UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON D.C. 20460
OFFICE OF THE ADMINISTRATOR
SCIENCE ADVISORY BOARD
August 30, 2004
EPA-SAB-CASAC-CON-04-005
Honorable Michael O. Leavitt
Administrator
U.S. Environmental Protection Agency
1200 Pennsylvania Avenue, NW
Washington, DC 20460
Subject: Clean Air Scientific Advisory Committee (CASAC) Ambient Air Monitoring
and Methods (AAMM) Subcommittee Consultation on Methods for Measuring
Coarse-Fraction Particulate Matter (PMc) in Ambient Air (July 2004)
Dear Administrator Leavitt:
The Ambient Air Monitoring and Methods (AAMM) Subcommittee of the Clean Air
Scientific Advisory Committee (CASAC) met in a public meeting held in Research Triangle
Park (RTF), NC, on July 22, 2004, to conduct a consultation on methods for measuring coarse-
fraction particulate matter (PMc) in ambient air, based upon performance evaluation field studies
conducted by EPA. Measurement of PMc focuses on those particles in the ambient air with a
nominal diameter in the range of 2.5 to 10 micrometers (i.e., the coarse fraction of PMio).
This project was requested by OAQPS in anticipation of the potential need for reference
and equivalent methods for PMc measurement, should new PMc standards be established as a
result of EPA's ongoing review of the national ambient air quality standards (NAAQS) for
particulate matter (PM). The results of this consultation will support discussion of PMc air
quality monitoring to be included in the next draft of the OAQPS Staff Paper for PM, a policy
assessment of scientific and technical information prepared as part of the PM NAAQS review.
This draft Staff Paper is now planned for review by the CASAC PM Review Panel in early 2005.
As is our customary practice with a consultation, there will be no formal report from the
CASAC or the SAB, nor does the Subcommittee expect any formal response from the Agency.
Nevertheless, Subcommittee members were generally quite pleased with the effort that EPA has
put into the development and field evaluation studies of coarse particle monitoring methods. The
Subcommittee would welcome the opportunity to conduct a review of proposed PMc reference
and equivalent measurement methods in the future.
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The roster of the CAS AC AAMM Subcommittee is attached as Appendix A to this letter,
and Subcommittee members' individual review comments are provided as Appendix B.
Sincerely,
/Signed/
Dr. Philip K. Hopke, Chair
Clean Air Scientific Advisory Committee
Appendix A - Roster of the CAS AC AAMM Subcommittee
Appendix B - Review Comments from Individual CASAC AAMM Subcommittee Members
cc: Steve Page (MD-10) Robert Vanderpool (E205-01)
Rich Scheffe (MD-14) Thomas Ellestad (E205-01)
Tim Hanley (MD-14) Anthony Maciorowski (1400F)
Michael Papp (MD-14) Fred Butterfield (1400F)
Mary Ross (MD-15)
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Appendix A - Roster of the CASAC AAMM Subcommittee
U.S. Environmental Protection Agency
Science Advisory Board (SAB) Staff Office
Clean Air Scientific Advisory Committee
CASAC Ambient Air Monitoring and Methods (AAMM) Subcommittee*
CHAIR
Dr. Philip Hopke, Bayard D. Clarkson Distinguished Professor, Department of Chemical
Engineering, Clarkson University, Potsdam, NY
Also Member: SAB Board
CASAC MEMBERS
Dr. Ellis Cowling, University Distinguished Professor At-Large, North Carolina State
University, Colleges of Natural Resources and Agriculture and Life Sciences, North Carolina
State University, Raleigh, NC
Mr. Richard L. Poirot, Environmental Analyst, Air Pollution Control Division, Department of
Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT
CONSULTANTS
Mr. George Allen, Senior Scientist, Northeast States for Coordinated Air Use Management
(NESCAUM), Boston, MA
Dr. Judith Chow, Research Professor, Desert Research Institute, Air Resources Laboratory,
University of Nevada, Reno, NV
Mr. Bart Croes, Chief, Research Division, California Air Resources Board, Sacramento, CA
Dr. Kenneth Demerjian, Professor and Director, Atmospheric Sciences Research Center, State
University of New York, Albany, NY
Dr. Delbert Eatough, Professor of Chemistry, Chemistry and Biochemistry Department,
Brigham Young University, Provo, UT
Mr. Eric Edgerton, President, Atmospheric Research & Analysis, Inc., Gary, NC
Mr. Henry (Dirk) Felton, Research Scientist, Division of Air Resources, Bureau of Air Quality
Surveillance, New York State Department of Environmental Conservation, Albany, NY
Dr. Rudolf Husar, Professor, Mechanical Engineering, Engineering and Applied Science,
Washington University, St. Louis, MO
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Dr. Kazuhiko Ito, Assistant Professor, Environmental Medicine, School of Medicine, New
York University, Tuxedo, NY
Dr. Donna Kenski, Data Analyst, Lake Michigan Air Directors Consortium, Des Plaines, IL
Dr. Thomas Lumley, Associate Professor, Biostatistics, School of Public Health and
Community Medicine, University of Washington, Seattle, WA
Dr. Peter McMurry, Professor and Head, Department of Mechanical Engineering, Institute of
Technology, University of Minnesota, Minneapolis, MN
Dr. Kimberly Prather, Professor, Department of Chemistry and Biochemistry, University of
California, San Diego, La Jolla, CA
Dr. Armistead (Ted) Russell, Georgia Power Distinguished Professor of Environmental
Engineering, Environmental Engineering Group, School of Civil and Environmental
Engineering, Georgia Institute of Technology, Atlanta, GA
Dr. Jay Turner, Associate Professor, Chemical Engineering Department, School of
Engineering, Washington University, St. Louis, MO
Dr. Warren H. White, Visiting Professor, Crocker Nuclear Laboratory, University of California
- Davis, Davis, CA
Dr. Yousheng Zeng, Air Quality Services Director, Providence Engineering & Environmental
Group LLC, Baton Rouge, LA
SCIENCE ADVISORY BOARD STAFF
Mr. Fred Butterfield, CASAC Designated Federal Officer, 1200 Pennsylvania Avenue, N.W.,
Washington, DC, 20460, Phone: 202-343-9994, Fax: 202-233-0643 (butterfield.fred@epa.gov)
(Physical/Courier/FedEx Address: Fred A. Butterfield, III, EPA Science Advisory Board Staff
Office (Mail Code 1400F), Woodies Building, 1025 F Street, N.W., Room 3604, Washington,
DC 20004, Telephone: 202-343-9994)
* Members of this CASAC Subcommittee consist of:
a. CASAC Members: Experts appointed to the statutory Clean Air Scientific Advisory Committee
by the EPA Administrator; and
b. CASAC Consultants: Experts appointed by the SAB Staff Director to serve on one of the
CASAC's standing subcommittees.
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Appendix B - Review Comments from
Individual CASAC AAMM Subcommittee Members
This appendix contains the preliminary and/or final written review comments of
the individual members of the Clean Air Scientific Advisory Committee (CASAC)
Ambient Air Monitoring and Methods (AAMM) Subcommittee who submitted such
comments electronically. These comments are included here to provide both a full
perspective and a range of individual views expressed by Subcommittee members during
the Subcommittee's Consultation on Methods for Measuring Coarse-Fraction Particulate
Matter (PMc) in Ambient Air. These comments do not represent the consensus views of
the CASAC AAMM Subcommittee, the CASAC, the EPA Science Advisory Board, or
the EPA itself. The list of Subcommittee members providing comments is provided on
the next page, and their individual comments follow.
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Panelist Page#
Dr. Philip Hopke B-3
Dr. Ellis Cowling B-4
Mr. Richard L. Poirot B-7
Mr. George Allen B-18
Dr. Judith Chow B-23
Mr. Bart Croes B-33
Dr. Kenneth L. Demerjian B-37
Dr. Delbert Eatough B-40
Mr. Eric Edgerton B-46
Mr. Henry (Dirk) Felton B-48
Dr. Rudolf Husar B-54
Dr. Kazuhiko Ito B-57
Dr. Donna Kenski B-62
Dr. Thomas Lumley B-65
Dr. Peter McMurry B-67
Dr. Kimberly Prather B-71
Dr. Armistead (Ted) Russell B-74
Dr. Jay Turner B-77
Dr. Warren H. White B-81
Dr. Yousheng Zeng B-84
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Dr. Philip Hopke
Comments on the Development of Monitors in Support of a NAAQS for PM(io-2.5)
Philip K. Hopke
Bayard D. Clarkson Distinguished Professor and
Director, Center for Air Resources Engineering and Science
Clarkson University
In the development of monitoring methods for PM(i 0-2.5), there are two major objectives to be
achieved: measurement of the attainment/non-attainment status of a particular location and the
determination of the composition of the particles in support of air quality planning in the event of
non-attainment of the standard. The conventional approach has been to deploy filter-based
samplers on the premise that the samples could be used for subsequent chemical analysis.
However, such an approach only produces at best, 1 24-hour integrated value every third day. In
general these samples have not been extensive used for chemical characterization and the cost of
collecting the samples given the amount of labor in preparing, deploying, and weighing the
exposed filters is very high for the very limited amount of data produced. Thus, the primary
focus of any deployment of monitors in support of a coarse particle NAAQS should be on
continuous monitors. It was clear from the presentations that such monitors are sufficiently close
to being ready for deployment that they can be considered for FRM status, particularly those that
are based on fundamental physical principles. Given the limited amount of PM(i0-2.5) data and
epidemiology available on which to base a standard, it would be better to start with a continuous
monitor and have sufficient data to permit additional health effects epidemiology to be
performed so that refined standards could be promulgated during the next round of review. Filter
samplers should only be deployed in areas of likely non-attainment so that the compositional
information would be available to support of air quality planning.
I would strongly recommend a performance standard rather than a design standard. The lack of
precision in the 1987 PMio FRM was due to a lack of rigor in the specifications of the sampler
requirements. There was no requirement for flow control and the performance envelop on the
inlet was set too wide. From simulations based on measured size distributions of the ambient
atmosphere, it is possible to develop an inlet performance envelop that would provide the
precision necessary to meet defined data quality objectives. Thus, the key would be to define the
level of precision required to permit effective attainment/non-attainment decisions to be made
and then develop a set of performance criteria that are needed to achieve this level of precision.
These criteria may prove a challenge for the instrument manufacturers, but will not produce the
substantial impediment to technological innovation that design standards represent.
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Dr. Ellis Cowling
[Note: Sent via e-mail to CAS AC Chair Dr. Philip Hopke, members of the CAS AC Parti culate
Matter (PM) Review Panel, and the CASAC Designated Federal Officer (DFO) at 12:22 PM on
August 2, 2004]
In general, I find substantial merit in this First Draft summary of CASAC comments. I
believe these comments provide valuable guidance for NCEA's further efforts to provide a draft
Air Quality Criteria Document for Particulate Matter that can be accepted in full by CASAC at
its forthcoming conference call discussion now tentatively scheduled for some time in mid
September.
As befits my special particular role in CASAC, my major concerns about the AQCD for
PM have to do with the need for a more balanced treatment in the AQCD for PM of "Welfare
Effects," and the associated desirability of a "Secondary Standard" dealing with PM effects on
various "Air-Quality Related Values."
These values include: 1) visibility impacts on human enjoyment of scenic vistas
especially in national and state parks, 2) associated economic impacts on our tourism industries,
3) ecosystem responses to decreased solar radiation caused by regional haze, 4) increased
atmospheric deposition of the nutrient and growth-altering substances in PM (including organic,
oxidized, and reduced forms of nitrogen, sulfur, phosphorus, potassium, and the wide variety
organic nutrients of fine and coarse aerosol particles, 5) direct effects on materials such as soiling
of painted surfaces, exposed textile materials, etc., and 6) the need for greater concern during the
next several decades about "smoke management" in light of the greatly increased risk of wild
fires and the corresponding necessity for increased amounts of controlled burning of forests and
natural areas in parks and other recreational areas.
Greater attention should be given in the AQCD to these "Air Quality Related Values" in
rural as well as in urban areas.
Some of the many excellent and readily available photographs, tables, and figures should
be added to the AQCD to illustrate and quantify such welfare effects as: 1) visibility impairment
at scenic vistas and airports, 2) wild fire impacts on the aesthetic values of landscapes, 3)
wildfire impacts on wildlife populations, 4) economic data on tourism impacts and smoke
management costs and benefits, 5) the successes of urban areas that have adopted secondary
standards for visibility impairment, and 6) changes in populations of aquatic invertebrates or fish
that are induced by atmospheric deposition of the essential nutrient substances in the aerosols
involved in cloud nucleation and precipitation processes.
With regard to ideas for inclusion in the summary of individual comments deriving from
the CASC "Consultation on Methods for Measuring Coarse-Fraction Particulate Matter (PMc) in
Ambient Air, Based upon Performance Evaluation Studies Conducted by EPA," permit me to
summarize the two points I made at near the end of this "Consultation" on Thursday July 22.
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Point 1)
EPA (and many other federal research and monitoring organizations) need to guard
against the tendency to allocate so much of the funds used in field measurement campaigns to
"making careful measurements" and that inadequate funds are available for "scientific analysis
and interpretation" to determine what the measurements really mean.
As described on pages 282-284 in the attachment to this E-mail message, these
cautionary remarks about problems in field measurement programs were suggested originally by
the late Glenn Cass, formerly of Cal Tech and later of Georgia Tech, on the basis of his career-
long experience in various environmental monitoring programs programs in which too much
funding was allocated to "measurements" and too little to "analysis and interpretation" of the
data. Please note on pages 283 and 284, the "Fifteen general and specific reasons why this
happens" and the "Thirteen general and specific things that can be done about it!"
The reference for this published reviewed paper is: Cowling, E., and J. Nilsson. 1995.
Acidification Research: Lessons from History and Visions of Environmental Futures. Water Air
and Soil Pollution 85:279-292.
Please also note especially the suggestion in item 9 on page 284 about a "50:50
distribution" of funding allocations between "measurements" and "analysis and interpretation" of
monitoring data rather than the (90:10 or 80:20 distribution) that is typical of many monitoring
programs in EPA and other agencies.
But please also note that an even better suggestion was made by Mary Barber, former
executive leader with the Ecological Society of America's Executive, who opposed the "50:50
distribution" idea at a recent Whitehouse Conference on monitoring. Mary Barber insisted, and I
agree with her, that it would be even more appropriate to distribute the funding into three rather
than two categories of investments with equal shares going to "measurements," "analysis and
interpretation," and "outreach and extension of findings" to interested clientele and "customers"
for the results of field measurement programs.
This problem is so commonplace not only in this country but all over the world that
I commend these "lessons that are available to be learned" (and perhaps even the "15 reasons
why this happens" and the "13 things to do about it") for inclusion among the "comments from
individual participants" in the CASAC Consultation on PM Measurement Methods.
Point 2)
EPA should also guard against the tendency to give undue emphasis to "Data Quality
Objectives" in the selection and evaluation of instruments and subsequent implementation of
field measurement programs to the exclusion of concern about "Science Quality Objectives" and
"Policy Relevancy Objectives."
Experience within the Southern Oxidants Study and other large-scale field measurement
campaigns have demonstrated repeatedly that undue emphasis on "Data Quality Objectives"
often leads to:
1) Serious lack of attention to the scientific hypotheses and assumptions that are inherent in any
choice of scientific instruments, the appropriateness of the ground-based sites or aircraft
platforms on which the instruments are mounted, the skills of the instrument operators, the data
processing and data-display programs used, and especially the scientific quality of the
conclusions and statements of findings that are drawn from analysis and interpretation of the
measurements that are made; and
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2) Equally serious lack of attention to the policy relevancy of the measurements being made
relevancy to the general or specific enhancements of environmental protection that are the raison
de etre of the public health or public welfare concerns that led to the decision to establish a
monitoring program or undertake a field measurements campaign in the first place.
In this latter connection, permit me also to call attention to the "Guidelines for the
Formulation of Scientific Findings to be Used for Policy Purposes" which were developed
originally by the NAPAP Oversight Review Board led by Milton Russell. Please find attached to
this E-mail message, an electronic version of these Guidelines which we have adopted and very
slightly adapted for use in formulating policy relevant scientific findings in the Southern
Oxidants Study.
The original version of these Guidelines was published as Appendix III of the April 1999
Report titled "The Experience and Legacy of NAPAP." This was a Report to the Joint Chairs
Council of the Interagency Task Force on Acidic Deposition of the Oversight Review Board
(ORB) of the National Acid Precipitation Assessment Program.
As indicated in Appendix III:
"The following guidelines in the form of checklist questions were developed by the ORB
to assist scientists in formulating presentations of research results to be used in policy decision
processes. These guidelines may have broader utility in other programs at the interface of science
and public policy and are presented here with that potential use in mind."
These guidelines may also be of value as part of the "communication of individual
comments" from the CASAC Consultation on PM Measurement Methods.
********************************************
Dr. Ellis B. Cowling, Director
Southern Oxidants Study
North Carolina State University
Raleigh, North
*********************************************
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Mr. Richard L. Poirot
Supplemental Comments on EPA PMc Methods Review, R Poirot, 8/1/04
Overall, the effort EPA (& equipment manufacturers) have put into development and field
evaluation of coarse particle methods is excellent, and should be commended and continued
(with adequate funding support). Given the substantial expertise of many of the subcommittee
members on the technical details of the sampling methods, my comments are intentionally of a
more general nature, and specifically encourage EPA to consider the many important objectives
of a new PMc sampling program - in addition to determining compliance with anticipated new
standards.
Towards the beginning of the 7/22/04 CAS AC AAMM Subcommittee meeting, I raised some
general (devil's advocate) "issues" relating to the multiple objectives for PMc monitoring, with
the intent of generating some group discussion. In hindsight, this didn't work out so well - and
I'd like to try to clarify some of these points.
The draft chapters 8 & 9 of the PM CD seem to (me to) strongly and repeatedly emphasize the
relatively benign nature of "crustal" PM. Since crustal material (i.e. soil and its associated
physical, chemical & biological components) typically account for a large proportion of coarse
particle mass, this might raise questions on the focus of coarse mass as a relevant health effects
indicator. I don't agree with this emphasis in the current draft CD chapters, think that it is
overstated in a speculative manner, and have submitted supplemental comments on this issue
(copy attached at bottom of these NAAMM comments).
Those health studies that have specifically attempted to evaluate effects of coarse particles have
shown a wide range of potential responses - ranging from (illogical) negative associations with
mortality, to large positive associations that exceed those for fine particles. Although it is
currently unclear what level(s) or averaging time(s) will be proposed for a PMc standard(s), a
reasonable assumption is that there will not be high confidence that any specific level of standard
will represent a clear "bright line" above and below which effects do and don't occur. Given the
anticipated high degree of spatial and temporal variability of ambient PMc concentrations
(compared to PM2.s), it also seems logical that we will have less confidence in how well any new
PMc measurements represent human exposures. For these reasons, I suggest that the (always
important) monitoring objective of determining compliance with standards is of relatively less
importance for PMc than it is for many other criteria pollutants (PMs.s & ozone for example).
Conversely, other measurement objectives take on relatively greater importance and should be
carefully considered.
Virtually all past studies that have jointly considered health effects of fine and coarse particles
have employed methods that are more precise for PM2.5 than for PMc - with the latter typically
quantified by subtracting (precise) PM2.5 from imprecise PMio (and with the subtraction further
compounding the noise of the individual measurements). As emphasized in Warren White's
comments & associated reference (White, 1998, J. Air & Waste Manage. Assoc. 48, 454-458):
"noise always depresses observed correlations with other measurements, historical PMc
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measurements have been much noisier than associated PM2.5 measurements, and crucial
inferences about health effects and atmospheric behavior are routinely based on the differences
observed between correlations involving PMc and PM2.5." Thus the current uncertainty over
specific PMc effects is at least partially due to the absence of collocated PMc and PM2.5 of
comparable precision (and, for epidemiological studies) in locations representative of large
population exposures. For these reasons, an especially important objective of new PMc
measurements should be to collocate precise PMc and PM2.5 both in areas of high anticipated
PMc exposures (arid southwest and agricultural areas), and also in large population centers
nationwide. Given the anticipated high degree of temporal variability in PMc (vs. PM2.s), the
development of precise continuous methods for PMc seems especially important for meeting this
objective, in support of future health studies.
