statistical method known as "multiple
imputation" as a way to use existing data to impute missing data,
but this research is in a preliminary stage. The discussion did not
lead to any resolution of the difficult issue of how best to handle
and report nondetected values.
Session 4D: Potential Effects of Data
Artifacts on Receptor Modeling Results
Rich Poirot, Vermont Department of Environmental
Conservation
(Full presentation is in Appendix 4D.)
Data artifacts, which can include measurement errors, uncer-
tainties, and various hole-filling replacements for nondetects, can
interfere with the identification of real sources. Poirot discussed
his experience with UNMIX and dealing with nondetect data.
There are two choices for dealing with nondetects: one can censor
die input data to screen out all nondetects, or one can use some
hole-filling techniques to replace nondetects. The former approach
can create a small and biased subset of the original data. Poirot
discussed the results of using various hole-filling techniques to
modify the input data for UNMIX calculations. In the end, Poirot
17
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fell that simple replacement of nondetect values with zeros (or
some small constant) yielded the most consistent and imerpretable
UNMIX results.
Poirot showed a series of slides lending support to those who
mistrust reported uncertainties and MDLs. For example, Ni and As
measurements at Lye Brook, VT, are totally uncorrelated and yet
the reported uncertainties exhibit a significant positive correlation
(top figure below). Also, he said, "although concentrations of Ni
and As are uncorrelated, their MDLs are highly correlated, both
as a function of three methods changes in different time periods,
and also within each of three different reporting periods"
(bottom figure below).
3 "Sources" of Arsenic and Nickel« Lye Brook, VT V81-5/99
Time Series of Arsenic « Nickel MDL * Lye Brook, VT: 8/91-5/99
0.7 i
1 51 101 151 201 251 301 351 401 451 501 551 601 651
Sample* (1-693)
18
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Poirot went on to say that "(this is] possibly due to common
interferences or instrumental drift, but not due to changing
ambient concentrations. Generally, in most long-term measure-
ment programs both ambient concentrations and detection limits
are likely to decrease over time, creating the possibility of false
positive correlations between source activity for some elements
and lab activity for other elements." This latter point was
elegantly demonstrated by a plot of same-day, above-MDL As
concentrations at Acadia and Mt. Rainier IMPROVE sites. The
measured concentrations exhibit no correlation (as expected
given the continental distance between sites). However, same-
day As MDLs for these sites are correlated, generally due to
"progress"' (improving detection limits over time) in the 10+
year IMPROVE network. Poirot also provided evidence for
"misquamified" MDLs for Al in IMPROVE data. He presented
some encouraging results, which showed that despite wide
differences in data preprocessing and model input, both UNMIX
and PMF identified three common sources in an IMPROVE-like
data set. However, artifacts associated with changes in Se MDLs
due to a change from P1XE to XRF analysis during the sampling
period clearly influenced the UNMIX and PMF results in
different ways.
Poirot concluded by saying. "Data Artifacts, including
MDLs and uncertainties as reported by labs and/or as processed
by data analysts, can and do influence receptor model results."
Session 4E: Open Discussion
There was further discussion of the MDLs. It was not
known whether the EPA PM. 5 Speciation Monitoring Network
will report the single method-based MDLs or the daily-varying
sample MDLs. Henry reconsidered his distrust of reported
MDLs and uncertainties and found it to be justified. In situations
where one cannot afford to lose data due to nondetects, Henry
recommends just replacing the nondetects with zero or a small
constant.
Important MDL-relaied questions include the following:
How are MDL and uncertainty values determined by analytical
laboratories? Do these reported values have the same meaning
at different labs or in different measurement programs? How
have analytical methods and the resultant data changed over the
course of a measurement program? And finally, what are the best
ways of processing th is information as input to different receptor
models?
In response to the question of whether or not to use mass as
a fining species, Henry and Hopke expressed different
philosophies. Henry likes to include the mass so that the total
mass is apportioned just like the species mass. Hopke has
traditionally kept the mass separate and likes to use the results of
the mass regression analysis as an added check on the validity of
the model results.
Henry expressed his concern that the errors reported in both
UNMIX and PMF have not been given adequate scrutiny. Hopke
believes that the error estimates in PMF are almost certainly
overestimates.
Several members of the audience commented on the dreaded
UNMIX message informing the user that there was "no feasible
solution" to a problem. Henry responded that rather than viewing
this as a bug or deficiency in UNMIX, it should instead be
viewed as a valuable feature in that a bad solution is worse than
no solution.