For the most part, the chemical composition of coarse particles has not, historically, been well
characterized (typically done by subtraction or dicot if at all) and this would seem like a third
important objective. Here, I think the sampling separation of PMc from PM2.5 deserves special
emphasis. One of the original design concepts of the dichotomous sampler was to prevent the
mixing (and subsequent chemical interaction) of fine & coarse particles (for example to quantify
fine particle acidity without artifactual reaction with alkaline crustal material - & vice versa). For
this reason, methods that allow collection of coarse-only particles on filter media suitable for
chemical analyses should also be emphasized. For this reason, I encourage EPA to consider
reanalysis (by other labs and methods) of the coarse dicot filters (or maybe the PM-10 and 2.5
filters) collected during the field evaluations - with a specific emphasis on developing closure
with the measured mass. Also, some evaluation of the uniformity of coarse particle distribution
across filter media and/or any limitations of surface beam (XRF) analytical methods on coarse
particle samples should be evaluated - possibly through comparison with neutron activation
analysis.
Along similar lines, one suggestion for PMc methods development is to consider - or at least
evaluate the effects of - adding SCh & HNOs denuders to PMc samplers to prevent artifactual
reactions with alkaline crustal or marine sea salt material during sampling. Potentially, the use of
such denuders would be most important for 24-hour filter-based methods and less critical for
"fast response" continuous methods.
Given the above-stated current uncertainties in absolute "bright line" PMc concentrations which
are injurious, the high temporal variability, and the greater artifact potential, I don't think it's
absolutely necessary (and perhaps not desirable) to have a filter-based FRM for PMc. Some
consideration should be given to specifying a continuous method as the FRM, with a filter
method as FEM, specifically intended for subsequent speciation analyses at a limited number of
sites.
For various reasons, developing a (much) better understanding of the biological content of coarse
particles seems especially important. Its unclear to me if there are currently available analyses
that could reveal key biological indicators from filter-based samples on a "routine" basis, but I
would suggest EPA devote some research into potential, low-cost "biological indicator" analyses
that might be conducted on "routine" PMc filter samples (protein, carbohydrates, etc.), and also
consider development of PMc sample collection methods that would allow a more
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comprehensive evaluation of specific biological components (pollen, endotoxin, and specific
fungi, virus and/or bacteria). Clearly such detailed biological analyses will not be economically
feasible on a large network basis, but it would be important to devote some fraction of
monitoring (&/or research) funds to development & implementation of such methods at a limited
number of sites.
It can be reasonably assumed in advance that PMc will exhibit a high degree of spatial variability
(especially for locally generated particles - PMc from more distant sources like African or Asian
dust storms will be more spatially uniform). For this reason, I caution against attempting to
quantify this spatial variability by uniformly dense siting of routine monitors (we can't afford it),
and encourage EPA to consider alternative methods of characterizing spatial variability of
concentrations & exposures. Characterizing PMc spatial patterns can't be done by measurements
alone, and development of better microscale models should be considered an important
objective. Characterizing temporal variability with continuous methods will help here, as
time/space patterns are related. Better quantification of the full range of coarse particle size
distributions (including above 10 um) such as by optical particle counters will also be helpful.
Careful attention to monitor siting characteristics, especially inlet height and distance from
roadways, agricultural and mining activities will also be critical. Focused gradient studies (how
rapidly do concentrations fall off with distance from roadways?) will be useful (an important co-
objective of such studies should be to clarify the current, continuing disparity between EPA
PM2.5 road dust emissions estimates and ambient concentrations!). But such "experiments" do
not need to be repeated everywhere, and consideration should be given to identifying a small
number of more intensive sites in each region of the country. Possibly such sites could be
periodically "rotated" among states in a region to avoid disruption of state-specific monitoring
funds.
While filter-based measurements of PM2.5 and low-volume PMio with subsequent subtraction is
the least desirable measurement approach, it should be recognized that low volume sampling - in
many cases collocated with PM2.5 and/or PM2.5 speciation samplers is or will be conducted for
"air toxics" purposes by State agencies. Some consideration should be given to coordination
between the toxics and PMc programs, to at least allow for use of subtraction (of precise
samples) at sites where such measurements are being conducted already. In addition, for sites
where PM2.5 speciation measurements are collocated with low-vol PMio, some consideration
might be given to providing for complementary XRF analysis on the PMio samples (I think
ICPMS is generally the most common method for "toxics metals", but possibly it could be
preceded by (nondestructive) XRF at selected sites.
Along similar lines, is there any possibility of modifying inlets of existing PM2.5 speciation
samplers to a 10 um cut? My sense is the PM2.5 speciation samplers are more widely deployed
than need be, and if a few of these could be converted to PMio and collocated at a few PM2.5
speciation sites, we could possibly generate some comparative data relatively rapidly and at
modest cost.
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[The following is also included at the request of Mr. Poirot:]
Supplemental Comments on PM CD Chapters 8 and 9, R. Poirot 7/26/04
Several sections of chapters 8 & 9 (for example 8.2.2.5.3 & 9.2.3.2.1) summarize health effects
associations with different chemical components and/or source categories on PM in various size
fractions. These discussions are clear, detailed, helpful and informative - and (I think) the results
could conceptually be presented, integrated, summarized, etc. in two general ways:
1. to indicate that many or most all of the major PM mass-contributing species or source
contributions have been individually shown to be injurious (this adds considerably to the
use of PM2.5 or PM10-2.5 mass as a regulatory metrics, regardless of different PM
mixtures in different regions).
2. to indicate that some species or source categories appear to be more harmful or less
harmful than others (potentially this might lead to species-specific standards or source-
specific priorities in the implementation phase).
Based on discussions at the CASAC PM CD review, subsequent discussions at the PM coarse
monitoring methods review and on a re-reading of relevant sections of chapters 8 and 9,1
encourage EPA to more heavily emphasize the former (#1) use of this information and de-
emphasize the latter (#2). General reasons include:
a. Adverse health effects are associated with many different species and/or source-specific
contributions, although these associations are not always consistent among studies.
Taken in the aggregate, they clearly show adverse effects from many species, but
individually no one study is definitive.
b. The species and/or source-specific health associations are not sufficiently strong or
consistent in their findings to support species-specific standards or to prioritize (or
exculpate) species or sources for future controls at the present time - and to do so would
require choosing among or rating studies which show contrary effects (a much more
difficult argument to support than #1).
c. Epi. studies associating specific source categories with effects (or non-effects) are limited
in number, and have generally have relied on "factor analysis" approaches (such as PCA
with Varimax or Procrustean rotation) which are not currently considered state-of-the-
science (poorly constrained and potentially yielding many different "equally correct"
answers) and require subjective interpretation of the resulting sources. These results are
then often further interpreted and commented on in the CD in a highly speculative
manner.
Specifically, I think the chapter 9 integrated synthesis should de-emphasize or present counter
examples in sections where specific source categories are identified as uniquely benign. This
seems most evident for the contributions of "crustal" emissions to PM2.5, PM10 and/or PM10-
2.5.1 think this is especially not helpful in considering any coarse particle standards, since
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crustal material (and its associated anthropogenic chemical or biological contaminants) is
typically a large fraction of coarse mass at most times & places. For example following are
several examples where I think the potential effects of "crustal particles" are unnecessarily (&
speculatively) de-emphasized:
On p. 9-44, lines 18-19: "Also of much importance, all of the above studies that investigated
multiple source categories found a soil or crustal source that was negatively associated with
mortality". Here, it's not entirely clear why this is "of much importance" (compared to what?), or
what "all of the above studies" refers to (the preceding paragraph, page, section?). The consistent
finding of a negative association (and implication we would live longer if it were dustier) is a
consistent indication (to me) of a poorly formulated model(s). It is also inconsistent with the
many studies (mostly cited in the CD) which do show effects associated with coarse particle
mass, and with the rather extensive bodies of literature on adverse effects from both the
inorganic components of crustal material (silicosis, pneumoconiosis, etc.), as well as with the
extensive and growing literature on diseases associated with soil-borne fungi or bacteria
(Coccidioidomycosis, etc.). I've listed some references grouped in these 3 general areas at the
end of these comments.
Several features of the (rather outdated) receptor model approach taken by the studies which I
assume are referred to in "all of the above studies" are important. First, all multi-elemental
measurement techniques, and especially the most common XRF, coincidentally quantify a large
number of elements which are of predominantly crustal origin (Si, Al, Fe, Ca, Ti, etc. - much
more so than for any other source category). For this reason, a "crustal" or "soil" factor is nearly
always identified in virtually all receptor model applications. The (rotated eigenvector) factor
analysis approach which I think was used in all of the above studies seeks first to account for the
collective variance of all the species used as input, and so typically (prior to rotation) the first
component, explaining a maximum of the total variance tends to be "crustal" (even though these
elements together typically account for only a small fraction of the fine mass). Subsequent
rotational schemes (Varimax, Procrustes, etc.) then redistribute the variance in ways that require
highly subjective decisions by the modelers. These models also require (can only find) sources of
fixed, unique chemical composition and variable, unique contribution. Soil itself has a highly
variable composition but tends to be more alkaline in the West than in the East, very alkaline in
areas with calcarious bedrock, and different yet again in the Sahara Dust and Asian Dust which
often result in the highest soil contributions in the Eastern US and West coasts respectively.
These more distant dust events also tend to have much smaller particle size distributions than
"local dust" emissions, as the larger particles are more readily removed during transport. Crustal
material can become heavily contaminated with anthropogenic S, N, OC, EC, salt and metals -
both as it is deposited & resuspended from roadways or as it undergoes chemical reactions
during transport. Conversely, many other sources also contain "crustal impurities" (coal fly ash
for example), and so when one obtains a "pure crustal source" from a factor analysis it's not
entirely clear what that source actually represents. If the rotation is oblique, the sources are
required to be uncorrelated, and it's therefore highly probable that the "crustal" source will (to
the extent local sources contribute) be a good indicator of high wind speeds, since this will lead
uniquely to high emissions & concentrations of dust which will be uncorrelated with all other
(gaseous &) particulate pollutants. While high dust concentrations that also build up under
stagnation conditions (from road dust emissions) or dust from more distant origins will tend to
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get mixed into other modeled sources. Quite possibly the consistent finding of negative health
associations with dust just reflects windy days when folks stay indoors and the air is otherwise at
its cleanest. For example:
On p. 9-27, lines 1&2, we learn that "new studies have shown no increases in mortality on days
with high concentrations of wind-blown dust (crustal particles), using PM 10 concentrations and
data on wind speed as indicators of dust storm days." Which new studies? I think the (not
unreasonable) use of wind speed as a dust surrogate is telling, as dust emissions (especially the
maximum concentrations) are uniquely associated with high wind speeds - which in turn will
tend to minimize concentrations of all other (fine) particle and gaseous components - assuring
minimal chemical reactions between crustal particles and other species. High concentrations of
crustal particles and chemically associated contaminants (on the surface of coarse particles) from
MV, SO2 or smelting activities would also reach high concentrations (as would many other
gaseous and PM pollutants) on local stagnation days with low mixing heights - but would not be
considered with this "wind speed" surrogate (nor would dust of distant origin). Potentially
outdoor activities are curtailed on very windy, "local" dusty days, windows are closed, inhalation
efficiency of coarse particles likely decreases with wind speed, and the spatial representativeness
of "central site monitors" diminishes. Conversely, the lengthy Section 8.4.3.5 discussion of
"Adjustments for Meteorological Variables" includes factors like temperature and humidity that
might tend to exaggerate assumed PM effects, but makes no mention of wind speed - which
might tend to diminish such effects.
On p. 9-27, lines 3-6, it is postulated that cardiovascular mortality in Phoenix may be due to the
metal rather than crustal content of coarse particles. Yet on p. 8-63, lines 22-28 it's indicated that
"... (Smith et al., 2000) indicate that coarse particle-mortality associations are stronger in spring
and summer, when the anthropogenic metal (Fe, Cu, Zn, and Pb) contribution to PM10-2.5 is
lowest, as determined by factor analysis." In this case, the seasonal association of effects when
crustal, not metal, coarse particles are greatest is attributed speculatively to "biogenic processes
(e.g., wind-blown pollen fragments, fungal materials, endotoxins, and glucans) of the particles
during spring and summer". It is also specifically emphasized that the authors "observed that the
implication that crustal, rather than anthropogenic elements, for the observed relationship with
mortality was counterintuitive." Thus a finding that does not fit the theory is discredited.
Emphasizing the potential importance of coarse biological content is reasonable, but on p. 8-326,
lines 8-17, its indicated that "Reasons for differences among findings on coarse-particle health
effects reported for different cities are still poorly understood, but several of the locations where
significant PM10-2.5 effects have been observed (e.g., Phoenix, Mexico City, Santiago) tend to
be in drier climates and may have contributions to observed effects due to higher levels of
organic particles from biogenic processes (e.g., endotoxins, fungi, etc.) during warm months."
Here, I can understand how dry climate can and does lead to increased emissions and
concentrations of coarse crustal material (and any biological material it contains), but I'm not
sure why or if its logical to expect arid climates (and associated sparse vegetation) to have
uniquely higher pollen, endotoxin or fungi emissions & concentrations than humid areas - where
wind-blown dust emissions would tend to be suppressed by precipitation, and where pollens,
pollen fragments and fungi might be relatively more abundant.
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I think a more logical explanation could be effects from soil-associated fungi, which for the most
part become airborne only as the soil becomes airborne during "natural" dust storms and/or as
modified by human agricultural activities (tilling harvesting, grazing, etc.) and on & off-road
vehicles.
For example, the geographically-focused incidence of "Valley Fever" specifically caused by
caused by the fungus Coccidioides sp., which grows in soils in areas of low rainfall, high
summer temperatures, moderate winter temperatures, and which is emitted in direct association
with the soil that supports it, would seem like a more logical causal or contributing factor than
some non-soil-related biogenic contribution from pollen or more benign fungi in general. See
also the references on other soil-related fungal or bacteriological effects on human & animal
health, crops, aquatic ecosystems, etc. - for example Garrison et al. (2003).
On p. 8-326, lines 17-21 it is indicated that "in some U.S. cities (especially in the NW and the
SW) where PM10-2.5 tends to be a large fraction of PM10, measurements, coarse thoracic
particles from wood burning are often an important source during at least some seasons. In such
situations, the relationship between hospital admissions and PM10 may be an indicator of
response to coarse thoracic particles from wood burning."
Spatial Distribution of Valley Fever
Source: http://www.valleyfever.com;
However, since wood smoke concentrations are VERY predominately < 2.5 um, it seems
illogical that wood smoke should be the likely causal factor for coarse particle effects in areas
that have high coarse:fine ratios. I also question whether the NW has a high coarse:fine ratio and
why the (dusty, crusty) SW would tend to have a uniquely high coarse wood smoke contribution
(compared to all northern areas where space heating demands and fuel wood supplies are
greater). This also seems inconsistent with the "counterintuitive" Phoenix results indicating
highest coarse PM effects in the spring & summer. I'm getting picky here, but again it looks like
trying too hard to show "it must be anything but crustal emissions"...
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References
On Coarse PM health effects associations in general:
Becker S, Soukup J. Coarse(PM(2.5-10)), fine(PM(2.5)), and ultrafme air pollution particles
induce/increase immune costimulatory receptors on human blood-derived monocytes but not on
alveolar macrophages. J Toxicol Environ Health A. 2003 May 9;66(9):847-59.
Becker S, Soukup JM, Sioutas C, Cassee FR. Response of human alveolar macrophages to
ultrafme, fine, and coarse urban air pollution particles. Exp Lung Res. 2003 Jan-Feb;29(l):29-44.
Becker S, Fenton MJ, Soukup JM. Involvement of microbial components and toll-like receptors
2 and 4 in cytokine responses to air pollution particles. Am J Respir Cell Mol Biol. 2002
Nov;27(5):611-8.
Soukup JM, Becker S. Human alveolar macrophage responses to air pollution particulates are
associated with insoluble components of coarse material, including paniculate endotoxin.
Toxicol Appl Pharmacol. 2001 Feb 15;171:20-6.
Ostro BD, Broadwin R, Lipsett MJ. Coarse and fine particles and daily mortality in the
Coachella Valley, California: a follow-up study. J Expo Anal Environ Epidemiol. 2000
Sep-Oct;10(5):412-9.
Ostro BD, Hurley S, Lipsett MJ. Air pollution and daily mortality in the Coachella Valley,
California: a study of PM10 dominated by coarse particles. Environ Res. 1999 Oct;81(3):231-8
Sheppard L, Levy D, Norris G, Larson TV, Koenig JQ. Effects of ambient air pollution on
nonelderly asthma hospital admissions in Seattle, Washington, 1987-1994. Epidemiology. 1999
Jan;10(l):23-30.
Cifuentes LA, Vega J, Kopfer K, Lave LB. Effect of the fine fraction of particulate matter versus
the coarse mass and other pollutants on daily mortality in Santiago, Chile. J Air Waste Manag
Assoc. 2000 Aug;50(8): 1287-98. Burnett et al. 2000
Lin M, Chen Y, Burnett RT, Villeneuve PJ, Krewski D. The influence of ambient coarse
particulate matter on asthma hospitalization in children: case-crossover and time-series analyses.
Environ Health Perspect. 2002 Jun;l 10(6):575-81. Sheppard L et al. 1999
Pozzi R, De Berardis B, Paoletti L, Guastadisegni C. Inflammatory mediators induced by coarse
(PM2.5-10) and fine (PM2.5) urban air particles in RAW 264.7 cells. Toxicology. 2003 Feb
l;183(l-3):243-54.
Kleinman MT, Sioutas C, Chang MC, Boere AJ, Cassee FR. Ambient fine and coarse particle
suppression of alveolar macrophage functions. Toxicol Lett. 2003 Feb 3;137(3):151-8.
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Monn C, Becker S. Cytotoxicity and induction of proinflammatory cytokines from human
monocytes exposed to fine (PM2.5) and coarse particles (PM10-2.5) in outdoor and indoor air.
Toxicol Appl Pharmacol 1999;155: 245-52.
Schins RP, Lightbody JH, Borm PJ, et al. Inflammatory effects of coarse and fine particulate
matter in relation to chemical and biological constituents. Toxicol Appl Pharmacol. 2004; 195(1):
1-11.
Shi T, Knaapen AM, Begerow J, et al. Temporal variation of hydroxyl radical generation and 8-
hydroxy-2'-deoxyguanosine formation by coarse and fine particulate matter. Occup Environ
Med. 2003;60: 315-21.
Greenwell LL, Moreno T, Jones TP, et al. Particle-induced oxidative damage is ameliorated by
pulmonary antioxidants. Free Rad Biol Med. 2002;32(9): 898-905.
Mar TF, Norris GA, Koenig JQ, et al. Associations between air pollution and mortality in
Phoenix, 1995-1997. Environ Health Perspect 2000; 108: 347-53.
Castillejos M, Borja-Aburto V, Dockery D, et al. Airborne coarse particles and mortality.
Inhal Toxicol 2000; 12 (supplement l):61-72.
On dust -associated inorganic components & effects:
Gift, J.S., & Faust, R.A.: "Noncancer Inhalation Toxicology of Crystalline Silica: Exposure-
Response Assessment," J. Expos. Analysis Environ. Epidemiol. 7(3^:345-358 (1997).
Wright, G.W.: "The Pulmonary Effects of Inhaled Inorganic Dust" (Chapter 7). In Patty's
Industrial Hygiene and Toxicology, 3rd Rev. Ed., Vol. 1 General Principles. New York: John
Wiley & Sons, 1978. pp. 175-176.
M. Schenker (2000) Exposures and Health Effects from Inorganic Agricultural Dusts,
Environmental Health Perspectives Supplements, Volume 108, Number S4 August 2000.
International Agency for Research on Cancer: I ARC Monographs on the Evaluation of
Carcinogenic Risks to Humans, Vol. 68 Silica, Some Silicates, Coal Dust and Paraaramid
Fibrils. Geneva, Switzerland: World Health Organization, IARC Press, 1997. pp. 41-85.
U.S. Environmental Protection Agency/Office of Research and Development: Ambient Levels
and Noncancer Health Effects of Inhaled Crystalline and Amorphous Silica: Health Issue
Assessment (EPA/600/R-95/115). November 1996. pp. 7-1-7-6.
On dust -associated biological components & effects:
Buchwaldt, L., ... and C.C. Bernier. 1996. Windborne dispersal of Colletotrichum truncatum
and survival in infested lentil debris. Ecology and Epidemiology 86:1193. Gloster, J., R.F.
Sellers, and A.I. Donaldson. 1982. Long distance transport of foot-and-mouth disease virus over
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the sea. The Veterinary Record 110(Jan. 16):47. Griffin, D.W., V.H. Garrison, etal. 2001.
African desert dust in the Caribbean atmosphere: Microbiology and public health. Aerobiologia
17(June 14):203.
Griffin, D.W., C.A. Kellogg, and E.A. Shinn. 2001. Dust in the wind: Long range transport of
dust in the atmosphere and its implications for global public and ecosystem health. Global
Change and Human Health (September). O'Hara, S.L., etal. 2000. Exposure to airborne dust
contaminated with pesticide in the Aral Sea region. Lancet 355(Feb. 19):627.
Prospero, J.M. 1999. Long-term measurements of the transport of African mineral dust to the
southeastern United States: Implications for regional air quality. Journal of Geophysical
Research 104(July 20): 15917.
Snyder LS, Galgiani JN. Coccidioidomycosis: The Initial Pulmonary Infection and Beyond.
Seminars in Respiratory and Critical Care Medicine 1997; 18:235-247.
Galgiani JN. Coccidioidomycosis. In: Remington JS and Swartz MN ed. Current Clinical Topics
in Infectious Diseases. 1997, pp. 188-204.
Morbidity & Mortality Weekly Report, "Coccidioidomycosis- Arizona, 1990-1995. Dec. 13,
1996/vol. 45:1069-1073.
Einstein HE, Johnson RH. Coccidioidomycosis: new aspects of epidemiology and therapy.
Clinical Infectious Diseases 1993;16:349-356.
Galgiani JN. Coccidioidomycosis. Western Journal of Medicine 1993;159:153-171.
Galgiani JN. Coccidioidomycosis. In: Rakel RE, ed. Conn's Current Therapy. Philadelphia: WB
Saunders Co., 1996, pp. 188-190.
Galgiani JN, Ampel NM. Coccidioidomycosis in human immunodeficiency virus-infected
patients. Journal of Infectious Diseases 1990;162:1165-69.
Hall KA, Copeland JG, Zukoski CF, et al. Markers of Coccidioidomycosis before cardiac or renal
transplantation and the risk of recurrent infection. Transplantation 1993;55:1422-24.
Kwon-Chung KJ, Bennett JE. Medical Mycology. Philadelphia, Lea and Febiger, 1992, pp. 356-
396.
Pappagianis D. Marked increase in cases of Coccidioidomycosis in California: 1991, 1992, and
1993. Clinical Infectious Diseases 1994;19(Suppl 1):S14-18
Pappagianis D, Zimmer BL. Serology of Coccidioidomycosis. Clinical Microbiology Reviews
1990:3:247-268.
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Schneider E, et al: A Coccidioidomycosis Outbreak Following the Northridge, Calif Earthquake.
JAMA 1997;277:904-908.
Pappagianis D: Coccidioidomycosis, In DiSalvo A (Ed) Occupational Mycoses. Philadelphia,
PA, Lea & Febiger, 1983, pp 13-28.
V. H. Garrison, E. A. Shin, W. T. Foreman, D. W. Griffin, C. W. Holmes, C. A. Kellogg, M. S.
Majewski, L. L. Richardson, K. B. Ritchie & G. W. Smith (2003) African and Asian Dust: From
Desert Soils to Coral Reefs, BioScience . May 2003 / Vol. 53 No. 5, pp. 469-480.
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Mr. George Allen
To: Fred Butterfield, Designated Federal Officer
EPA SAB, Clean Air Scientific Advisory Committee (CASAC)
Ambient Air Monitoring and Methods Subcommittee
From: George Allen, AAMM subcommittee member, August 7, 2004
The following are revised written comments on PM-coarse measurement methods per Dr.
Hopke's request at the AAMM subcommittee meeting on PM-coarse on July 22, 2004. As
requested, a copy of these comments is being sent to Dr. Phil Hopke, CASAC AAMM
Subcommittee Chair. These revised comments reflect my original comments and the discussion
at the July 22 AAMM meeting.
First, I wish to express my appreciation for a well designed and executed field evaluation
study of a wide range of methods for coarse mode particulate mass (PM-c) and related
measurements. The results from this study provide a solid base for this subcommittee's work. I
encourage EPA to continue to perform meaningful monitoring method evaluation studies in the
future, and to request or allocate additional funds as necessary to continue this PM-coarse work
in an effort to resolve several outstanding issues. More detail on the need for further work is
included below.
It must be noted up front that the results from this study are a "best case" metric because
of the high level of staff skill and attention (resources) available for all aspects of this study. The
same methods deployed in a large scale routine network would most likely not perform as well;
this goes across the board for all methods, but perhaps more so for those that are relatively
complex. It would be very informative to deploy some of these methods on a limited scale to
selected SLT agencies to assess "real-world" performance.
For multiple reasons, PM-c is inherently more difficult to measure with the precision and
accuracy possible for PM2.5 or PM10 measurements. Particles substantially larger than 1 jim are
more likely to be lost or to bounce on sampler inlets and surfaces; there is no truly "direct" way
(simple, single measurement) to measure this size range; and PM-c concentrations are often
substantially lower than PM2.5 in much of the U.S. Therefore, the identification of a robust
"benchmark" reference method (for use in evaluation of candidate reference or equivalent
methods) is critical. For this, I agree with the approach taken by Vanderpool et al. in the EPA
PM-c field study where other methods are compared to a very carefully operated collocated pair
of low-volume FRM samplers for PM2.5 and PM10, with PM-c calculated by difference. With
care, this approach can provide results with coefficients of variation of better than 1.5% for
single sampler precision (Allen et al. 1999, JAWMA 49:PM, 133-141). This is the least
ambiguous PM-c method as well as being technically compatible with the existing PM2.5 FRM.
There are also minimal concerns related to mechanical loss of particles from the filters (coarse
mode particles are presumably better "bound" to the PM10 filter substrate by fine mode
particles). As such, I can not recommend any other approach for this purpose. The EPA study's
experience with coarse particle loss from the R&P dichotomous sampler is a good example why
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the difference method must be the "benchmark" for evaluation of other candidate reference or
equivalent PM-c methods.
Specifically, the reference method should not be a manual (or sequential) dichotomous
sampler. There are too many performance uncertainties in the virtual impactor approach
(regardless of design), performance can vary strongly as a function of coarse to fine mode ratios
and aerosol loading, particles on the coarse filter can be lost from the filter media after
collection, and the method is inherently more complex. This does not necessarily mean that the
low-vol FRM difference method is the best choice for routine deployment in large scale
monitoring networks; the performance of the difference method in the EPA PM-c study was
optimal because of the available resources, and would likely be significantly degraded in routine
use. There is nothing inherently wrong with the reference method not being routinely used for
SLT measurement programs (ozone and sulfur dioxide are existing examples).
For dichotomous samplers the EPA report notes that "Effective shipping protocols
resulted in negligible particle loss during transport...". It is not clear to what extent particle loss
from dichotomous sampler coarse channel filters is still a concern under less than ideal shipping
conditions; e.g., should special shipping protocols be required for dichotomous filters to prevent
particle loss? This has been shown to be a problem in the past, in the early 1980's when a 15 jim
inlet was used (Dzubay JAPCA 33:7, Spengler JAPCA 33:12). Despite the EPA report's claim of
negligible particle shipping loss, the work to-date by EPA does not ruled out the potential for
coarse dichotomous filter shipping loss. The EPA report concludes that the rough filter handling
action of the R&P sequential filter dichotomous sampler was mechanically "knocking off up to
20% of the coarse filter mass, presumably leaving little mass to be further lost during shipping.
The winter Phoenix study is too limited to be used to demonstrate resolution of dichotomous
sampler coarse particle loss, both because of the small (15) sample size and the relatively small
coarse to fine mode mass ratios for that study. R&P now has a revised version of the sequential
dichotomous sampler that addresses this filter transport mechanism issue. The best way to test
both the sampler design changes and potential for shipping loss is to return to a site like Phoenix
in the summer with both high coarse mass concentrations AND a high coarse to fine mass ratio.
A parameter of concern for PM-c integrated filter samplers is the ability of the sampler to
routinely produce field blanks with minimal mass gain. Existing EPA PM2.5 FRM regulations
allow up to 30 jig mass gain on field blanks. For areas with PM-c means in the range of 10 to 15
jig/ms (much of the eastern US), a PM-c sampler (difference or dichotomous approach) that was
close to the limit of meeting this specification would produce data with degraded precision (a
high blank value implies a variable blank value). It is recommended that the field blank limit of
30jig be reduced to half that value or less. With proper sampler design and filter handling
procedures, mean field blank values of less than 5 ug are readily achievable.
The issue of using a design-specification reference method approach (as was done for the
PM2.5 FRM) or a performance specification approach (used for the PM10 HiVol, resulting in
some method biases) for approval of candidate PM-c methods was discussed. I support a
performance specification approach, since it widens the scope of technologies that could be
candidates for approval. This approach does require careful consideration of the required tests to
assure that a method works well over a wide range of situations, including differences in sample
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chemical composition, wind speed, ambient temperature, mass loading, fine to coarse mass ratio,
etc.
The ability of a method to meet multiple monitoring objectives (beyond only NAAQS
compliance) is important. However, no single PM-c method will meet all the goals noted in Rich
Scheffe's June 18th 2004 memo; that would take a mixture of integrated filter and real-time
(continuous or semi-continuous) methods as is presently implemented in the PM2.5 and STN
networks. The integrated (filter-based) methods (FRM-difference or dichotomous samplers) are
appropriate for trends, compliance with NAAQS, and chemical speciation. Other than the
differences noted above, the only additional comment in this context is that PM-c speciation is
more readily done with a dichotomous sampler, since the coarse-channel filter is mostly coarse-
mode aerosol; this improves the performance of XRF and some other analytical methods, and
also allows more precise measurement of PM-c chemical components that are primarily in the
fine mode (a difficult task when doing PM-c speciation by difference). It would also be useful to
have more information on the virtual impactor design used in the R&P 2500D dichotomous
sampler; it is an "EPA-design" according to the sampler's manual, but the pictures in the 2500D
manual appear to be the classic Loo and Cork design used in earlier commercial dichotomous
samplers, which is not an "EPA-design".
The "continuous" (real-time) methods are essential for public reporting (AQI, AIRNow,
media alerts, etc.), health-effect assessment of PM-c on a sub-daily time-scale, identification of
short-term events, and assessment of diurnal patterns (a useful tool in source identification).
These methods also have the potential to provide much more detailed data at a lower overall
operational cost. I support EPA's goal of wide deployment of continuous methods for PM, but
only as the available technology permits collection of data of useful quality. One of the few
limitations of the available material from the EPA PM-c field study is the lack of information on
sub-daily precision for any of the continuous methods. It would be expected that there could be a
substantial range of performance (precision) on an hourly time-scale across the three continuous
methods, but this can not be evaluated at present. I would like to see a summary of precision
within and across the continuous methods for one and four hour means.
The following comments are on the study's continuous methods and are therefore limited
to the context of daily mean metrics, even though that is only part of the stated monitoring
objectives for the PM-c program. None of the tested continuous methods were without
significant performance limitations, although there are promising candidates.
R&P CM TEOMฎ For all sites except Phoenix/Summer, this method read substantially
lower (20 to 30%) than the reference method, but was well correlated with the FRM difference
method across all sites; this might in part be explained by a 9 |im inlet cut point on the non-
standard 50 LPM PM-10 inlet. However for the Phoenix/Summer test period, this method read
5% higher than the reference (a large intercept but a slope consistent with the other test sites); no
rationale is given for this in the EPA report. The Phoenix/Summer test period was notable for its
very high coarse mass concentrations and coarse to fine mode PM ratios, which may be
associated with the different PM-c TEOM results there. However, it is worth noting that if part
of the explanation for why the R&P CM TEOM method read lower than the reference at the
other three test locations is the low inlet cut point size, that is potentially inconsistent with the
Phoenix/Summer results. Analysis of the APS coarse mode size distribution data may help
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resolve this question. Other aspects of the PM-c TEOM method are desirable, but for it to be
given serious consideration as an acceptable continuous PM-c method the reason for the
difference in response relative to the reference method across the four study sites would need to
be identified and corrected. It is not the low bias at three of the test sites that is of concern here.
It is sufficient for a method to be consistent in its response relative to the reference method
across sites and seasons, although it also helps promote confidence in a method to understand
why any differences exist. As a first step in this process, I would suggest a thorough and
independent laboratory evaluation of the performance of the virtual impactor used in the R&P
CM TEOM - particle loss, collection efficiency, and calculated enrichment factor - for a wide
range of aerosol sizes (0.2 to 15 jim). Any PM-c method that does not use the classic "FRM"
PM10 low-vol inlet needs to have the inlets' performance adequately tested, including tests of
the inlet's aspiration/penetration curve at various wind speeds. The R&P CM TEOM 50 LPM
inlet has recently been redesigned to better match the size cut of the FRM 16.7 LPM PM10 inlet;
ideally both the new and old design should be characterized. If a rigorous wind-tunnel test is not
possible, one simple way this could be done would be to run the FRM PM10 inlet and both (old
and new) R&P coarse mass TEOM PM10 inlets as lowvol PM10 samplers, in a windy and high
coarse to fine mass mode ratio environment such as Phoenix in the summer. Finally, it would be
informative to revisit Phoenix during the summer with the redesigned PM-c TEOM PM10 inlet
to determine if the method still has a different response relative to the FRM difference method at
this site as seen in the first Phoenix summer tests.
Tisch/Kimoto SPM 613D beta dichot. This method performed reasonably well across all
test periods for PM-c but was unacceptably variable for PM2.5 or PM10 for various reasons,
possibly a combination of poor virtual impactor design (excessive coarse particles in the fine
mode channel) and inadequate control for particle-bound water. This defeats part of the purpose
of a dichotomous sampler (to provide data for both fine and coarse mode PM). The Tisch beta
dichotomous method is also likely to have the worst sub-daily PM-c precision of the 3
continuous methods tested, although those data are not currently available. As with the R&P
CM TEOM, if the Tisch continuous dichotomous sampler is to be considered as a viable method,
the virtual impactor performance needs to be thoroughly characterized to help understand why
the method performs as it does.
TSI model 3321 APS. The TSI time of flight method seemed to work best in Phoenix,
regardless of season. Its PM-c correlation there was excellent, although there was a large bias
(approaching a factor of 2). The PM-c correlation with the reference method at the other two
sites was marginal at best. The reason for the large bias remains unexplained even after
substantial efforts by the manufacturer, although this bias may be consistent with other field and
lab work; it may be due to losses in the sampling system or as EPA presented at the AAMM
meeting to the "shape factor" of typical coarse mode aerosols (the APS measures aerodynamic
diameter, but assumes spherical shape when converting the number data to estimated mass). The
ability of the APS to provide detailed size distribution along with a PM-c measurement is a
desirable feature.
To summarize, I suggest additional field testing be done at two sites: revisiting Phoenix in the
summer, and an eastern US site also during the summer. These two scenarios provide different
but challenging environments for PM-c methods. A Phoenix summer test (NOT winter) would
show how the R&P TEOM coarse mass method works with a redesigned PM10 inlet does it
still perform differently in the Phoenix summer environment compared to the other sites? This
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site in the summer could also properly address the coarse particle mass loss issues with the
revised R&P dichotomous sampler (both filter handling and shipping loss). A summer eastern
US location with fine to coarse mode mass ratios of 2 or more (common to much of the eastern
US) would provide a new challenge to all the coarse mass methods - both integrated and
continuous, and provide more information on the performance of the redesigned R&P TEOM
continuous coarse monitor (new PM10 inlet, possibly run at 30ฐC, etc.). None of the EPA study
test sites to date had fine to coarse ratios greater than about 1; as this ratio increases there is the
potential for PM-c data quality degradation for ALL of the tested methods except the APS. Any
interference from the 4% of PM-fme present in the PM-c TEOM method would be most
noticeable at a site with high fine to coarse PM ratios and high dewpoints. For all of these
additional tests, it is critical that they be performed during the summer, not the winter; the
desired fine and coarse PM characteristics and relevant meteorology are a strong function of
season.
There was some discussion at the AAMM meeting of the need to determine the spatial
characteristics of PM-c in advance of network design and deployment, since PM-c has the
potential to be much more spatially variable than either PM2.5 or PM10. The concept of
performing some saturation studies to assess this issue was proposed. If this is done it is
important to use methods that produce PM-c data with good precision; some of the commonly
used PM saturation study methods have insufficient precision for this use. There are also some
existing studies that did PM-c by difference with several sites across urban areas; one such multi-
city study was funded by EPA and done by HSPH in the mid-1990's (Suh et al. 1997, Environ.
Health Perspect, 105:826-834). Data from these existing studies may be useful in this context.
The EPA DQO tool is an interesting and potentially useful approach in assessing the
impact of various changes to a sampling program on its ability to identify uncertainties in
compliance with a "bright line" NAAQS value. This is desirable from a compliance perspective,
but does not address data quality from another critical need: use of PM-c data in multi-variate
health effect studies concurrently with PM2.5 or other pollutants such as CO or NO2 or O3.
There is a need for PM-c data with sufficient precision so that acute (time-series) health-effect
models can properly use PM-C without precision-related biases. Because of the more complex
nature of PM-c measurements as noted above, most existing PM-c data have relatively poor
precision compared to other NAAQS pollutants. This can bias the health-effect estimate towards
the more precisely measured pollutant (White, JAWMA 48:454-458). Most NAAQS pollutants
have precision for daily metrics that are 2 to 5 times or more better than historical PM-c
precision. If we are to make progress in properly assessing the acute health effects of PM-c, it
will be essential to generate data in compliance networks with sufficient precision to minimize
this source of model bias. It remains to be seen if current technologies can achieve this goal in
the context of routine use in SLT networks. As noted in my opening comments, to the extent
that these simulations rely on the results from this field study, the results may not reflect the
performance of these methods when used in routine monitoring networks run by SLT agencies.
This may limit the ability of a DQO tool to properly assess expected network performance when
using the results of this study as input. Finally, the use of PM-c data generated by difference
from HiVol PM10 and LowVol PM2.5 to estimate data quality has limited value, since these two
methods are sufficiently different to introduce various artifacts in the difference PM-c data.