There was some discussion about dealing with outliers.
Henry relies heavily on UNMIX scatterplots to identify outliers.
He urged caution in eliminating suspected outliers because, if
real, they can provide very important information about source
compositions. Hopke typically does a principal components
analysis of the data and plots factor scores to identify outliers.
The "robust mode" option in PMF automatically down weights
outliers (but does not eliminate them) so that they do not exert
too much influence. If the user knows that a certain sample is an
outlier (e.g., fireworks on the Fourth of July), then it is best to
remove that data point before performing UNMIX or PMF
analysis.
The interpretation of source profiles remains one of the
biggest challenges in using these tools. Receptor modeling
should not be done in a vacuum. Ideally, the modeler will have
intimate knowledge of the modeled airshed, or will work closely
with someone who does. Rich Poirot suggested creating an
informal, unofficial bulletin board or site where modelers could
share source profiles (accompanied by some descriptive
information) generated by UNMIX or PMF. Lewis would like
modelers to show their profiles in publications. With emphasis
on PM: „ there is likely to be increased mass being apportioned
to regional sources, which are typically dominated by secondary
species. It would be useful to compile a library of regional
"fingerprints." Such a library could be helpful in proper source
identification. There are some good tools such as residence time
analysis, back-trajectory analysis, and partial source contribution
function (PSCF) analysis for identifying and quantifying
regional impacts. Hopke showed how PSCF was able to trace a
Ni-V factor in Vermont back to residual oil combustion in the
Eastern urban corridor.
19
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Session 5
16 February, a.m.
Session 5A: Application of PMF
in the Northern Great Lakes:
A Tale of Two Studies
Dr. Kurt Paterson, Michigan Technological University
(Full presentation is in Appendix 5A.)
Dr. Paierson presented an overview of two studies
conducted in the Northern Great Lakes in which PMF was
applied. The first study involved source apportionment of a
mixture of trace gases and paniculate matter in order to identify
the sources that influence air quality in the northern Great Lakes.
PMF extracted three sources identified by Paterson as biogenic
(defined by isoprene). local, and regional transport. Paterson
combined PMF with residence time analysis, met data analysis,
and time-series analysis to confirm the identification of the
sources. In the second study PMF was used on panicle size
distribution data, not to apportion sources, but to extract distinct
factors that could reveal the dynamics of different panicle
modes. The original data comprised 100 size ranges from 5 nm
to 7.5 urn and 1046 half-hour samples. PMF collapsed this data
into six factors, which fell out into distinct panicle size ranges
and which exhibited different dynamic properties. Two factors,
for example, showed strong diurnal cycles, Two factors were
most influenced by long-range transport. And PM, < mass was
most influenced by panicles in the size range 220-800 nm. The
chemical composition data for these samples are now available
and Paterson will repeat these analyses, adding in the com-
position data and using both PMF and UNMIX.
Session SB: Discussion of FPEAK, Open
Discussions, and Workshop Conclusion
Depending on the input data set, PMF may generate
multiple solutions that are all equally valid within the rotational
ambiguity of the PMF model. Somehow the user must decide
which rotation is the best. FPEAK is one parameter available in
PMF that allows the user to try "arious rotations. Positive
FPEAK values force the source composition matrix toward more
extremes (zeros for some species and large percentages for other
species) and the source contribution matrix toward less
extremes, while negative FPEAK values produce the opposite
effect. Eberly presented a simple example (seven samples, three
species, two sources) to show the effect of FPEAK (see
Appendix 5B). PMF was executed with FPEAK values of-0.5,
0.0. and 0.5, and the resultant source composition and con-
tributions were presented. All three of these possible solutions
are consistent with the measurements recorded at the receptor,
that is, the masses balance. Examination of the solutions shows
that (1) for the negative FPEAK value, the source contributions
are the most extreme, including some days when one source is
not contributing, and (2) for the positive FPEAK value, the
source compositions are the most extreme, including a species
whose proportions are 0.01 and 0.85.
UNMIX was also run on the simple example and the results
were presented. UNMIX produces only one solution and this
solution had compositions and contributions similar to those
from PMF where the FPEAK value was -0.5. The reason for this
is that the UNMIX algorithm assumes there are days when each
source is not contributing to the receptor. That is, UNMIX seeks
sources for which there are some contributions near zero, and
this is similar to what PMF does with negative FPEAK values.