B-22
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Dr. Judith Chow
July 19, 2004
To: Fred Butterfield, Designated Federal Officer, Clean Air Scientific Advisory Committee
(CASAC)
From: Judith C. Chow
Cc: Phil Hopke, CASAC Ambient Air Monitoring and Methods (AAMM) Subcommittee Chair
Subject: CASAC AAMM Subcommittee Consultation on PMcoarSe Method Evaluation
Subcommittee members were asked to comment on the strengths and weaknesses of candidate
methods tested (Question 1), relate these test samplers to multiple monitoring objectives
(Question 2), and evaluate the process used to develop PMcoarse data quality objectives (DQO)
(Question 3). Substantial efforts have been made in the multi-site evaluation and in the
development of PMcoarse DQO. These activities provide a good starting point; however,
additional documentation is needed and more testing may be required to fully address these
questions. The following observations and suggestions address issues regarding: 1) study design;
2) selection of samplers; 3) sample processing and validation; 4) data analysis; 5)
recommendations on further method characterization and development by manufacturer; and 6)
development of PMcoarse DQO.
1. Study Design
No single measurement method can meet all the monitoring objectives for compliance, diurnal
variations, public alert, chemical characterization, source attribution, and long-term trends. A
combination of both integrated and in-situ continuous samplers is needed. The major challenges
in PMcoarSe measurements are: 1) sensitivity of sampling efficiency to inlets with different
sampling effectiveness between 2.5 and 10 jim and 2) large spatial variations. The major features
of mass distributions of particle sizes found in the atmosphere consist of multiple modes. The
peak of coarse modes may shift between -6-25 jim (Lundgren and Burton, 1995). As pointed
out in Chow (1995), small shifts in the 50% cut-point of PMio samplers will have a large
influence on the mass collected because coarse mode often peaks near 10 jim. On the other hand,
a similar shift in cut-point near 2.5 jim will have a smaller effect on the mass collected, owing to
the low quantities of particles in the 1-3 jim size range. See Watson et al. (1983) and Wedding
and Curney (1983) for more on this topic.
In the report (Vanderpool et al., 2004), a table that documents inlet type (e.g., operating
principle), 50% cut-point, slope, and a reference to the tests is needed to provide readers with an
overview of the inlet's cut-point and the sharpness of the curve. (See attached example in Table
1 by Watson and Chow, 2001.) In addition, a diagram of the sampler layout on a horizontal scale
(assuming the same layout is used for every location tested) is needed to evaluate collocated
precision (e.g., two samplers next to each other might agree better than a pair at opposite ends of
the platform). Eight PMcoarse samplers were installed inside the motor home with extended
downtubes. What is the inlet height above ground level? Will the large sample air travel distance,
B-23
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residence times, and particle deposition between the inlet and the filter/sensor result in variations
in PM concentrations? On-site meteorological conditions are useful for the interpretation of the
data. It is not clear from the report where the location of the meteorological sensors is with
regard to the testing platform and what the averaging times are for wind speed (WS) and wind
direction (WD). Gusty winds during the test period are of importance for explaining abrupt
concentrations in the continuous PM measurements.
Given the difficulties in selecting proper locations for testing, some background information
regarding emissions, seasonal variations, and PM composition in each sampled area would be
helpful. PMcoarse may be higher during fall, as agricultural activities increase. Additional tests
under high-wind periods and during fall are recommended.
2. Sampler Selection
Five types of samplers (two types of filter samplers and three continuous methods) were tested,
which included: 1) BGI, Rupprecht & Patashnick (R&P), and Thermo-Andersen Federal
Reference Methods (FRMs); 2) R&P 2025 dichotomous sampler (dichot); 3) R&P PMcoarse
tapered element oscillating microbalance (TEOM); 4) Tisch Dichot beta attenuation monitor
(BAM); and 5) TSI aerodynamic particle sizer (APS). These included two types of PMio inlets,
both of which are based on particle impaction for 10 jim size cuts at 16.67 and 50 1pm. It appears
that the FRMs, R&P 2025, Tisch, and APS used a so-called standard PMio inlet (assumed to be
the louvered SA246 inlet) at 16.67 1pm. Only the R&P PMcoarse TEOM used a 50 1pm inlet.
Virtual impactors are used in three samplers; the major and minor flows varied from 15 and 1.7
1pm in the R&P 2025, 15.2 and 1.5 1pm in the Tisch, and 48 and 2 1pm in R&P TEOM,
respectively. Besides the standard PMio and WINS inlets, are sampling effectiveness curves
available for these other inlets? Will particles overload in a higher-flow-rate sampler in a
polluted environment over the 30-day testing period? Either inlet overloads or high wind speeds
may result in changes in sampling effectiveness curves. In addition, size selective properties of
inlets are not necessarily the same for all sampled particle sizes and for all conditions of the inlet.
The selection criteria listed in Vanderpool et al. (2004) are reasonable, except that the PM2.5
FRM standard cassette does not seem to be a necessary requirement for a candidate PMcoarse
sampler. The IMPROVE sampler, for example, that has performed PM2.5 and PMio monitoring
since 1988 does not use a standard FRM cassette, yet its measurements will be used to establish a
five-year baseline for the Regional Haze Rule (Watson, 2002). Why wasn't the URG FRM
included? For the integrated samplers, only the R&P 2025 sequential dichot sampler was tested
in addition to FRMs. Why wasn't the Thermo-Andersen manual dichot (the "original" dichot)
tested? This manual dichot sampler was the EPA-designated reference method (RFPS-0389-073,
Federal Register, Vol. 54, p. 31247, 7/27/89). It is commercially available and was used
extensively during the 1980s for the U.S. EPA's inhalable particle network. It was tested with
prototype FRMs for PM2.5 (Pitchford et al., 1997). Although there is no PM2.5 FRM for high-
volume samplers (HIVOL), they are commercially available. Also commercially available are
battery/solar-powered mini-volume PM2 5 and PMio samplers (AirMetrics and BGI). High-
volume samplers provide large mass loadings for organic speciation and mini-volume samplers
are useful for better understanding spatial variabilities and conducting indoor/outdoor exposure
assessments. Given that EPA is re-evaluating its current network, these mini-volume and
B-24
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continuous samplers can be used at a so-called Level III NCore site as part of the National
Ambient Air Monitoring Strategy (U.S. EPA, 2002).
For continuous monitoring, the virtual impactor in dichot has the advantage of getting both
PMfme and PMcoarse fractions concurrently through one instrument, but it also introduces
uncertainties in flow splitting and contamination of PMcoarse with PMfme. Given that there are
three PMio Federal Equivalent Methods (FEM), some PMcoarse FEMs may be considered. Using
the same difference technique as is used for the PMio minus PM2 5 FRM, these BAM and TEOM
continuous methods should also be tested with PMio and PM2.5 inlets to better understand them.
Many state and local agencies may already have one or the other type of continuous PM2.5 or
PMio sampler, so these comparisons can give them a better perspective on how to best use their
existing resources.
To avoid the perception that EPA is favoring a certain type of sampler, a summary of surveys on
the existing PM2 5 and PMio instruments used in U.S. ambient monitoring networks should be
given and criteria for selection of candidate samplers should be more explicitly justified.
3. Sample Processing and Validation
For the integrated filler samplers, discrepancies between PM2.5 and PMio mass determination
should be revisited regarding filter cassette shipping and handling, filter equilibrium, corrections
for sample volume, and field flank shipping and handling. Much more stringent requirements in
sample shipping and handling are required for PM2 5 but not for PMi0. "Effective snipping
protocol" (page 23 of Vanderpool et al., 2004) is mentioned without documenting the procedure.
Filter equilibration
Filter equilibration conditions for temperature and relative humidity (RH) are within ฑ3 ฐC
between 15-30 ฐC, and within ฑ5% between 20-45% for PMio, and within ฑ2 ฐC between 20-23
ฐC and within ฑ5% between 30-40%. In conducting gravimetric analysis, the number of days
prior to and after sampling and the conditions for filter storage are required only for PM2.5 and
not for
Sample volume construction
sample volumes are adjusted to sea level pressure and at 25 ฐC for PMio, whereas no
adjustment is used for PM2 5. See page v of Appendix 3 A in Attachment 3, where the standard
condition is used in the analysis (meaning the volume used in the concentration calculation was
adjusted to 1 Atm and 25 ฐC).
B-25
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Field blanks
It is not clear if field blanks were collected during the 30-day comparison and if blank
subtraction was performed. The need for field blank collection and correction should be
investigated. Passive deposition of PMcoarse could be significant in a dusty environment.
4. Data analysis
Vanderpool et al. (2004) detailed the test performance and test results. However, much of the
information is imbedded and it is not easy to make equivalent comparisons. The following
information is recommended:
A table of inlet specifications (as suggested in Section 1); a table providing sampling
effectiveness slopes and cut points with reference to the detailed test report (if it is
available) and the sampling system, such as measurement principles, particle size, inlet
type, inlet and sampling surface, flow rate, filter holder/type, type of flow control, and its
minimum averaging time and detection limits.
Provide time series plots for every testing period on all instruments in an Appendix to
allow a cross-comparison over time and location.
Tabulate all statistical comparisons. Linear regression statistics provide only part of the
information; they do not specify outliers or distribution. Mathai et al. (1990) used several
performance measures to establish the equivalence, comparability, and predictability of
the collocated measurements. For example, percent distribution of uncertainty intervals is
needed to better understand the differences between the samplers.
Criteria for invalidating the data need to be clarified. For instance, on page 6 of Attachment 3,
the precision of the R&P dichot is 3.8% (Table 3) but mean bias ranges from -9.8% to 12.5%.
Notice that total data points in the R&P dichot are 47 compared with 90 for the Tisch.
Using the USC prototype during the January 2004 test appears to be an afterthought. What's the
difference between the R&P PMcoarse TEOM and the USC TEOM? What is the basis for
concluding that the two instruments are identical based on 15 days of measurements at one
location?
What is the purpose of including APS while no particle size data is given? There are several
types of particle size methods available commercially (e.g., Grimm OPC, Dekati ELPI, Climet
OPC, MSP wide-range size classifier). There is no advantage to adding a PMi0 inlet, assuming
the same density of 2g/cm3, and comparing PMcoarse mass with others. The conclusion that APS
has an acceptable level of precision is not supported by the data presented. Mean bias in APS
varied between 50-54% at all sites (see Table 2 of Attachment 3) with overall precision of 19.5%
(Table 3 of Attachment 3). Note that large discrepancies (>2 fold) were found on days 10 and 12
in Figure 14 (p. 22 of Vanderpool et al., 2004) for the collocated APS in Gary, IN. There appears
to be no consistent relationship between PMcoarse mass, and the differences between filter
measurements and APS measurements seem larger with higher concentrations.
B-26
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APS in this comparison is not used properly. Why not examine the particle size distributions for
each location and for differed meteorological characteristics? What are the situations of particle
size distributions under average and extreme conditions? These data may shed light on the
discrepancies among candidate samplers.
5. Recommendation for Further Testing
For collocated PM2.5 and PMio FRMs, these inlets have undergone numerous tests and
appear to be well documented. The PMcoarse determined by difference should be called a
"benchmark" sampler in order not to confuse it with the term FRM. Testing of other
high-volume and mini-volume samplers should also be considered.
The R&P 2025 sequential dichot may be compared to manual Andersen dicot that was
designated as PMio FRM. Various virtual impactors need to be characterized for
sampling effectiveness. Common problems in sequential sampling systems are 1) the
reliability of the transfer mechanism and 2) leakage.
Other PMio and PM2.5 BAMs and TEOMs should also be included to better understand
how the differences in PMcoarse result from inlet function and from different measurement
principles.
The Tisch dichot appears to show the potential for both hourly measurements and their chemical
composition if: 1) the filter punch can be advanced on an hourly or 2-3 hour basis; 2) the filter
media are suitable for subsequent elemental and ion analysis (note that low hygroscopicity
polyfon is listed on page 5 of the report of Vanderpool et al., but a glass-fiber filter is shown in
Vanderpool's presentation); and 3) a light absorption measurement is used as a surrogate for
black carbon. (This is available on some of the Kimoto units.)
Even though the three Tisch instruments show acceptable precision (10%), several improvements
need to be made and tests need to be conducted:
What are the standards used for calibration? Why not calculate volume flow from the
mass flow control by measuring temperature and pressure? Beta attenuation is sensitive
to RH for hygroscopic particles. Maintaining 25 ฐC with an external heater downstream
of the inlet will not minimize the RH interference since atmospheric RH can still be
>65% at 25 ฐC. (Consider Atlanta, GA, or Riverside, CA, during summer.) The sampler
should be heated only when RH is >65%. Smart heaters can be used to achieve RH <
65%. Beta attenuation depends on the atomic number and, hence, particle composition of
the aerosol (Jacklevic et al., 1981). Since the dominant atomic numbers and chemical
compositions of PMfme and PMcoarse differ, their calibration constants are probably
different. To eliminate problems with the flow splitter, another option is to use well-
tested PMio and PM2.5 inlets separately since both channels operate independently.
B-27
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6. Development of PMcoarse DQO
The approach taken here is highly technical, but it appears to be well grounded in statistical
theory. The description of the DQO tool would be improved if it more clearly stated its
assumptions in plain language, probable deviations from these assumptions that are likely to
occur in practice, and the magnitude of uncertainty introduced by various (but probable)
deviations.
The effect of sampling frequency on uncertainties in the 98th percentile and annual average is a
useful feature of this analysis. A less precise indicator (e.g., from the TEOM and BAM) might
provide a more certain estimate owing to its ability to sample every day.
The application applies to measurements at a single site used to determine compliance with
assumed annual and 24-hour forms of a PM NAAQS. The PM2 5 annual average standard uses a
spatial average of measurements at community representative sites. Sites with averages that
differ from the spatial average by more than ฑ20% are removed from the spatial average and
examined against the 24-hour 98th percentile average, along with source-oriented monitors. This
form was intended to better represent the uncertainty of spatial variability and to better represent
population exposure. A larger number of less precise monitors might provide a better estimate of
effects-related exposures if they could be deployed for the same cost as a certain number of
integrated filter samplers. Decisions such as these (i.e., how do I obtain greater benefit for the
same cost) are likely to become more common as EPA's National Ambient Air Monitoring
Strategy (U.S. EPA, 2002) is implemented.
The DQO application does not examine other uses of PM data to improve public health. These
should also be assessed based on a given investment in monitoring resources rather than on an
individual site and sampler basis. It may be that some combination of different monitoring
methods would best achieve the objectives for the same resource investment.
Data uses might include:
Broadcasting air quality alerts and perfecting air quality forecasts (Stockwell et al.,
2002). This would require real-time hourly monitors. The uncertainty of the
measurement would be balanced against the cost of the network. It is probable that
spatial coverage for the same amount of resources would be the deciding point.
Adding an additional monitor probably provides more benefit than a 5%
improvement in precision.
Source identification. Time resolved measurements can be correlated with wind
direction and identifiable events. Filter samples can be analyzed for more specific
source markers. Some combination of different sampler types for the same resources
might allow both.
Source amelioration and emission inventory. A real time measurement would allow
an inspector to identify a problem and immediately remediate it. Over the long-term,
examination of deviations from diurnal patterns would help to identify sources that
are not included in the inventory and when they are most likely to occur.
B-28
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Future health relationships. Outdoor human exposure could be better quantified with
shorter duration, more frequent, and spatially diverse sampling. Presumably,
epidemiological relationships between adverse health effects and monitoring results
would be better with more frequent and spatially diverse measurements from
continuous monitors. They would also be enhanced by more specific chemical
compositions that are currently only available from integrated filter samples.
To improve the analysis, the DQO model needs to incorporate spatial distribution as well as
optimization criteria for additional air quality monitoring objectives that are above and beyond
the determination of compliance with NAAQS. The overall objective should be to optimize
monitoring networks to improve public health at the least cost to the economy and within a given
allocation of monitoring resources.
References
Chow, J.C. Critical review: Measurement methods to determine compliance with ambient air
quality standards for suspended particles; JAWMA 1995, 45(5), 320-382.
Lundgren, D.A.; Burton, R.M. Effect of particle size distribution on the cut point between fine
and coarse ambient mass fractions; Inhal. Toxicol. 1995, 7(1), 131-148.
Jaklevic, J.M.; Gatti, R.C.; Goulding, F.S.; Loo, B.W. A beta-gauge method applied to aerosol
samples; Enivron. Sci. Technol. 1981, 75(6), 680-686.
Mathai, C.V.; Watson, J.G.; Rogers, C.F.; Chow, J.C.; Tombach, I.H.; Zwicker, J.O.; Cahill,
T.A.; Feeney, P.J.; Eldred, R.A.; Pitchford, M.L.; Mueller, P.K. Intercomparison of
ambient aerosol samplers used in western visibility and air quality studies; Enivron. Sci.
Technol. 1990, 24(7), 1090-1099.
Pitchford, M.L.; Chow, J.C.; Watson, J.G; Moore, C.T.; Campbell, D.E.; Eldred, R.A.;
Vanderpool, R.W.; Ouchida, P.; Hering, S.V.; Frank, N.H. Prototype PM2.5federal
reference method field studies report-An EPA staff report; U.S. Environmental
Protection Agency: Las Vegas, NV, 1997. http://www.epa.gov/ttn/amtic/pmfrm.html.
Stockwell, W.R.; Artz, R.S.; Meagher, J.F.; Petersen, R.A.; Schere, K.L.; Grell, G.A.; Peckham,
S.E.; Stein, A.F.; Pierce, R.V.; O'Sullivan, J.M.; Whung, P.Y. The scientific basis of
NOAA's air quality forecasting program; EM2002(Dec.), 20-27.
U.S.EPA. National ambient air monitoring strategy (Second draft); U.S. Environmental
Protection Agency: Research Triangle Park, NC, 2002.
http://www.epa.gov/ttn/amtic/files/ambient/monitorstrat/compms.pdf
Vanderpool, R.; Ellestad, T.; Handley, T.; Scheffe, Richard, Hunike, E.; Solomon, P.; Murdoch,
R.; Natarajan, S.; and Noble, C. Multi-Site Evaluations of Candidate Methodologies for
Determining Coarse Particulate Matter (PMC) Concentrations; Draft Report; 2004.
Watson, J.G. Visibility: Science and regulation; JAWMA 2002, 52(6), 628-713.
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Watson, J.G.; Chow, J.C. Ambient air sampling, in Aerosol Measurement: Principles,
Techniques, and Applications, Second Edition, 2nd ed., Baron, P., Willeke, K., Eds.; John
Wiley & Sons: New York, NY, 2001, pp. 821-844.
Watson, J.G.; Chow, J.C.; Shah, J.J.; Pace, T.G. The effect of sampling inlets on the PMi0 and
to TSP concentration ratios; JAPCA 1983, 33(2), 1 14-119.
Wedding, J.B.; Carney, T.C. A quantitative technique for determining the impact of non-ideal
ambient sampler inlets on the collected mass; Atmos. Environ. 1983, 77, 873-882.
Table 1. Size-selective inlets and characteristics for ambient aerosol sampling (Watson and
Chow, 2001).
Name, Manufacturer,
References
Inlet ID: dso (um), Slope,
Flow (1/min)
Flow Rate Description and Comments
(L/min)
Impactor
Airmetrics Minivol
Impactor (ARM)
(Turner, 1998; Wiener et
al., 1992)
Harvard Sharp Cut
Impactors (ADE)
(Marple et al., 1987;
Turner etal, 2000)
URG Impactors
URG-2000-30DBE
Impactor
URG-2000-30DBF
Impactor
URG-2000-30DBN
Impactor
URG-2000-37F
Impactor
URG-2000-25A
Impactor
Impactor/Elutriator
MV10: ~10,NA,5
MV2.5: ~2.5,NA,5
MST123: 1,1.22,23
MST24: 2.5,1.02 ,4
MST210: 2.5,1.06,10
MST220: 2.5,1.25,20
MST104: 10,1.11,4
MST1010: 10,1.09,10
MST1020: 10,1.06,20
URG25AA:1,NA,4
URG25PAB:10,NA,4
URG30DBE: 10,NA,16.7
10 urn
10 urn
10 urn
2.5 urn
2.5 urn
5
5
4
16.7
16.7
32
2
4
Available in machined polymeric propylene
plastic or machined aluminum. PM10 and PM2 5
inlets are used in series for PM2 5 sampling.
Apiezon vacuum grease dissolved in hexane is
pipetted onto impaction surfaces before each
sample to minimize re-entrainment.
Machined aluminum.
Used for personal paniculate sampler and indoor
air monitoring.
Anodized PM10 inlet.
Used on URG dual sequential fine particle sampler
and URG weekly air particle sampler.
Versatile Air Pollutant Sampler (VAPS) Teflon-
coated PM10 inlet.
Used with 37 mm impactor filter pack.
Used for personal paniculate sampler and indoor
air monitoring.
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Name, Manufacturer,
References
Inlet ID: dso (um), Slope,
Flow (1/min)
Flow Rate
(L/min)
Description and Comments
Andersen (GRA) hivol
PM10
(Kashden et al.
(1986);McFarlandetal.,
1984); Ranade et al.,
(1990);Weddingetal.
(1985)
Andersen (GRA)
medvo!PM10
Olin and Bohn( 1983)
Andersen (GRA, R&P,
URG) lovol'Tlat Top"
PM10
McFarland et al. (1978);
Van Osdell and Chen
(1990); Wedding etal.
(1980)
FRM (BGI, GRA, R&P,
URG)lovol "Curved
Top" PM10
Federal Register (1997)
EPA (BGI, GRA, R&P,
URG) Well Impactor
Ninety Six
Federal Register (1997);
Kenny et al. (2000)
Virtual Impactor
Andersen (GRA)
dichotomous virtual
impactor, McFarland et
al. (1978)
VAPS (URG) Virtual
Impactor
Cyclone
Wedding (GRA) IP10
(Wedding et al., 1982)
Andersen SA246B2.5
Andersen 3.68 Cyclone
(modified AfflL)
G1200:
9.7,1.4, 1,133
SA254I: 10,1.6,113
SA246B:
10.2,1.41, 16.7
Curved Top PM10:
10,NA,16.7
WINS: 2.48,1. 18,16.7
SA24 1:2.5 um,NA
VAPSVI:2.5,NA,32
IP10: 9.6,1.37,1133
2.5 urn
2.4
2.7 urn
1.16
2.3 urn
1.18
1,133
113
16.67
16.67
16.67
32
32
1,133
16.67
24
28.1
Anodized spun aluminum with a single stage of
opposing jets. The body is hinged to facility
cleaning and re-greasing of the removable
impaction plate that is sprayed with an aerosol
adhesive after cleaning. The G1200 was preceded
by the SA-320 single stage PM15 inlet and the
SA321A and SA321B dual stage PM10 inlets that
are no longer sold but may still be in use. It is not
entirely clear which sampling effectiveness tests
apply to each of these inlets.
Spun aluminum with ten impactor jets and a
central elutriation tube. The inlet can be
disassembled for cleaning. The SA254I was
preceded by the S A254, or "Blue Head" owing to
its enamel painting that was nearly impossible to
disassemble for cleaning.
Machined aluminum with three parallel impactor
tubes and a central particle elutriator tube. Rain
drops are blown into the inlet beneath the flat top
and accumulate on the impaction surface. Water
exits through a small drain attached to a bottle on
the outside of the inlet. The top unscrews for
cleaning impactor surfaces.
Same materials and design as the SA246B but
with a top that curves over the inlet bug screen to
minimize the entry of windblown raindrops.
Machined aluminum well with a detachable
impactor jet. The impaction surface consists of a
37 mm quartz fiber filter immersed in 1 ml of
vacuum pump oil to minimize particle re-
entrainment over multiple sampling days between
cleaning.
RFPS-0789-073. Designated for PM10
dichotomous sampler only.
RFPS-1087-062. Inlet cleaning port on top of
inlet.
Typically used with SA246B PM10 inlet.
Used on Andersen RAAS speciation sampler.
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Name, Manufacturer,
References
Inlet ID: dso (um), Slope,
Flow (1/min)
Flow Rate
(L/min)
Description and Comments
BGI GK-2.69 Cyclone
BGI SCC- 1.062 Cyclone
BGI SCC-2.229 Sharp
Cut Cyclone
BGI SCC-A and SCC-B
Sharp Cut Cyclones
IMPROVE Cyclone
(modified AfflL)
MetOneSCC-1.118
Sharp Cut Cyclone
Rupprecht & Patashnick
SCC-1.829 Sharp Cut
Cyclone
MetOneSCC-2.141
Sharp Cut Cyclone
MSA
Sensidyne BDX99R
SKC Cat. No. 225-01-02
Cyclone
URG-2000-30EHB
Cyclone
URG-2000-30EAM
Cyclone
URG-2000-30ENB
Cyclone
URG-2000-30EA
Cyclone
URG-2000-30ED
Cyclone
URG-2000-30EN
Cyclone
URG-2000-30EH
Cyclone
URG-2000-30EC
Cyclone
Stacked Filters
Nuclepore Filters
BGI CIS Foam
10 urn
4.0 urn
1.0 urn
1.21
2.5 urn
1.20
4.0 um
1.22
1.0 um
1.17
2.5 um
1.19
2.5 um
2.5 um
0.81
2.5 um
1.23
2.5 um
1.24
0.78 um
4 um
1.56
5 um
1 um
10 um
10 um
10 um
2.5 um
2.5 um
2.5 um
1.37
3.5 um
10 um
4 um
2.5 um
1.62
4.2
3.5
1.5
1.05
16.7
16.7
22.7
2
5
6.8
2
1.7
1.9
16.7
15
16.7
28.3
o
J
10
16.7
28
3.5
3.5
3.5
PM10/thoracic - oil mist.
High flow respirable - silica.
For indoor air quality PM\ use. The inside
diameter of the cyclone is 1.062 cm.
For indoor air quality PM2 5 use.
For indoor air quality respirable use.
For use with the BGI PQ200. The inside diameter
of the cyclone is 2.229 cm.
For use with the BGI PQ200. Equivalent to EPA
WINS.
Modified Air Industrial Hygiene Laboratory
cyclone.
The inside diameter of the cyclone is 1.118 cm.
The inside diameter of the cyclone is 1.829 cm.
The inside diameter of the cyclone is 2.141 cm.
Generally used in personal sampling applications.
Formerly the Gilian version BDX 99R. Generally
used in personal sampling applications.
Generally used in personal sampling applications.
Aluminum surface with Teflon coating.
Aluminum surface with Teflon coating.
Aluminum surface with Teflon coating.
Aluminum surface with Teflon coating.
Aluminum surface with Teflon coating.
Aluminum surface with Teflon coating.
Aluminum surface with Teflon coating.
Aluminum surface with Teflon coating.
For indoor air quality PMio/thoracic use.
For indoor air quality respirable use.
For indoor air quality PM2 5 use.
B-32
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Mr. Bart Croes
U.S. EPA's PM Coarse Methods Evaluation and Data Quality Objectives
July 22, 2004 Consultation Meeting
CASAC AAMM Subcommittee Review Comments, Bart Croes
Overall, the multi-site PM coarse sampler intercomparison and performance-based approach to
determining data quality objectives (DQOs) represent an impressive initiative by U.S. EPA to
take a systematic approach towards implementation of a likely coarse particle (PMc) National
Ambient Air Quality Standard (NAAQS). U.S. EPA's equal emphasis on continuous samplers is
refreshing as these are a vital component of a comprehensive air quality monitoring program.
Time-resolved, real-time availability of PM data are necessary for use air quality index (AQI)
forecasting and burn allocations, and can lead to a better understanding of emission sources,
transport, background levels, deposition, and health effects of PM. I appreciate the opportunity
to comment during this intermediate stage of the process. The documents provide a thorough
description of the monitoring methods and protocols for the intercomparison, clearly explain the
monitoring results, and provide a reasonable rationale for the development of and inputs to the
DQO software tool. I agree with the basic approach taken by U.S. EPA, and offer comments on
several aspects that need further attention. My comments address the three basic questions posed
by Rich Scheffe in his June 18, 2004 memo to Fred Butterfield, as well as additional issues
raised by U.S. EPA staff and others at the consultation meeting.
Responses to Questions
For Questions 1 and 2 regarding the strengths and weaknesses of each method tested for
purposes of a reference method or measurement principle, and to meet multiple monitoring
objectives, I completely agree with the comments made by Peter McMurry (as replicated below
with minor edits).
B-33
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PM Monitoring Method Strengths and Weaknesses (adapted from Peter McMurry)
PM Monitoring Method
Strengths
Weaknesses
PMcFRM = PM10FRM
minus PM2.5 FRM
1. Uses established monitoring
equipment
2. Filters can be analyzed for
particle composition
1. 24-hour time resolution
2. Expensive, manual filter analysis
3. Not useful for real-time AQI
reporting
4. Involves collection of particles on a
filter, rather than direct
measurements of gas-borne particles
R&P 2025 Sequential
Dichotomous Sampler
1. Single sampler for both
fine and coarse
mass/composition
2. Less influenced by
fine/coarse missing than
PM10-PM2.5
3. Filters can be analyzed for
particle composition
1. 24-hour time resolution
2. Expensive, manual filter analysis
3. Not useful for real-time AQI
reporting
4. Involves collection of particles on a
filter, rather than direct
measurements of gas-borne particles
R&P Continuous Coarse
TEOM Monitor
1. Fast time response for better
information on temporal
exposures
2. Established track record
3. Data can be used for real-
time public reporting
4. May currently have greater
potential for accuracy than
other methods.
1. Volatilization losses for high
temperature collection
2. Apparent sampling losses for coarse
particles
3. Involves collection of particles on a
filter, rather than direct
measurements of gas-borne particles
Tisch Inc. Model SPM-613D
Dichotomous Beta Gauge
1. Fast time response for better
information on temporal
exposures
2. Established track record
3. Data can be used for real-
time public reporting
1. PM2.5 mass in poor agreement with
those from other samplers
2. Involves collection of particles on a
filter, rather than direct
measurements of gas-borne particles
TSI Inc. Model 3321
Aerodynamic Particle Sizer
(APS)
1. Provides valuable
supplemental information
on size distributions
2. In-situ measurements of
gas-borne particles (the
type we breathe!)
1. Not a mass measurement method;
should not be considered as such
For Question 3 regarding the appropriateness of the uncertainty estimates and completeness of
the factors considered for the DQOs, my expertise is limited. Since this is a new, relatively
untested software tool, the results should by reviewed by monitoring experts at U.S. EPA and
State, local, and tribal (SLT) agencies to ensure that they match common sense. In addition, U.S.
EPA and other staff associated with AirNow and the air quality epidemiology community should
be consulted on DQOs for their uses of PM monitoring data.
B-34
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Other Issues
Further analyze the sampler intercomparison data.
Suggestions for further analysis include consideration of speciation data (including anions),
particle size data (from the APS), and meteorological data to further understand sampler
performance and differences.
Define the potential scope of a national PMc monitoring network.
While U.S. EPA has not yet promulgated a coarse particle NAAQS, it has released a Staff Paper
with a proposed range of possible standards for PM2.5 and PMc. As a first-order estimate, data
from the existing PM10 monitoring network should be compared to the proposed lower and
upper ranges of the coarse particle recommendations to determine if the potential scope of a PMc
monitoring network would be national in scale or restricted to a few states. In these likely
nonattainment areas, PM10 would primarily consist of the coarse fraction. Sites that have
collocated PM2.5 and PM10 monitors, or SLT agencies that have operated dichot samplers (see
Motallebi, et al., 2003ab for California) provide more relevant data. A list and map of sites with
PM10 only, PM2.5 only, and both would be a useful summary.
Allow PM10 monitors to be used to determine attainment.
U.S. EPA and SLT agencies have already invested huge resources into the current PM10 and
PM2.5 monitoring networks. Several states (i.e., California) have State ambient air quality
standards for PM10 and do not plan to follow U.S. EPA in adopting a coarse particle standard.
Surely if a site meets the PMc standard with PM10 monitoring data (uncorrected), then there is
no need to deploy a PMc-specific monitor at the site.
Analyze special studies to determine spatial distributions of PMc.
A key issue for potential PMc nonattainment areas is the number of monitors that need to be
sited to properly represent population exposure. The California Air Resources Board and
perhaps other SLT agencies have conducted special studies. One example is a PM saturation
study conducted with mini-vols during 2000 in Corcoran, an agricultural community in the San
Joaquin Valley with high dust levels. Similar studies may have been conducted in Las Vegas
and Phoenix.
Define the difference method as the Federal Reference Method (FRM).
It is unclear from the documentation provided to the committee whether or not U.S. EPA will
allow the difference between PM10 and PM2.5 FRMs to be defined as the PMc FRM. My
recommendation is to allow this in consideration of the huge resources that have already been
invested into the current PM10 and PM2.5 monitoring networks, and the excellent precision
results obtained in the multi-site sampler intercomparison study. After all, a difference method is
already used to determine NC>2 levels.
Devote resources to developing a traceable standard for PM.
Problems with the TEOM (page 18, section 5.3, unit three) and APS (page 22, section 5.5, unit
two) were only discovered during the intercomparison study because multiple units were
carefully operated by U.S. EPA and monitoring industry experts. If the units were operating by
themselves in an SLT agency monitoring station, it is unlikely that instrument drift and other
B-35
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problems would have been noticed. Without the ability to challenge a PM analyzer with a know
concentration of PM, all you have to verify proper operation of an analyzer is the "due diligence"
of the site technician. If U.S. EPA took a dozen of the candidate samplers and sent them to 12
randomly selected SLT agencies, after four months they would get a dozen different regressions
and correlations, no matter how consistent the analyzers performed in the controlled, three-city
study.
Other continuous, criteria pollutant monitors (O3, NO2, CO, SO2) are challenged with a known
concentration each day (the in-station zero and span checks) and at six- and twelve-month
intervals (independent transfer standards). For filter samplers the micro-balance used to weigh
the filter is similarly "zeroed and spanned" with NIST-traceable standard weights each weighing
session. Ozone does not come in a bottle, but accurate and precise quantities are generated on
demand to challenge ozone analyzers. Resources should be devoted to research an accurate and
precise PM generation system. I realize this would be very difficult to do with PM, but perhaps
something similar to an aerosol inhaler (used for administering asthma medication) could be
developed.
Considerations for a follow-on sampler intercomparison study.
Since U.S. EPA resources are to limited to two sites at most for a follow-on study, consider the
use of a PM Supersite (i.e., Fresno with the high PMc and PM2.5 nitrate during the fall harvest
season) and revisit Phoenix in the summer. SLT agencies should be involved to duplicate "real-
world" operation. Perhaps the existing dichot monitor and high-volume PMIO and PM2.5
monitors (that are already deployed by some SLT agencies) should be included to determine
their suitability to determine if an area meets the NAAQS (e.g., they do not have negative
biases).
References
Motallebi, N., C. A. Taylor, Jr., K. Turkiewicz, and B. E. Croes (2003a) Particulate matter in
California: Part 1 - Intercomparison of several PM2.5, PMio-25, and PMio monitoring networks.
J. Air Waste Manage. Assoc., 53: 1509-1516.
Motallebi, N., C. A. Taylor, Jr., and B. E. Croes (2003b) Particulate matter in California: Part 2
- Spatial, temporal, and compositional patterns of PM2.5, PMio-25, and PMio. J- Air Waste
Manage. Assoc., 53: 1517-1530.
B-36
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Dr. Kenneth L. Demerjian
CASAC AAMM Subcommittee Consultation on PM Coarse Methods Evaluation
July 22, 2004 Meeting at Research Triangle Park, NC
Review and Comments: Kenneth L. Demerjian
Overall the project reports (Multi-Site Evaluation of Candidate Methodologies for Determining
Coarse Paniculate Matter (PMc) Concentrations; Use of a Performance Based Approach to
Determine Data Quality Needs for the PM-Coarse (PMc) Standard; General characterization of
PMc as found in the U.S., based upon data from current network of PMioand PM25monitors)
provided by OAQPS to the AAMM subcommittee were well written and very informative. This
work provides an excellent foundation with regard to the measurement challenges and the issues
that must be addressed to deploy a credible PMc measurement network. OAQPS is to be
commended for this initial effort to characterize several commercial samplers configured to
measure PMc and provided some basic performance statistics on their mass measurement
capabilities relative to what some might argue is an arbitrary measurement standard (i.e., PMio
FRM - PM2.5 FRM). These studies should provide the setting to assess overall performance of
the sampling systems and the rationale for their observed differences. Unfortunately the current
analyses fall short of the latter step, but may benefit from further analyses as the PM chemical
compositional data collected as part of this program become available.
The AAMM Subcommittee was asked to focus their consultation around three major questions:
1. What are the strengths and weaknesses of each method tested in the ORD study for purposes
of using it as a reference method, a measurement principle, and as a method that would provide
the basis for approval of candidate reference and equivalent methods?
2. What are the strengths and weaknesses of each method tested to meet multiple monitoring
objectives such as comparison to potential PMc standards, public reporting, trends, chemical
speciation, and characterization of short-term episodes and diurnal variation?
3. For the PMc DQOs, is the process the Agency took to develop the estimates of uncertainty
appropriate? Are there factors the Agency has included that should not be considered or are there
other inputs that should be included?
A summary of comments & recommendations as they related to questions 1 and 2 are report in
Table 1. Overall, the currently multi-site evaluations of candidate methodologies for determining
coarse PM concentrations suggest that only the direct FRM PMio-PM2.s differencing can provide
unequivocal high precision measurements of the PM coarse fraction (operational standard).
Although this approach will likely have to be the operational reference method, every effort
should be made to establish equivalency with one or more dichotomous filter based and
continuous PMc mass monitoring systems. Performance issues associated with these alternative
methods relative to the FRM need to be addressed through more systematic studies than those
presented herein. Although a reasonable start, the number of environments sampled and the
sampling periods considered in this study is insufficient to draw final conclusions regarding
instrumentation recommendations for a PMc monitoring network.
B-37
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I. Siimmun. ol'PM < ourse Methods l'.\alnation Refills
Measurement
Method
I nlegrated FRM
Integrated DichiJt,
Sequential
Tl-OM
PM
Men-it
I'M-
PM
PMr.
PM: ซ
PMc
PM,,,
PMc
Sampler
Man tifiU'tii rer( s)
BGI, R&P. AND
R&P 2025
R&P
Performance Metric
1 50i 3 4% (4 |P
3e>ฐซ 4 Iป.(4)P
24% 4 7% (4 IP
1 j%-3 8rl,,rV
I 00-1 09mDFRM
1.7%-4.2%CV
0 7Q-0 % niD'FRM
I 2%-3.0%C'V
0 84-0 '17 niD FR\!
i 7ฐ-i-(j.b%fV
06Q-1 05 TEOM FRM
Caitimenls/RecommcndAtionit
1 The data snu^est th;H these methods fulfill EPA's itxiuirerrieiit^
(precedent set bv PM2.5I to he designated as a reference method and
can be used to pr in ule ?t Ix'i^is i'or approval of candidate lefereiice and
equivalent methods. T!ie(ie\elo})ineni ofalKoluiecalibiaticsn Mandatds
Ibt the |>erioniiiiiK'e testntu. of FRMji, reiTiam an di^ive sciL'tUific
clnd[ei'iL;e to the aeuwol cmtir^unity
2 Nei;au\e^ a 1 gravmieti ic melhcKls are costly and lalwi in^et^jve
iei|tn]m_y st.-\cjia] i>pejaUii jiueivL-^iions, bHliiTerencitiL' independent
(PMIO and PM2 5) measurements doubles upeiational costs and
iet|unea I'tsgli ptecisioi^ in the independent niea^ui'eitieriis
1 More data eetds to b-e cidlecied and analy/ed ti> detesunne il lite
diffeit-nces t spoiled aieonls due to the mecliaincal peribimance of the
dieliotonuHLs samplers or aie alsif alTecieti by the clteitnea!
compOMUoi) of tile ambient aerosols. Noi leadv as a rei'eiencc rncthsxJ,
Init nii>sl likeh1 to establish equivalency oiaee iinlhef testing and
analvsts identifies souict"(s| ol' sampler bias
2 Neiiames algsanmetnc ineilnats aiecostlv and lishtu intensive
iet|U!iniLf se\era! opeialoi inter veiations, Positne. a} dMec! coilecliftn
of PMc fiactnn'1 eliminate!, ditTereiicinj! iridejwndent (PMIO and
PM2 5) nieasineiiietus seducniLf ccisi and jwecrsum denuyidsi. b) well
suited foi clieiincal s^cration jx>sl anah'Sss,
I Moie data needs to he collected and analvved ID deleinmie if the
dii'iaencei lepoited are only due to the niecliameal peribnnance of the
sampler snlet^ 0 e internal cuipomts ^ uiti \ s lOuiriJ 01 are also
affected bv tile chemical ctimposilKin of the ambient aerosols. Not
ie.adv as a fefeience loethod,, hut rm>s^ likely to establish etjinv^leiKv
once furthei te;ป!nia and analysis identifies source)*) of sampler bias.