As mentioned, a requirement of UNMIX is that there must
be sampling days when each source disappears or is insig-
nificant. How does UNMIX handle a source like motor vehicles
in Washington, DC, which never turns off? Henry responded
that this was the reason for putting the "tracer" option in
UNMIX. This option allows the user to select one species as a
tracer. This constrains the UNMIX solution by forcing all of the
tracer species mass into one source. For motor vehicles, Henry
recommended using CO as a tracer (not perfect, but usually good
enough). Without a tracer in this case, UNMIX may not find a
feasible solution.
Is there a rule of thumb for the number of samples needed
by UNMIX or PMF? It is really a signal-to-noise problem. PMF
has been applied to as few as 40 samples, but typically there is
not enough variability present in so few data points to be able to
pull out distinct factors. Recent work by John Ondov (PM2000
20
-------
Charleston Conference) has shown that by sampling with high
time resolution (half-hour) one can dramatically improve the
signal 'noise for sources with temporal variability. Henry offered
the following rule of thumb for UNMIX: 200-300 samples may
get you five sources; 2000-3000 samples may be needed to
extract 9-10 sources.
How can receptor modelers take advantage of the EPA
Speciation Monitoring Network now coming online? What tools
are available to interpret these data? Instead of modeling
multiple species at a single site, one can model a single species
across multiple sites. In this way, one can extract spatial
concentration gradients, which, combined with wind-direction
analysis, can identify source locations. Alternatively, one can
model multiple species at multiple sites using three-way factor
analysis (Hopke et al.. 1998).
A member of the audience pointed out the discrepancies in
the UNMIX and PMF solutions for the Phoenix data, most
notably the mass apportioned to vegetative burning in the two
models. Are such discrepancies the result of applying different
models, or the result of different people interpreting the same
information? Hopke responded by saying that the modeler needs
to tap into the local expertise to help identify' important sources
and to screen out unreasonable solutions. It is always good to
come at a problem with as many tools as possible. If one can get
similar solutions using both PMF and UNMIX, this adds
confidence to the results.
Henry proposed a strategy that combines UNMIX and PMF
and should yield defensible solutions. In this combined ap-
proach, the modeler might start with UNMIX to estimate the
number of factors and to get good starting source profiles.
UNMIX profiles could be used as starting profiles for PMF,
since PMF is particularly good at finding smaller sources and
including additional species. (This will shorten the PMF analysis
since the model does not have to start with random profiles.)
Applying other information such as wind-direction plots, one
can probably come up with 10 or more sources. The abi'ity to
look at residuals in PMF can be very helpful as a quality' check
at the end of the modeling process.
Kurt Paterson suggested that it would be very useful to have
thorough training tutorials for both PMF and UNMIX showing
detailed applications of the tools in actual case studies.
There was a broad discussion regarding the roles of regional
planning bodies and state regulatory agencies in dealing with
compliance issues. Within a few years, regulatory agencies will
need to address reductions at both the regional and local levels,
with the regional planning bodies probably taking the lead. Will
the state and regional agencies have the resources and the
expertise to utilize the latest modeling tools? How can PMF and
UNMIX be used to separate the regional from the local sources?
Can the IMPROVE and Speciation networks be combined in
some way to help separate regional sources from local sources?
Henry responded that there presently exist a handful of good
tools for dealing with regional sources. The challenge is for
someone to put these tools together and make people aware that
they exist. Perhaps the EPA regional offices can play a role in
disseminating information about these tools or providing training
to state agencies.
21
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References
PMF Publications
Anttila, P., P. Paatero. U. Tapper, and 0. Jarvinen (1995)
Application of Positive Matrix Factorization to Source
Apportionment: Results of a Study of Bulk Deposition
Chemistry in Finland. Aimos. Environ. 29:1705-1718.
Chueinta. W., P.K. Hopke. and P. Paatero (2000) Investigation
of Sources of Atmospheric Aerosol Urban and Suburban
Residential Areas in Thailand by Positive Matrix Factori-
zation. Atmos. Environ. In press.
Garrido Frenich. A.. M. MartinezGalera, J.L. Martinez Vidal,
D.L. Massan. J.R. Torres-Lapasio. K. De Braekeleer, J.H.
Wang, and P.K. Hopke (2000) Resolution of Multi-
component Peaks by OPA, PMF and ALS, Anal. Chim.
X«fl411:145-155.