2 Posdues a) ContinuiHis dneci mass measurement of PMc has maior
cost and d;Ma utility ad\ ai'iUiges over filler based ^lavtmetiic niedu>ds
(i e Ingh iitne resolved data, real-time da^i di-s|>hiv)
TABI.li I. Siimman ot'PVt t uurso Motlnuls [-.valuation Refi
Beta Anenttation
TimeofHtsiht
(APS)
PM; <
PMc
PM
PMe
TtschSPM-bl3D
TSI
C'V's not re]H>iled
I2b-l 70Tach/FRM
O.fll-1 08 Tisch/FRM
I 09-1 2Q Tisch/FRM
t'Vs not rqwrled
0 42-0 62 APS 'FRM
1 As wtlli R&P PMc TEOM tnoie data needs to be collected and
analyzed to determine if the differences repented are only due lo the
meclianical pei fot tnance of tlte dichotatnous saniplei (Why diws the
R&P 2025 dichot gives dilTerent resiilB than the Tisch Diclwt'.') or aie
also affected by the chemical composition ol'the ambient aeiosols Not
teady as a lefeience method
2 Positives a) Continuous direct mass measurement of PMc lias majoi
cosl and data utility acK anutges over filler based gia*, itnetric ntetluxls
(i e. hii-h lime resolved data, real-nine data display)
1 Many tjueปitons remain to be addressed if the APS is to be considered
a viable candidate lor PMe measurements, including sensidv ity 10
variation in PM chemical composition, relative humidity effects and
variations in pai tide density Not leady as a reference method
2 Positives i) Continuous direct mass measurement of PMc has maior
cost and data utility advantages over filler based giavimetric meihods
(i e high lime resolved data, leal-umedaia display)
B-38
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The availability of chemical composition data for PMc will prove valuable in addressing
performance difference between the FRM and other methods. For example, the R&P coarse
TEOM vs. FRM differences may well be explained by volatile losses of nitrate and semi-volatile
organic aerosols, rather than by an inlet cutpoint issue.
The DQO process as outline in attachment 3 ["Use of a performance based approach to
determine data quality needs for the PM-coarse standard"] to develop qualitative and quantitative
statements regarding PMc data, provide estimates of uncertainty and potential levels of decision
error seems reasonable and should prove to be a useful tool for regulators/decision makers. It is
difficult to assess, based on the write-up provided how user friendly the DQO tool is and if it will
gain mainstream acceptance by decision makers. It does raise a fundamental question as to
whether or not such a detailed statistical assessment creates false expectations with regard to the
FRMs ability to measure the true absolute mass concentration of ambient paniculate matter.
B-39
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Dr. Delbert Eatough
Review Comments
Delbert J. Eatough
Professor of Chemistry
Brigham Young University
I. Multi-Site Evaluation of Candidate Methodologies for Determining Coarse Particulate Matter
(PMc) Concentrations
A. General:
EPA has conducted a multisite study to evaluate several methods for determining PMc in
anticipation of the setting of a new standard by EPA. As a result of a court decision, EPA can
not set the previously anticipated PM2.5 and PMio standards because the fine paniculate material
is included in the PMio standard. Hence, if EPA decides to set a new non-PM2 5 standard, it must
include material in a decidedly different size range. The draft document from EPA assumes this
will be a PMi0to 2.5 size range and give this measurement the title PMc in the document. It seems
to me that since the standard is indeed new, and not just a continuation of the old PMio standard,
that EPA should give some thought in the document as to justification of the supposed new size
range. It's relationship to the old PMio standard is obvious. However, what is not obvious is
that the PMc as defined in the document is the best choice for a new standard.
The old PMio standard was a compromise between what was readily achievable in
sampling at the time and what was know about lung deposition patterns. The fine particles are
now separated out from the coarse in the path EPA is taking. Putting aside the argument of
whether a 1 or a 2.5 cut is the better cut between the fine and coarse particle modes, the new
PMc standard is clearly focused on particles larger than the combustion particles and secondary
products which dominate the fine particulate range. Is the sole use of the new PMc standard to
circumvent the court ruling and yet maintain the ability to track changes in what is happening
relative to the old PMio standard, or is the new standard really intended to generate data which
will further indicate the epidemiological need (or lack thereof) for control of coarse particles. If
the latter is the case, EPA should give thought to the cut point selected for the new standard. It
should represent our best understanding of lung deposition and possible exposure to coarse
particulate matter. As I understand it, this point is not well met with a PMc standard with a
range of 2.5 to 10 //m.
In addition to justifying the standard from a physiological point of view, EPA should also
justify the chosen standard from a sampling point of view. With the choice of an upper cut of 10
//m, EPA has virtually insured that various sampling techniques with different outlets will not be
comparable. This puts the upper cut at the peak of the coarse particle size distribution for many
environments and means that small changes in the inlet system will insure non-comparability of
different methods. I suggest below that this effect is at the heart of the reasons for differences
seen in some of the comparisons given in the manuscript. While the poor choice of a cut point
may have been somewhat livable with the old PMio standard, where at least the influence of the
B-40
-------
fine particulate mode was an ameliorating influence, the problem is much more severe with a
PMioto 2,5 standard. EPA needs to be careful that it is not boxing itself into such a sampling hole
with the chosen size cut for the PMc standard that it cannot approve sampling techniques which
may be much better then the "standard" of the difference measurement as outlined in the
document now before the committee.
B. True Intercomparability of the Various Samplers Used:
A basic premise of the study is that the 2.5 and 10 //m cut of the various samplers are all
identical. Without this intercomparablility, the causes of any difference seen are not identifiable.
However, the cut points, especially the more sensitive 10 //m cuts points are not comparable.
Arguments are made about losses in some of the systems. However, if the true cut points and the
shape of the cut point curves are not know, meaningful comparisons between the various
samplers are not possible. There are several points where EPA can improve the information in
the report in this respect.
The characteristics of both the cut points of the FRM PM2.5 and PMio samplers have been
studied and reported in the literature. EPA has defined carefully the nature of the inlet devices
for both these samplers so that variability from manufacturer to manufacturer is minimal.
However, the same care is not taken in the samplers chosen for incomparability in this study.
Details related to this point need to be provided. Specifically:
1 . The Dichot Sampler. Is the PMio inlet in the dichot sampler identical to that used in
the PMio sampler? If not, what differences are known about the shape of the inlet curve? These
differences will directly affect the total mass entering the sampler. What is known about the
difference between the cut point of the fine - coarse splitter in the dichot and the WINS impactor
of the PM2.5 sampler. For both of these important cut-points, do the size distribution data
obtained with the APS indicate that a specific bias would be expected in the various studies and
is the nature of the bias expected to be different for the different sampling locations?
2. The Tisch Inc. Beta Gauge. The virtual impactor which makes the 10-2.5 cut is
stated to be different for the Tisch samples. Details on the design and what is known about the
shape of the curve in the 2.5 //m cut region should be given. Again, do the APS data predict any
bias in the results due to the nature of the cut around 2.5 //m?
3. R&P Continuous Coarse TEOM Monitor. The situation is even more complex for the
continuous R&P instrument. Both the sensitive 10 //m cut point and the 2.5 //m cut point are
different from the FRM samplers. What is know about the nature of the two curves and what do
the APS data predict bias will be because of the nature of the shape of the two cut points? It is
not specifically so stated, but I assume that the TX40 filter of the measuring TEOM is kept at 50
4. The APS Instrument. I am a bit confused about the need for a splitter after the
inlet in this instrument. Why could not the APS data themselves identify the lower cut? More
about the assumed density later.
B-41
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5. Finally, a general comment on the various TEOM data. Were the instruments all run
without the slope and intercept offsets suggested by the manufacturer so that we are looking at
true results and not artificially altered results?
C. Comments on the Data Presentation:
1. It would be very useful if the figures related to the various studies were all
comparable. For example, Figure 9 for the Phoenix study shows that the mass weighing for
PMio at the site and at RTF were comparable. However, it also includes the PM2.5 data and
clearly shows that difference around the 2.5 //m cut for the various instruments will have a minor
effect on the results because fine particle were a minor contributor to the total for all data points.
Similar data in the plots for results obtained at the other sites would be informative.
2. A Table of the various fine and coarse matter results would be helpful to the reader.
Presently these values are scattered throughout the manuscript. The tables all refer to differences
as a % of the measured as compared to the control. However, the importance of various
mechanisms which can contribute to errors will be a function of the relative importance of total
mass in the fine and coarse size ranges. A Table which provides these averages in a convenient
place would be very helpful to the reader.
3. There is a general reliance on the presentation of regression slopes and R2 values in
the discussion of the sampler comparison. Some consideration of the calculated intercepts and
the total measured mass might give a better picture. I would like to see X, Y graphical
comparisons of all the data. Such a visual presentation often suggest bias or other effects not
readily apparent in just linear regression results.
4. I am confused by Figure 10. The heading says the comparison is for dichot vs. FRM
PM2.5 data. However, the axis says it a comparison of FRM PM2.5 and Dichot PMc results.
Inclusion of all the linear regression analyses in the insert boxes further confuses the issue. As
stated in the previous point, I would like to see comparison PM2 5, PMio and PMc X/Y plots for
each of the studies. This may reveal details hidden in the limited regression results given in the
paper now.
D. Specific Comments on the results:
Figure 11. How can you be certain that the difference observed in Figure 11 is due to
mass loss during filter movement and not due also to the differences in the PMio inlet cut point
curves. The data given in Table 3 would suggest that such cut point differences were present in
the data. For example, in Phoenix, where the data would be most sensitive to the nature of the
2.5 //m cut, the R&P dichot gives higher fine particulate material concentrations than the FRM.
In Riverside, where the coarse particle mass averaged 30 //g/m3, compared to a PMc average of
55 //g/m3 in Phoenix, the dichot and FRM data differed by only about 4%. In Phoenix, the 2X
higher PMc concentrations resulted in a 20 to 30% difference. Why was the mass loss also not
present in Riverside. Is it possible that, while there may be some mass loss in the dichot at both
sites, the difference in the shape of the coarse particles size distribution at the two sites and
difference in the nature of the PMio cut point for the different samplers contribute significantly to
B-42
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the differences seen? The APS data may shed some light on this question. As a further example
of where the APS data could you useful, on page 16 the over measurement of PM2 5 by the dichot
is attributed to incursion of coarse particles into the fine mode for this sampler. Are the know
curves for the dichot and the nature of the APS data consistent with this assumption? What is the
difference in the particle size distribution near 10 //m for the Phoenix, as compared to the
Riverside data?
On page 18 of the text it is stated that R&P has additional data supporting loss of coarse
particles during transport of the collected material. Can we get the details of these results so we
can judge how applicable the studies conducted by R&P are to interpretation of the results of the
EPA study reported here?
The discussion on page 19 emphasizes the importance of knowing the cut point
characteristics of the various samplers. Here you attribute the difference in the R&P coarse
sampler to a cut point problem. The know cut point characteristics of the various samplers really
need to be detailed in the manuscript. The assumption is made that the differences between the
coarse TEOM and the gravimetric results is all due to this cut point difference. Are the APS size
distribution data consistent with 20 to 30% of the coarse mass being in the 9 to 10 //m range?
Can any of the difference be due to semi-volatile material lost from the heated TEOM monitor?
The chemical composition data may shed some light on this question. In this regard, the
difference between the gravimetric and TEOM measured masses largely disappeared in the May
to June Phoenix test. Do the APS data indicate no mass in the 9 to 10 //m range for these
studies? Or, alternatively, is it possible that the difference in chemical composition results in
less semi-volatile coarse particulate mass for the later Phoenix study?
Large biases, but with good regression slopes are seen for the Tisch Beta Gauge data. It
would be very helpful to see plots of the data on which the statistics given in Table 6 are based.
The assumption is made in the interpretation of the Tisch data that there was intrusion of coarse
particles into the sampler fine mode. Do the APS data support the probability of this occurring.
The amount of mass involved would suggest a significant tail of the coarse below 2.5 //m for this
to be the case. Is the steepness of the 2.5 cut for the Tisch know to be much poorer than for all
the other samplers to which it is compared?
E. Chemical Composition and Measured Mass:
Sufficient chemical composition data is being obtained in the various analyses to do a
reasonable job for estimating mass from the composition. This analysis may shed light on
whether adsorption artifacts, losses, etc. are affecting the results for any of the sampling systems.
Have any data been obtained which would provide artifact free nitrate and OM concentrations
for comparison with the measured mass? Are any results available which would shed light on
the relative importance of semi-volatile species in the coarse particles sampled? These effects
may be particularly important for the Gary and Riverside studies. I have a few additional
suggestions in this regard in the last section of my comments.
The chemical composition data may also shed light on results obtained at a site such as
Riverside. Substantial coarse particle nitrate could be present. This will be known when the
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composition data are available. At high RH values (such as will be present at night) the uptake
of water could give a very different response for the APS, as compared to gravimetric
measurements which presumable excludes any water uptake. Are RH data available? During
the night, periods of high relative humidity should be present in the Riverside samples.
Mention is made on page 13 of experiments with a USC prototype coarse particle
sampler in the studies at Phoenix. Details of the sampler and results obtained should be given.
F. APS Results and Chemical Composition:
It is not surprising that the APS results were not in good agreement with the PMio - PM2.5
calculation of PMc. A constant density (without justification for the selected value) is used for
the interpretation of all the APS data. It would be expected that the density of the coarse particles
in Gary which is dominated by wind blown dust from coal pile would be very different from
coarse particles dominated by suspended crustal material. For example, the ratio of APS to FRM
results given in Table 6 vary from 0.4 to 0.6. Are the results off because the assumed density is
not consistent with the measured chemical composition and are the different ratios, in part, due
to differences in composition at the very different sites. While it may be expected that the
composition (and hence density) of fine particles will be somewhat similar at each site as similar
sources contribute to these fine particles, the same will not be true of the coarse particles. In
fact, it appears that you have correctly chosen sites with rather different coarse particle
compositions to test the samplers. Now you need to use the data you have to improve the
interpretation of the APS data. In fact, it seems to me that it is only as you can bring the APS
and other data together that you will really understand the data well enough to say you
understand the results obtained. This will include both considerations of composition and
particle cut point characteristics.
G. A Final Comment on Other Studies Which Should be Conducted:
It would be good to understand what role semi-volatile material may be playing in results
obtained with the various samplers. Hopefully we will soon be to the point where we not only
worry about a defensible FRM, but also about measuring the actual concentration and
composition of particles in the atmosphere to assist in the interpretation of future health related
studies. This point will become even more important as we put in place semi-continuous
monitors to let us better understand diurnal variations and peak effects. The FDMS modification
to the TEOM monitor appears to correctly measure semi-volatile species based on recent results
reported by our group and others. It would be most informative to compare standard and FDMS
TEOM measurements in the semi-continuous monitor (and APS results, perhaps even both
heated and unheated) at the sites. If not at all sites, at least at Gary and, especially Riverside
where effects might be expected.
II. Use if a Performance Based Approach to Determine Data Quality Needs fir the PM-Coarse
(PMc) Standard.
I do not have the expertise to completely critique this report. Better input on this
document will come from others on the committee. But in general, I thought the approach was
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informative and that the Gray Zone information was potentially most helpful to those who must
make decisions.
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Mr. Eric Edgerton
Comments on Review Material for 7/22/04 Consultation by AAMM Subcommittee
Eric S. Edgerton
The charge to the AAMM was to review documents titled "Multi-site evaluations of candidate
methodologies for determining coarse particulate matter concentrations" and "Use of a
performance based approach to determine data quality needs for the PM-coarse standard" and to
respond specifically to three questions. Responses to the 3 questions (paraphrased) follow.
1. Strengths and weaknesses of each method tested, with respect to using it as a reference
method, a measurement principle, or as a method for approval of candidate methods?
Due to the dispersion across methods, it is too early to answer this question. The only
method I would discourage at this time as a "reference" is the APS, since it isn't, and doesn't
purport to be, a mass measurement method. All other methods showed very acceptable
precision, which suggests that other factors, such as inlet cutpoint(s), particle transmission
and other currently uncontrolled or unknown factors are responsible for the dispersion. At
this time, the only requirement I would place on a "reference" method is that it must have a
well-characterized inlet.
2. Strengths and weaknesses to meet multiple monitoring objectives (e.g., comparison to
NAAQS, public reporting, trends, chemical speciation)?
The continuous methods have a clear advantage over filter-based methods, when it comes to
public reporting, ease of use, cost, and temporal information content. They are inferior to
filter-based methods only in terms of chemical speciation.
3. For the PMc DQOs, is the process the Agency took to develop uncertainty estimates
appropriate, and are there factors that should/should not have been included?
The DQO tool is an interesting methodology for estimating overall uncertainty surrounding
PMfine and/or PMcoarse measurements, and the likelihood of Type I or Type II
measurement error. It appears that the DQO tool would be used in the design of a
compliance or public reporting network, but it is unclear how or whether it would be used
after this (unless to verify input assumptions). Other potential applications and users should
be clarified.
The Executive Summary states that "gray zones are most sensitive to population variability,
sampling frequency, measurement bias and completeness." This is true based on the input
assumptions applied later in the document. It would be worthwhile to perform a sensitivity
analysis to determine if this conclusion is generally applicable across the range of population
parameters (e.g., as shown in Table 1).
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Measurement bias of 10% may be a little optimistic for certain techniques. Inspection of
Table 2, shows that only the "reference" method (FRM) consistently performed to this level.
This raises two questions: first, what would the curves look like for method-specific bias
estimates of say 15-20%?; and 2) would the agency consider an FEM approach to adjust for
bias? Intra- and inter-method precision data suggest this might be a viable option (assuming
future field tests do not show convergence of results).
Finally, it is unclear how PMfine in the minor flow of the TEOM and Tisch units figures in
the DQO calculation. For the Tisch unit, it seems as though the calculation should be
similar to the dichot and both should be affected by the additive errors of two methods. For
the TEOM, it may be argued that PMfine in the minor flow is insignificant, because of the
particle concentrator inlet. This is certainly true for sites with high PMcoarse/PMfme, but
perhaps not for sites with low PMcoarse/PMfme.