Hopke, P.K.,P. Paatero. H. Jia, R.T. Ross, and R.A. Harshman
(1998) Three-Way (PARAFAC) Factor Analysis: Exami-
nation and Comparison of Alternative Computational
Methods as Applied to Ill-Conditioned Data, Chemom.
Intell. Lab. Syst. 43:25-42.
Hopke. P.K., Y. Xie, and P. Paatero (1999) Mixed Multiway
Analysis of Airborne Panicle Composition Data, J.
Chemom. 13:343-352.
Hopke, P.K. (2000) A Guide to Positive Matrix Factorization,
report prepared for the Office of Air Quality Planning anc*
Standards, U.S. Environmental Protection Agency, under
Contract No. 9D-1808-NTEX, January 2000.
Hopke, P.K. (2000) Application of Source Apportionment
Methods to the State Implementation Planning Process,
report prepared for the Office of Air Quality Planning and
Standards, U.S. Environmental Protection Agency, under
Contract No. 9D-1808-NTEX, January 2000.
Huang. S., K.A. Rahn, and R. Arimoto (1999) Testing and
Optimizing Two Factor-Analysis Techniques on Aerosol at
Narragansett, Rhode Island,X/mos. Environ. 33:2169-2185.
Juntto, S., and P. Paatero (1994) Analysis of Daily Precipitation
Data by Positive Matrix Factorization, Environmetrics
5:127-144.
Lee, E., C.K. Chan, and P. Paatero (1999) Application of
Positive Matrix Factorization in Source Apportionment of
Paniculate Pollutants in Hong Kong, Atmos. Environ.
33:3201-3212.
Paatero, P. (1997) Least Squares Formulation of Robust, Non-
Negative Factor Analysis, Chemom. Intell Lab. Syst.
37:23-35.
Paatero, P. (1999) The Multilinear Engine—A Table-Driven
Least Squares Program for Solving Multilinear Problems,
Including the n-way Parallel Factor Analysis Model, J.
Computational and Graphical Stat. 8:1-35.
Paatero, P., and U. Tapper (1993) Analysis of Different Modes
of Factor Analysis as Least Squares Fit Problems, Chemom.
Intell. Lab. Syst. 18:183-194.
Paatero, P., and U. Tapper (1994) Positive Matrix Factorization:
A Non-negative Factor Model with Optimal Utilization of
Error Estimates of Data Values, Environmetrics 5:\ 11-126.
Paterson, K.G., J.L. Sagady, D-L. Hooper, S.B. Bertman, M.A.
Carroll, and P.B. Shepson (1999) Analysis of Air Quality
Data Using Positive Matrix Factorization, Environ. Sci.
Technol. 33:635-641.
Polissar, A.V., P.K. Hopke, W.C. Malm, J.F. Sisler (1996) The
Ratio of Aerosol Optical Absorption Coefficients to Sulfur
Concentrations, as an Indicator of Smoke from Forest Fires
when Sampling in Polar Regions, Atmos. Environ. 30:
1147-1157.
22
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Polissar, A.V., P.K. Hopke, W.C. Malm, J.F. SisJer (1998)
Atmospheric Aerosol over Alaska: 2. Elemental Com-
position and Sources../. Ceophys. Res. 103:19,045-19,057.
Polissar, A.V., P.K. Hopke, P. Paatero. YJ. Kaufman, O.K.
Hall. B.A. Bodhaine, E.G. Dutton, and J.M. Harris (1999)
The Aerosol at Barrow, Alaska: Long-Term Trends and
Source Locations, Atmos. Environ. 33:2441-2458.
Ramadan. Z., X.-H. Song. P.K. Hopke (2000) Identificaton of
Sources of Phoenix Aerosol by Positive Matrix Factori-
zation. JA WMA. In press.
Xie. Y.-L.. P.K. Hopke. and P. Paatero (1998) Positive Matrix
Factorization Applied to Curve Resolution Problem, J.
Chemom. 12:357-364.
Xie, Y.-L., P.K. Hopke. P. Paatero, L.A. Barrie, and S.-M. Li
(I999a) Identification of Source Nature and Seasonal
Variations of Arctic Aerosol by the Multilinear Engine,
Atmos. Environ. 33:2549-2562.
Xie. Y.-L., P.K. Hopke, and P. Paatero (1999c) Calibration
Transfer as a Data Reconstruction Problem, Anal. Chim.
Acta 384 -.193-205.