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Mr. Henry (Dirk) Felton
Comments for AAMM meeting to discuss PM Coarse Methods
Dirk Felton July 15, 2004 Revised July 26, 2004
General Comments
States need both 24-Hr filter samples for Speciation and Continuous Data: Many State and
Local Air Quality Agencies are currently busy examining PM-2.5 data to evaluate NAAQS
compliance and to develop SIPs. States that have attainment problems for PM-2.5 will have to
enact control strategies that tend to work by reducing one or more species or components of PM-
2.5. These States will have to speciate their PM-2.5 filters as well as their PM-10 filters in order
to know the effectiveness of these control strategies. PMc by difference provides filters useful
for this purpose. These same States will need continuous PMc data to input into computer
models to demonstrate how their control strategies will effect ambient concentrations far into the
future.
Where and how much PMc sampling is needed?: It is apparent from AQS data in review
document #4 "An Overview of PMc" as well as data collected by the New York State
Department of Environmental Conservation (NYSDEC) that PMc mass is not necessarily related
to population density and is only problematic in a few Regions. For these reasons, basing the
requirement for PMc monitoring on population density such as was done with PM-2.5 would be
a mistake. PMc concentrations are higher and more uncertain in areas affected by industrial and
crustal sources. This places the majority of the need for PMc monitoring in smaller industrial
cities and in areas impacted by wind blown crustal materials.
How strict should a standard be?: It is clear that many of the epidemiology studies to date that
have focused on PMc data have used data suffering from poor bias and accuracy. I would prefer
to see the current emphasis on producing accurate PMc data so that new epidemiology studies
will be more robust in their determinations of causal effects. Until these new more accurate
studies are performed, there is little justification to enforce an overly protective new mass based
PMc standard. It is quite likely that for PMc, the epidemiology studies may find a particular
species of PMc that needs to be regulated as opposed to the mass of PMc.
Why stick with the current PMc size fraction?: The proposed PMc size fraction of PM-2.5
through PM-10 dovetails with the NAMS, NCORE, STN PM-2.5 monitoring programs and the
NAMS and TTN PM-10 monitoring programs. The future of EPA supported monitoring most
likely is going to be guided by the NCORE program currently under development. One of the
tenants of NCORE is the movement away from single pollutant networks to "coordinated, highly
leveraged multi-pollutant networks". The data from a future PMc monitoring program will be
much more valuable if it can be used in a consistent manner with these other existing monitoring
programs. It would be reasonable to switch monitoring size fractions in the future only if there
was overwhelming epidemiological evidence in support of a different size fraction.
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FRM, FEM, Does it matter? Do we need both?: As mentioned earlier, many Regions will
need the ability to collect both filter PMc for speciation as well as continuous PMc for modeling.
The best way to provide for all data needs is to approve a monitoring principle as the FRM for
PMc. It is preferably that this principle be based on a technology that is not linked to a specific
vendor so that future equipment development is not hampered. It is also preferable that the
requirements necessary to obtain FEM status be such that the EPA will be able to support the use
of one or more of the automated PMc monitoring technologies. Due to procedural rules in some
States, it would be difficult for some Agencies to specify monitoring equipment that is
designated as a FRM or FEM.
Difference Method
Question 1: The difference method is the best choice for providing the basis for approval of
candidate reference and equivalent methods. It is the only method that uses a "fundamental
measurement principle " to determine PMc. Since this method is weight based, it is
consistent with the PM-2.5FRMthe PM-10 FRM and it works reasonably well in all
geographic areas. The use of virtual impactors or optical/beta attenuation techniques by the
other reviewed methods would include particulate properties in the PMc measurement that
are not uniform from one geographic area to another.
Question 2: The accuracy and consistency of the difference method makes the data robust
enough to compare to potential standards, use in health studies, provide filters which can be
used for species analysis and for 24-Hr based modeling. Monitoring agencies are familiar
with the field samplers and analysis issues and may in fact have extra samplers if the
requirements for PM-2.5 FRM sampling are reduced. PMc network implementation costs
would be reduced assuming that PMc sites were collocated with PM-2.5 FRM monitoring
sites.
The disadvantages of the difference method are the limitations of a 24-Hr sample period,
the delay in obtaining data, the additional uncertainty due to the operation of two
samplers and the costs associated with field operations and remote lab preparation and
analysis. Diurnal data is not available from this method though in areas where the PM-
2.5/PMc ratio is fairly stable, diurnal information can be inferred from the hourly PM-2.5
network.
Dicot Filter Sampler
Question 1: The principle disadvantage of the filter based Dicot sampler's use as a
reference sampler is the deposition of a portion of the fine fraction on the coarse filter.
This can be accounted for mathematically but the correction may not work well in all
regions and it makes the method one step removed from a "fundamental measurement
principle".
Question 2: This method has the potential to be accurate enough for comparison with
proposed standards and can provide filters for speciation analysis.
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The disadvantages of the Dicot filter sampling method are similar to those of the difference
method: the limitations of a 24-Hr sample period, the delay in obtaining data and the costs
associated with field operations and remote lab preparation and analysis. Additionally,
measurement problems associated with capture of the coarse fraction may make this
technique less accurate in areas of the country with larger PMc/PM-2.5 ratios.
Dicot Beta Gauge
Question 1: The Dicot Beta Gauge has not demonstrated consistent results from one
geographic area to another. This shortcoming demonstrated by sub par comparison to the
filter PM-2.5 andPM-10 data makes this a poor choice as a reference method or technique.
Additionally, the lack of volumetric flow control will affect the instrument's performance in
cold or high altitude areas.
Question 2: The only significant advantage to this method would be the availability of short
term/hourly PMc data and the reduced labor costs associated with an automated method. It
is likely that this method could produce PMc data useful for public health information but it
would have to be periodically evaluated and adjusted against another method such as the
filter difference method.
Disadvantages of the Dicot Beta gauge stem from fine particle intrusion onto the coarse
filter, the unquantifiable effect of heating the sample stream and the extra error associated
with the use of two Beta sources and detectors. One question that arises from the evaluation
report is how exactly is the "span calibration performed by the user " related to the aerosol
mass collected on the filter.
Coarse TEOM Method
Question 1: This technique has the greatest potential of the automated methods to be a
reference or equivalent method. The instrument uses a single weight based measurement
system and therefore does not calculate particle mass from a regional or aerosol dependant
multiplier.
Question 2: The advantage of this design is the high flow rate virtual impactor which may in
fact eliminate the issue of fine particle intrusion into the coarse measurement. This design
element along with the question of the actual inlet cutpoint will require further evaluation.
This method could provide hourly data useful for public reporting, trends and modeling
analysis.
The principle disadvantages of this method include the heated sample, the lack of a fine
particle measurement, and the inconsistent comparisons with the FRM difference data. It is
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apparent that during the evaluation in Gary IN. on days like 4, 12, 25-27 the TEOMmethod
drastically under predicts the data from the FRM difference method. It would be important
to know if these days were associated with high PM-10/PM-2.5 ratios or high concentrations
of volatile species.
APS Instrument
Question 1: The APS method would be a poor choice for a reference or equivalent method
because it uses an assigned particle density to calculate a mass per unit volume. Average
particle densities could be provided by regional evaluations but this can not effectively
capture the short term changes in the density of aerosols particularly in urban and near
source monitoring environments.
Question 2: The advantage of the APS method is its real-time data availability.
The disadvantages of the system are the lack of particle mass information, the inability to
detect particles below 0.7 micrometers and the inconsistent comparison with the FRM
difference method. The large differences between the collocated APS units in Gary IN. on
sample days 10, 11, and 12 should be investigated.
Question 3: For the PMc DQOs, is the process the Agency took to develop the estimates of
uncertainty appropriate? Are there factors the Agency has included that should not be
considered or are there other inputs that should be included?
The process used to produce the PM Coarse DQO may or may not be appropriate but I worry
about the unstated Type 3 error. The DQO tool was developed backwards, that is by examining
the existing data in AQS. To actually know what the uncertainty is in any method you would
have to study it from the beginning. You have to calculate the error associated with each step in
a method from the preparation of the filter through the final data manipulation and presentation
and then look at the propagation of those errors. For the PMc data used in this document, the
potential errors are compounded by the use of vastly different and inconsistent sampling methods
for both PM-2.5 and PM-10. Type 3 error is the problems that result from using inconsistent
data.
Inconsistent data should not be used comparatively. For example, low volume FRM PM-2.5
data includes a proportion of volatile material in the sample weight. High volume PM-10
samplers are not designed to capture this material and the resulting subtraction often creates a
negative value for PMc. Similar inconsistencies result when using TEOM PM-10 data which is
collected at 50 Deg C and is then compared to a different PM-2.5 collection method.
The DQO tool must be evaluated by using data that is of the same type and quality as the data
that will be used in the future PMc network. This is the only way of knowing for sure that the
resulting DQO is appropriate for the measurement. It would be advisable to initiate the PMc
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network with a start up phase that could provide data for the purpose of generating accurate
DQOs.
Results of PMc measurements in NY by difference Jan. 2002 - May 2004
Manhattan
Average
Median
Std Dev
25th %
75th %
PM10ug/m3 PM 2.5 ug/m3
25.40
23.92
11.79
16.38
31.50
15.47
13.08
8.71
9.13
19.63
PMc PM2.5/PM-10
9.92 0.60
9.08 0.61
4.95 0.13
6.38 0.52
13.00 0.68
PM-10 vs PM-2.5 Manhattan, NYC
Collocated R&P 2025 Instruments
y = 0.68x- 1.90
R2 = 0.86
y = 0.62x
0 10 20 30 40 50 60 70 80 90
PM-10 ug/m3
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Niagara Falls PM10ug/m3 PM 2.5 ug/m3
Average
Median
Std Dev
25th %
75th %
20.25
18.13
9.93
13.63
24.67
11.42
9.71
6.90
6.44
14.25
PMc
8.84
7.54
5.95
4.88
11.21
PM2.5/PM-10
0.57
0.57
0.17
0.43
0.70
PM-10 vs PM-2.5 Niagara Falls, NY
Collocated R&P 2025 Instruments
y = 0.56x + 0.04
R2 = 0.65
y = 0.56x
= 0.65 I
10
20
30
PM-10 ug/m3
40
50
60
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Dr. Rudolf Husar
Comments by Rudolf Husar, CAPITA, Washington University
Multi-site evaluations of candidate methodologies for determining coarse
paniculate matter (PMc) concentration
EPA has conducted a set of field comparisons for candidate PMc concentration measuring
instruments. The project well conceived and executed and the report is well prepared. The
Abstract could be more informative. Beyond listing the instruments and monitoring locations, it
would be helpful to contain the key findings.
The field comparisons at Gary, IN (Mar-Apr, 2003); Phoenix, AZ (May-June, 2003); Riverside,
CA (Jul-Aug, 2003) and Phoenix, AZ (Jan, 2004) have shown a remarkable precision for PMc
concentration measurements, which indicated that technologies currently exist for reliable PMc
measurements. However, the intercomparison field study has also revealed that substantial
systematic deviations exist between some of the instruments, at some of the locations. The
comments below pertain to these systematic deviations of PMc concentration measurements.
The potential causes of systematic instrumental deviations can be related to either instrumental
malfunction or to fundamental differences in the sampling/detection under different aerosol
conditions. EPA, RTI and the instrument manufacturers have sufficiently addressed the
instrumental malfunction issues. Therefore the comments below pertain to additional data
analyses and characterization of the response characteristics of different instruments.
Additional analysis of existing TSI-APS size distribution data
During the field study the APS size distribution data were integrated to yield PMc concentrations
comparable to the other instruments. It would seem beneficial to evaluate the shape of the APS-
derived size distributions for each integrated sample periods and analyzing the observed
instrumental PMc deviations in the context of the varying size distributions. For instance it
could reveal that the magnitude of the deviations are related to the mass above 10 um.
Furthermore, the APS data could also confirm or contradict the hypothesis of fine-coarse mass
cross-contamination at the 2.5 um size cut.
During the July 22, 2004 CASAC meeting the investigators have indicated the willingness to
"make the APS data available to others", but they did not present a plan for the re-analysis of the
APS size distribution data. It is the opinion of this reviewer that virtually all of these instruments
will be extremely sensitive to the shape of the size distribution at the upper cut-of 10 um, which
also influences the absolute PMc mass determination. This is particularly significant since the
upper end of the PMc size spectra is highly variable due to the short residence time of 'giant'
particles above 10 um.
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Additional instrumental inter-comparisons under controlled conditions
Field studies, as conducted during the first intercomparison study, are appropriate for initial
evaluation of instrumental performances. However, field studies by themselves may not provide
defendable explanations for the causes of the observed deviation. On the other hand, fully
controlled laboratory comparisons, e.g. wind tunnel studies of instrument performance can be
expensive and hard to control. A third alternative suggested herein is the 'big room'
intercomparison, similar to the 'big bag' intercomparisons used for the study of short-lived
ultrafme particles. In this approach, aerosols are generated, e.g. dust (by mechanical dispersion)
or sea salt (by atomization) or any other substance is generated within a large pressurized room,
such as a garage or hangar. The instruments could be mounted in the same configuration as
during the field studies. Within the room, the aerosol would be circulated by large fans that
assure the delivery of the same aerosol to all instruments. Since coarse and giant particles have
relatively short life-time, during any given experiment the size distribution would change due to
sedimentation of the giant fraction. Throughout the dynamic experiment the aerosol size
distribution would be continually measured with continuous particle counters, such as ASP. Such
an approach could yield exposure to partially controlled but fully characterized exposure to the
instruments under a variety of aerosol conditions.
Sampling representativeness of PMc monitors
The design of PMc monitoring networks has to incorporate the issue of sampling
representativeness. Sedimentation of giant particles above 10 um limits their residence time to
hours, minutes, or less. As a consequence of short atmospheric residence time, coarse and giant
particles exhibit much stronger spatial-temporal variability then particles in the 0.1-5.0 um size
range. Thus, placing the monitors at the appropriate locations is crucial for obtaining the
relevant PMc data. This location problem, of course, is hampered by the classical network
layout paradox: the monitors can not be located optimally since the true emission/concentration
fields are not known. If on the other hand, the concentration fields were known, there would be
no need to perform extensive monitoring.
During the July 22, 2004 meeting several members have suggested the design and
implementation of 'saturation monitoring' studies for purposes of characterizing the fine-scale
spatial-temporal pattern of PMc at characteristic locations/seasons. It is the opinion of this
reviewer that such studies would be necessary before the large-scale deployment of the new PMc
sampling network. However, such intensive monitoring pilot studies would need to be
augmented with significant analysis of the collected monitoring data. One particular crucial
analysis would need to relate the temporal variability of PMc at specific monitoring sites to the
spatial variability among the sites. Establishing such a space-time variability relationship from
the saturation monitoring would allow the interpretation of continuous but sparse monitoring
data to other locations. For instance, any continuous monitoring site that shows a relatively
smooth background concentration and concentration spikes superimposed on this background
provide clear evidence that a local PMc source is impacting that site. The interpretation of such
monitoring data requires local wind data as well as a suitable mass conserving dispersion model
for the evaluation of space-time relationship. The numerical analysis techniques for space-time
analysis could be tested on the existing continuous PM25 monitoring data.
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Estimating Parameters for the PMc DQO Tool
The PMc DQO estimation tool is a useful addition to the analytical toolbox of Regional, State
and Local agencies. The report is well prepared. The comments below pertain to the formulation
and testing of the model used in the PMc DQO tool.
Conduct model validation
The DQO tool is based on a statistical model that is formulated to describe the aerosol
distribution pattern for estimating possible non-compliance. From science perspective, the
statistical model need to fit a multi-dimensional PMc data space, including spatial dimensions
(X, Y), temporal dimension (T) and particle size (D). The current DEQ model includes
parameters for sinusoidal seasonality, phase shift between PM2 and PMc seasonal peaks, day-to-
day variability of PM25 and PMc, the mean PM25/PMc ratio, correlation between PM25 and
PMc.
In developing this empirical model, the DQO tool team has undoubtedly tested the validity of the
above model for different aerosol conditions. It is therefore peculiar that the report is void of any
information on model performance for 'characteristic' conditions. Was the model-data fit that
poor? Where does it work where it does not? How can one use and trust a model (even for
estimation purposes) if it has not been validated?
Alternative model formulations
The sinusoidally seasonal model with random daily perturbation and fixed PM25/PMc ratio is
just one possible statistical model of the aerosol system. Alternative formulations, such as non-
sinusoidal seasonality; regional baseline + local (additive not multiplicative) perturbation;
independent PM25/PMc time series, would help establishing the robustness of the model(s).
Spatial aerosol parameters
The current statistical model simulates the temporal aspects of the aerosol variations. What about
the spatial aspects? The addition of spatial parameters, such as spatial scale (e.g. derived from
spatial cross correlation) for regional and local PMc would help estimating the spatial
representativeness of different sites. Some of the spatial parameters could also be derived from
the temporal monitoring data as discussed under the above heading: Sampling representativeness
of PMc monitors.
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Dr. Kazuhiko Ito
Comment on PMc monitoring documents. (Revised 8/01/04; original comment was submitted on
7/21/04).
Kazuhiko Ito, NYU.
(I) Multi-Site Evaluation of Candidate Methodologies for Determining Coarse P articulate
Matter (PMc) Concentrations
Issues about the difference method:
In my original written comments, I expressed concerns regarding the use of the difference
method (PMc = PMio - PM^.s), in particular the possibility of negative values. However, during
the 7/22/04 meeting, at least two people, including one speaker, mentioned that this was not an
issue with the current FRM samplers. The negative values apparently can be a problem when the
PMc is computed based on the values from a "high-vol" PMio sampler with a low-vol PM2 5
sampler, but this is apparently not the case with the current FRM samplers (my original concern
came from the data distribution of the computed PMc in Appendix 4, which included such high-
vol PMio samplers). Though I have not seen a database to support this point, my concern is
dissipated after hearing from these experienced researchers who are close to these data. With
this negative value problem being no longer an issue, and with the very high correlation of the
data from co-located samplers in these data (R2 is mostly > 0.95), my concern about PMc is now
shifted to the spatial/temporal "error", not the instrumental or analytical measurement error. I'll
discuss this in the second part of my comments ("The use of a performance based...").
The great precision:
Since most of the discrepancies (mainly constant over- or under-estimation) between the
samplers could be explained away and therefore some adjustments could fix them (or at least I
got that impression), the great precision and the high correlation in most of these tests stood out.
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I was impressed by the data, but I could also imagine that considerable care and efforts must
have into these experiments, downside of which may be that, under more routine conditions, the
same extent
Comments about specific candidate samplers:
I imagine that each of the candidate samplers must have an experimentally obtained
collection efficiency curve. Showing such curves (and combined with estimated size
distributions of PM in each location) would have been helpful in understanding some of the
discrepancies across the samplers. Some of the reasoning for the differences between samplers
remain unclear.
R& P sequential dichotomous sampler:
Unlike the difference method, the dichotomous sampler should not result in negative PMc mass.
Therefore, the sequential dichotomous sampler seems more desirable than the difference method
in estimating two mass fractions. It would be interesting to compare the dichot sampler vs. the
difference method from FRM samplers in locations where PM2.5 dominates PMio. The results
from these high PMc locations look promising in terms of correlation with the FRM (R2 > 0.97).
The PMc under-estimation problem (due to particle loss) seems to be resolved.
R& P Coarse TEOM sampler:
Considering the major advantage of being able to provide real-time measurements, the high
correlations between these TEOM samplers and the FRM samplers seem promising. If the
consistent under-estimation of the TEOM sampler is in fact due to its smaller cut-off size (~
9|im), then the unit may be re-designed to fix this problem. The large intercept (12.8 |ig/m3) for
the Phoenix 2003 data is somewhat worrisome, and the "better agreement" (apparently pointing
to the mean TEOM/FRM ratio of 1.05) may be misleading if the ratio is systematically high at
the lower PMc range and low at the higher PMc range. A large intercept was not observed in the
Phoenix 2004 data, but PM2.5/PMi0 ratios were also higher (0.24 for run 1-11 and 0.53 for run
12-15) during that period than the 2003 study period (0.18). As the time-series plot of the
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TEOM and FRM from Gary, IN also suggests, the absolute difference appears larger at the
higher PMc range.