Xie. Y. L., P. Hopke. P. Paatero, L.A. Barrie, and S.M. Li
(1999b) Identification of Source Nature and Seasonal
Variations of Arctic Aerosol by Positix-e Matrix Factori-
zation, J. Atmos. Sci. 56:249-260.
Yakovleva, £., P.K. Hopke, and L. Wallace (1999) Receptor
Modeling Assessment of PTEAM Data, Em-iron. Sci.
Technol. 33:3645-3652.
Henry, R.C. (1997) History and Fundamentals of Muhivariate
Air Quality Receptor Models, Chemom. Intell. Lab. Syst
37:525-530.
Henry, R.C., and C. Spiegelman (1997) Reported Emissions of
Volatile Organic Compounds are not Consistent with
Observations, Proc. Nat. Acad Sci. 94:6596-6599.
Henry, R.C., E.S. Park, and C.H. Spiegelman (1999) Comparing
a New Algorithm with the Classic Methods for Estimating
the Number of Factors, Chemom. Intell. Lab. Syst. 48:
91-97.
Kim, B.-M., and R.C. Henry (1999) Extension of Self-Modeling
Curve Resolution to Mixtures of More Than Three
Components. Pan 2: Finding the Complete Solution,
Chemom. Intell. Lab. Syst. 49:67-77.
Kim, B.-M., and R.C. Henry (2000) Application of the SAFER
Model to Los Angeles PM10 Data, Atmos. Environ.
34:1747-1759.
Kim, B.-M., and R.C. Henry (2000) Extension of Self-Modeling
Curve Resolution to Mixtures of More Than Three
Components. Part 3, Chemom. Jntell. Lab. Sysl. Submitted.
Park. E.S., C. Spiegelman, and R.C. Henry (2000) Bilinear
Estimation of Pollution Source Profiles and Amounts by-
Using Receptor Models, Communications in Statistics,
Simulation & Computation, 29(3). In press.
UNMIX and SAFER (the Parent
Model of UNMIX) Publications
Henry, R.C. and B.- M. Kim (1989) A Factor Analysis Model
with Explicit Physical Constraints, Transactions AirPollut.
Control Assoc. 14:214-225.
Henry, R.C.,and B.-M. Kim (1990) Extension of Self-Modeling
Curve Resolution to Mixtures of More Than Three Com-
ponents. Part 1: Finding the Basic Feasible Region,
Chemom. Intell. Lab. Syst. 8:205-216.
Henry, R.C., C.W. Lewis, J.F. Collins (1994) Vehicle-Related
Hydrocarbon Source Composition from Ambient Data: The
GRACE/SAFER Method, Environ. Sci. Technol 28:823-
832.
23
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UNMIX/PMF Receptor Modeling Workshop Attendees
14-16 February 2000
U.S. EPA, RTP, NC
Name
Charles Leu is
Philip K. Hopke
Ron Henry
John Bachmann
Scon Kegler
Mark Hubble
Lara Awry
Donna Kenski
Tom Pace
Lam Cox
N. Dean Smith
Basil Couum
Alan Vene
Anne Rea
Ten Conner
Affiliation
U.S EPA
Clarkson University
University of Southern
California, Los Angeles
US. EPA
L'.S. EPA
Arizona DEQ
U.S. EPA
L'.S EPA
L'.S. EPA
L'.S. EPA
U.S. EPA
U.S. EPA
U.S. EPA
U.S. EPA
U.S. EPA
Phone
919-541-3)54
315-268-3861
2 1 3-740-0596
919-541-5359
919-541-4906
602-207-4481
919-541-5544
3)2-886-7894
919-541-5634
919-541-2648
919-541-2708
919-541-5028
919-541-1378
919-541-0053
9)9-541-3157
E-mail
lewis.charlesw@epa.gov
hopkepkgclarkson.edu
rhenry.gusc.edu
jbachmanngtpa.gov
kegler.scotugepa.gov
mhl Sev.suie.az.us
autn .laragepa gov
'(tnski.donna 2 epa.gov
pace .tomg epa.gov
cox.larry@epamail.epa.gov
smith.deangiepa.gov
coulant.basilgtpa.gov
vene.alan@epa.gov
rea.annffftpa.gov
cormer.terigtpa.gov
Mailing Address
U.S. EPA
NERL (MD-47)
RTP, NC 277 11
Clarkson University
BoxSSJO
Ptisdam, NY 1 3655-58 !"
U.S.EPA
NERL(MD-10)
RTP, NC 277 11
U.S. EPA
NERL(MD-52)
RTP, NC 27711
Arizona DEQ
3033 N. Central Ave.
Phoenix. AZ 85012
U.S. EPA
NERL(MD-I4)
RTP, NC 277 11
EPA Region 5
77 W. Jackson Blvd.
Chicago, JL 60604
U.S. EPA
NERL(MD-14)
RTP, NC 277 11
U.S. EPA
NERL (MD-75)
RTP, NC 277 11
U.S. EPA
NERL(MD-6I)
RTP, NC 277 11
L'.S. EPA
NERL (MD-14)
RTP, NC 277 11
U.S. EPA
NERL(MD-56)
RTP, NC 277 11
U.S. EPA
NERL (MD-S6)
RTP, NC 277 11
U.S. EPA
NERL (MD-46)
RTP, NC 2771)
24
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Name
Tom Braverman
Allan Marcus
Shao-Hang Chu
Ned M ever
Peter Frechtel
Eugene Kim
Tom Rosendahl
Cynthia Howard
Reed
Barbara Parzygnai
Bob Willis
kazlio
Shaibal Mukerjee
John LangsiatT
Melissa Gonzales
Tom Coulter
Rich Poirot
Sieve Fudge
Affiliation
L'.S. EPA
U.S EPA
L.S. EPA
U.S. EPA
U.S. EPA
University of Washington
f.S. EPA
f.S EPA
U.S. EPA
ManTech Environmental
New York University
I'.S EPA
U.S. EPA
U.S. EPA
Phone
9I9-541-J383
919-541-0636
919-541-5382
919-541-5594
919-541-1173
206-526-2909
9)9-541-5314
703-648-5222
919-541-5474
919-541-2809
914-731-3540
919-541-1865
919-967-6649
919-966-7549
919-541-0832
802-211-3807
919-933-9501
E-mail
braverman.tomgepa.gov
maicus.allangepa.gov
chu.shao-hang.gepa.gov
meyer.nedgepa.gov
ftechtel.petcrgepa.gov
ugenegu.washington.edu
rosendahl.tom@epa.gov
howard.c>Dthia2epa.gov
parzygnat.barbarag epa.gox
willis.robcn@epa.gov
kaz@-env.med.nyu.edu
mukerjee.shaibal gepa.gov
jlangstarTS-inindspring.com
gonzales.melissag-epa.gov
coulter.tom@epa.gov
richpog-dec.anr.state.vLus
ftidge.sievegecnveb.com
Mailing Address
U.S. EPA
NER1. (MD-14)
RTP.NC 277 11
U.S.EPA
NERL (MD-52)
RTP.NC277I1
U.S. EPA
NERL(MD-I5)
RTP.NC27711
U.S. EPA
OAQPS (MD-14)
RTP.NC27711
U.S. EPA
OAQPS (MD-14)
RTP.NC27711
U.S. EPA
OAQPS (MD- 15)
RTP.NC277I1
U.S. EPA
12201 Sunrise Valley Dr.
555 National Center
Reston. VA 20192
U.S. EPA
OAQPS (MD-14)
RIP, NC 277 11
ManTech Environmental
P.O. Box 123 13
RTP.NC 27709
New York University, Env. Med.
57 Old Forge Rd.
Tuxedo, NY 10987
U.S. EPA
NERL (MD^7)
RTP.NC 277 11
HOSVallevParkDr.
Chapel Hill, NC 275 14
U.S. EPA
MD-58A
RTP.NC 27711
U.S. EPA
MD-47
RTP.NC 277 11
103 South Main St.
Waterbury, VT 05671
1129 Weaver DairvRd.
Chapel Hill, NC 27514
25
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Name
Shell) Eberly
Jonj; Hoon Lee
Barbara Turpin
Kurt Paierson
Affiliation
L.S EPA
Rutgers University
Rutgers University
Michigan Tech University
Phone
9) 9-54 Ml 28
732-9324)306
732-932-9540
906-487-3495
E-mail
ebfriy.shelly@epa.gov
jhleegatsop.rutgen.edu
turpingaesop.rutgers.edu
Paiersongmtu.edu
Mailing Address
U.S. EPA
NERMMD-14)
RTF, NC 277 II
Rutgers University
14 College Farm Rd.
New Brunswick, NJ 08901
Rutgers University
14 College Farm Rd.
New Brunswick, NJ 08901
Michigan Tech University
Dspi. Civil & Env. Engineering
1400 lownsend Drive
Houghtcn, MI 49931
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