Dichotomous beta gauge sampler:
A real time dichotomous sampler's strength is its potential ability to characterize diurnal patterns
of fine and coarse particles, which may be also useful in separating out their corresponding
source types. It is interesting that this sampler measures PMc more accurately than PM2 5.
While the report speculates that the over-estimation of PM2.5 is due to the inadvertent intrusion
of coarse particles into the sampler's fine mode channel, it also mentions that PMio is also over-
estimated (and PMc is not under-estimated in 3 out of 4 locations). The source of PM2.5 over-
estimation needs to be clarified.
Aerodynamic Particle Sizer:
The obvious potential strength of this sampler is its ability to obtain size distribution of particles
(larger than > 0.7 jim) in real time. A potentially problematic feature of this sampler is that it
assumes a constant density for the coarse particles to obtain mass. It is not clear if this implies
that site-specific determination (calibration) of coarse particle density is necessary. The extent of
under-estimation of PMc in these data is rather large (~ a factor of two).
(2) "Use of a Performance Based Approach to Determine Data Quality Needs for the PM-
Coarse (PMc) Standard".
The general rationale for the use of DQO process seems reasonable. It may be helpful if
the reviewers actually get to try out this software with some scenarios.
There is one type of uncertainty that the document does not specifically address: spatial
and temporal correlation of PMc within the scale of a city. From a viewpoint of conducting a
short-term epidemiological study, the location of a PM monitor can be very important because
the measurements at the monitor is supposed to represent the population exposure of that city. If
the temporal correlations of PMc across locations within a city are low, then the associations
between the health outcome and the PMc measured at such locations are likely biased toward
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null (the difference in the absolute mean levels, not temporal correlation, affects long-term
epidemiological studies). If the results from these epidemiological studies influence the setting
of NAAQS, then the location-related uncertainty within the city should also be considered in the
process of the DQOs.
The extent of this sampling location-related uncertainty for PMc, I imagine, would be far
larger than the extent of instrumental bias and precision of co-located samplers reported in the
multi-site field evaluation study. For example, Wilson and Suh (J. Air & Waste Manage. Assoc.
1997; 47: 1238-1249) examined site-to-site correlation of PMio, PM2.5, and PMio-2.5 in
Philadelphia and St. Louis, and found that site-to-site correlation coefficients for PM2.5 were
high (r -0.9), but low for PMio-2.5 (r ~ 0.4: or R2 of 0.16!), indicating that coarse particles have
much larger errors in representing community-wide exposures. Compare this extent of
correlation with those reported for the co-located samplers in the multi-site field evaluation study
(R2s were mostly above 0.95). The uncertainty related to the location of PMc monitor can
overwhelm the uncertainties related to instrumental measurement error. Furthermore, the
location related uncertainty is also expected to vary regionally. Attachment 4 ("General
characterization of PMc as found in the U.S....") does not provide this information (site-to-site
correlation within a city), but this can be computed from the same database for the cities where
multiple monitors exist. At the 7/22/04 meeting, someone mentioned that the database in
Attachment 4 contains both the "high-vol" and "low-vol" PMio samplers, so that the computed
PMc in this database may have significant number of negative values (in fact, this is apparently
the case as the 25th percentile of the Box plots are near zero in some areas in winter). Despite
this limitation, I think it would be useful to characterize the spatial variability of PMc in the
existing database unless there is a good reason to believe that the negative value problem may be
regional and possibly blur the true spatial pattern of PMc.
Under the sources of uncertainty listed (the method, the NAAQS, the sample population,
or the measurement uncertainty), the sampling location-related uncertainty of PMc would be
categorized under the "Uncertainty Related to Sample Population". However, the items listed
here (seasonality ratio, population variability, auto-correlation, PMc/PM2.s ratio, and PMc/PM2.s
correlation) are only indirectly related to the location related uncertainty. For example, two sites
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can have an identical PMc CV (population variability coefficient of variation) and at the same
time still have a very low temporal correlation with each other. This information needs to be
somehow incorporated into the DQO process.
As I briefly mentioned at the 7/22/04 meeting, if the DQO model is to accommodate the
input from epidemiological studies, then it will need to take into consideration two types of
spatial/temporal variability: (1) temporal correlation across sites; and, (2) absolute difference in
the long-term means across sites. Basically, the former is important for the short-term
epidemiological studies (i.e., longitudinal and time-series studies) whereas the latter affects the
long-term studies (i.e., cohort studies) in which the comparison is more cross-sectional.
However, there are some further complications in defining the "error" or spatial/temporal
variations. For example, in a simplest scenario for short-term studies, the PMc measurements
made at each monitoring site within a city may be assumed to have random error that perturbs
"true" city wide temporal fluctuations of PMc that affect the whole population. In such a case,
the random error would attenuate (bias toward null effect) the true associations between PMc and
health outcomes, but knowing the extent of the error (from the site-to-site temporal correlation)
may allow "correction" of such attenuations. A more complicated scenario is that the PMc levels
measured across multiple sites may not temporally correlated at all but nevertheless represent
true exposures of the local population surrounding the monitor in each area. In such a case,
monitoring at multiple locations may be necessary. A reality may be somewhere in between
these scenarios. Likewise, for the purpose of long-term epidemiological studies, the variations in
the long-term (e.g., annual) mean levels across monitors within a city may reflect both the true
difference in exposure as well as some "error" in representing the population exposure for that
city. The DQO model will need to somehow address these complications, but I think the first
thing that has to happen is to estimate the spatial/temporal variation of PMc using the existing
database. If the PMc based on the difference method for the older (high-vol) PMio data is
problematic, then evaluating the spatial/temporal variation of PMio and PM2.5 separately may
still provide useful information.
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Dr. Donna Kenski
Comments on PM Coarse Methods Evaluation
Submitted to CASAC
by Donna Kenski
Lake Michigan Air Directors Consortium
2250 E. Devon Ave.
Des Plaines, IL 60018
July 30, 2004
In general, the documentation provided for review was quite thorough and the care with which
the evaluation study was designed was evident. Most of these comments are minor, expressing a
need for additional clarification or supporting material. It is tempting to pose some questions to
EPA about the rationale for a PMc standard and the evidence for health effects associated with
this particular size fraction of PM, but that seems outside the scope of this subcommittee's
charge. I trust that will be covered in detail in the coming criteria document.
It is obvious that no single method can meet all the goals for a PMc monitor that were noted in
the Scheffe memo of June 18. Thus a future PMc network will likely consist of a mix of
instruments to meet these various goals, much like the current PM2.5 network. The committee's
charge to determine a suitable method for reference or a measurement principle is constrained by
the definition of PMc as the difference between PM10 and PM2.5; since PM2.5 and PM10
already have reference methods, any PMc method logically seems to need to be linked to these
existing FRMs.
Simply by looking at how this current study was set up, it seems almost as if EPA has already
decided, a priori, that the difference method based on a PM2.5 FRM with and without the WINS
impactor is the reference method of choice for PMc. Based on the data presented in Attachment
2, the difference method using these two instruments is probably the best understood, least
biased, most consistent method available at the moment. However, this method is not without
problems, and I have a strong preference for allowing EPA and the states as much flexibility as
possible in implementing any monitoring, to avoid being locked into a single monitor design for
the foreseeable future. Perhaps this flexibility is best built into the DQO process and into
designating FEMs rather than FRMs. Nevertheless, the information presented in this study is not
sufficient to make a definitive case for any of the methods at this time: additional monitoring
methods should be evaluated (DRUM samplers), the manufacturers involved in this study have
planned modifications that should be evaluated prior to any decision, and the data from this study
should be mined comprehensively (i.e., using speciation analysis of all of the remaining samples,
measured size distribution data, and inlet characteristics to more comprehensively examine
sampler differences). As this study shows, each of the instruments had performance issues that
need to be more completely understood before being considered as a reference method or
measurement principle. These issues are summarized in the table below.
In evaluating the strengths and weaknesses of the candidate methods, I would like to have seen
the raw data for each site in scatterplots, and consistent presentation of data from site to site. The
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data presented in tables are useful summaries, but it's much easier to compare results graphically
than in tabular format. For example, for the dichots, show the same set of scatterplots for each
city: PM2.5dichot vs. PM2.5frm, PMlOdkhot vs. PMlOfrm, and PMcdichot vs. PMcfrm, with a 1:1 line
and a regression line on each plot. Subtlety and nuances in the data are lost when reduced to a
single regression equation. There are two Tables labeled Table 6, and neither of them contains
CV values; these should be added, since they are discussed in the text. As long as these tables
are trying to summarize all the relevant information, the average measured mass for each species
should be included as well, since it is an important factor to consider when examining relative
performance of the various methods.
One drawback to the study was that, by virtue of being a research endeavor, it did not test the
instruments under true "real world" conditions - for example, using local site operators instead
of research scientists, using automated filter changing instead of running instruments manually
(the sequential dichot), and weighing filters daily. The result is a more complete data set for
evaluation (a good thing) but a biased view of the true field performance of the instruments.
Comments on the actual ease of operation would have been helpful, although not necessary for
evaluating performance.
Several issues were not addressed that really need to be considered. Chief among these is the
presence of volatile material in the PM and the impact of that on these various measurements.
Several methods employ a heated air stream; the effect of this on PMc needs to be documented.
Similarly, the effect of the various inlets and their respective cut points should be examined.
Allusions are made to inlet effects (re coarse particle intrusion onto Pm2.5, for example), but
supporting data are not included.
Although the APS collected size distribution data, none of it was presented in the review
materials. It would be informative to examine PMc size distribution characteristics.
Since there were several continuous methods, it would have been nice to see a comparison of
diurnal data to evaluate how consistent these continuous methods are to each other, without
regard to the FRM. This kind of comparison would be helpful in examining the various
instruments' responses to changes in relative humidity and might be useful in evaluating their
differences from the FRM as well.
Some additional background data that would be helpful in evaluating the methods include a
description of spatial variability of PMc. Attachment 4 was a good start, but a quantitative
estimate of the scale of PMc variability across urban areas, across states, and across regions
would help. Similarly, since most existing PMc data (outside of this study) has been developed
from older, high-volume PM10 measurements, it would be helpful to describe the comparabiltiy
of the high-vol PM10 measurements to the low-volume measurements. A summary of PMc
composition, or a conceptual model of PMc, would also add perspective.
DQO Tool: The process to date in developing the PMc DQO is appropriate, although the
documentation is not the easiest report to read through. A question about Eqs. 1 and 2 in
Attachment 3, Appendix A: the model assumes that PM2.5 has a single distinct peak in each
year, but in the Midwest it tends to have a bimodal behavior with two peaks (summer and
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winter). What is the implication of this? Doesn't this mean the parameters (specifically for
seasonality) incorporated in the model won't accurately represent data in a large part of the
country?
Sampler
Advantages
Disadvantages
Comment
FRMc
Hundreds of similar
samplers are already in
use; field tested.
Filter artifacts
reasonably well
understood.
24hr integrated measurement is less
useful for public information goal,
doesn't address diurnal variation,
filter collection and weighing is
resource-intensive.
Seems like the only really
viable candidate for NAAQS,
but must be supplemented by
hourly measurements to
supply the public with real-time
information (if deemed
necessary)
R&P
sequential
dichot
One instrument vs.
two.
Consistently overmeasured PM2.5,
undermeasured PMc, due to
problems with sequential operation,
but mfr. is addressing problem.
24hr integrated measurement is less
useful for public information goal,
doesn't address diurnal variation,
filter collection and weighing is
resource-intensive.
What fraction of the PM2.5
mass really ends up on the PMc
filter? Should have some
analysis of actual vs.
theoretical.
Tisch
dichotomous
beta gauge
Semicontinuous
method gives real-time
data for public, usually
cheaper/easier to run
1. Mass flow control will introduce
errors when conditions change from
calibration conditions. Such changes
are inevitable in parts of the country
with wide daily temperature swings.
How big an effect?
2. Consistent overestimate of PM2.5
andPMlO.;
Is volumetric flow control a
possibility?
Should address possible effects
due to heating air stream to
25C.
Should show how well
theoretical adjustment for
PM2.5 mass contained within
PMc mass fits with actual.
CV values are missing from
table and report.
R&P Coarse
TEOM
Semicontinuous
method gives real-time
data for public, usually
cheaper/easier to run
Inconsistent: underestimates PMc in
3 of 4 trials, overestimates in 1, not
clear why.
Poor performance in rain.
Should address possible effects
due to heating air stream to
50C.
Why are the Phoenix 2003
results so different? Is this a
nitrate volatilization problem?
TSIAPS
Semicontinuous
method gives real-time
data for public, usually
cheaper/easier to run
Particle density (which is presumed
to be 2 g/cm3) will vary with site and
particle composition. Need
documentation to support this value
and description of variability.
Underestimate PMc. Correlation
with FRM great in Phoenix, but poor
in Gary and Californiawhy?
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Dr. Thomas Lumley
Comments on material for July 22 meeting.
Thomas Lumley
Associate Professor of Biostatistics
University of Washington.
0. One initial issue is whether the definition of PMc is on the table for discussion at this
point. The cutpoint at 10 microns is maximally difficult for precise and accurate
measurement. If there are biological grounds for moving the cutpoint it might make it easier
for different measurement methods and instruments to agree on PMc concentrations.
1. There are high correlations but relatively poor agreement in values between the
measurement techniques compared in the EPA study. The ratios between the methods vary
from site to site, implying that the instruments are not all measuring the same thing. I do not
have the right expertise to comment on which of these things is the best for defining as PMc,
but it seems important to understand the reasons for differences between the measurements
- Do the size distributions from the APS suggest that the differences are due to
different cutpoint characteristics at lOmicrons? Size distributions would also be of
interest in Phoenix for the times when 'intrusion of coarse particles into the fine
mode' was postulated for the R&P dichot sampler.
- Are there chemical composition or other data that would illuminate the likely impact
of semivolatiles on the differences?
A more difficult question to answer is whether there are day-to-day or seasonal
variations in the relationship between the measurement techniques (for example, in
Phoenix, is the relationship different on the windier days?). The reason this is
important is that a stable relationship would allow site-specific calibration of one
method to another.
- The variation between sites in the ratio of APS to FRM measurements suggests that a
single global density value may be inadequate. This in turn raises the question of
whether a single density value is adequate across size categories within a site.
Scatterplots comparing results of different measurement methods (like Figure 10 of
the evaluation document) would be very helpful for other comparisons, especially if
they included all the sites rather than just one.
Clearly the continuous-time methods have the ability to describe regular diurnal variation and
brief episodes of high concentration. The APS instrument can in addition describe size
distributions. Time-resolved (and size-resolved) information may be helpful in reporting and
understanding brief episodes of elevated PM. On the other hand the continuous-time
measurements do not collect PM in a form suitable for subsequent chemical speciation.
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2. The performance curve tools described in Attachment 3 are a very promising development.
When relating observed data to the true concentrations specified by the NAAQS there is
unavoidable uncertainty due to the variability of PM concentrations and the bias and limited
precision of measurement techniques. It is important to be able to relate the uncertainty in
concentration to the probability of making an incorrect decision, and to decide whether the cost
of reducing this probability is warranted.
The statistical model and simulations used to develop the curves are thoroughly developed and
generally well-described. Some specific points:
I am uncertain as to whether the autocorrelation parameter described is the
autocorrelation of the log-Normal errors or of the Normal errors that are used to
generate them. Either would be reasonable, but a user would need to know which was
intended, as they will be quite different.
Although not estimable from the AQS database, the correlation in measurement errors
between PM2.5 and PMc may affect the results, and may vary between measurement
techniques. For example, computing PMc as PM10-PM2.5 introduces a negative
correlation between errors in PMc and those in PM2.5. This correlation is likely only
to be important when considering the two size fractions jointly (e.g., what is the
probability of a location being incorrectly declared in compliance on both size
fractions?)
3. One standard but perhaps undesirable feature of the current calculations is that they assume
that the action limit, rather than the true concentration, is fixed. Considering a hypothetical PMc
standard of 50mcg/m3, for example, it would seem statistically natural to examine how different
action limits perform in ensuring a true concentration below 50mcg/m3. The description in
Attachment 3 reverses this, assuming that the action limit is prespecified and examining how the
true concentration would in fact be controlled.
In addition to the fact that the true concentration is presumably the quantity relevant to public
health, the interaction between cost and precision is easier to handle when the true concentration
is used as the target. Consider two locations, one with very low PMc levels, and one with
moderately high levels, just below the permitted threshold. In the first location even relatively
imprecise measurements will be sufficient to show that the PMc levels are in compliance. In the
second location much more precision is needed. If the monitoring specifications were designed
to ensure, say, a 95% power for detecting a true level of 55 mcg/m3, the first location could use
less precise measurements and a correspondingly lower action limit than the second location. In
both cases the public health would be protected, but at lower cost than if both locations used the
more precise monitoring.
The calculations for varying the action limit are almost the same as those for varying the true
concentration, and similar curves (but sloping down rather than up) would be produced.
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Dr. Peter McMurry
Peter H. McMurry
July 23, 2004
RE: CASAC Ambient Air Monitoring & Methods (AAMM) Subcommittee Meeting: Comments
on Materials provided for review
Preliminary Comments:
I am impressed with the amount of work and thought that was done prior to this meeting. I am
also delighted with the responsiveness of EPA to recommendations previously made by
CASAC's Technical Subcommittee on Particle Monitoring. It is clear that input from this
committee is having an impact on EPA's decision making process, and this is gratifying.
I am impressed (and surprised) by the excellent precision of measurements obtained with using
identical samplers operating in parallel. My compliments to the manufacturers and the field
measurement team on a job well done!
I agree with the accommodating tone of the report. For example, "Effective engineering
solutions to this noted problem could potentially result in close agreement of the R&P dichot
with the filter based FRM for all three metrics." (Summary, p. 23) Manufacturers should be
given an opportunity to refine instrument performance.
The DQO tool is intriguing. However, I will restrict my comments to measurements methods,
since this is my primary area of expertise.
Responses to Questions:
1 & 2. What are the strengths and weaknesses of each method tested in the ORD study
Method
Strengths
Weaknesses
PMc FRM=PM10-PM2.5
1. Uses established
monitoring equipment
2. Filters can be analyzed
for particle composition
1. 24 hour time resolution
2. Expensive, manual filter
analysis
3. Not useful for real-time
AQI reporting
4. Involves collection of
particles on a filter,
rather than direct
measurements of gas-
borne particles.
5. Relies on FRM, which is
known to be inaccurate
in some locations
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Dichot
1. Single sampler for both
fine and coarse
mass/composition
2. Less influenced by
fine/coarse missing than
PM10-PM2.5
3. Filters can be analyzed
for particle composition
1. 24 hour time resolution
2. Expensive, manual filter
analysis
3. Not useful for real-time
AQI reporting
4. Involves collection of
particles on a filter,
rather than direct
measurements of gas-
borne particles.
TEOM
1. Fast time response-better
information on temporal
exposures
2. Established track record
3. Data can be used for
real-time public
reporting
4. May currently have
greater potential for
accuracy than other
methods.
Volatilization losses for
high temperature
collection
Apparent sampling
losses for coarse
particles.
Involves collection of
particles on a filter,
rather than direct
measurements of gas-
borne particles.
Beta
1. Fast time response-better
information on temporal
exposures
2. Established track record
3. Data can be used for
real-time public
reporting
Fine mass measurements
are in poor agreement
with those from other
samplers. There must be
a lot of FRM-beta gauge
comparisons from
previous fine particle
measurements: are such
results typical?
Involves collection of
particles on a filter,
rather than direct
measurements of gas-
borne particles.
APS
2.
Provides valuable
supplemental
information on size
distributions.
In-situ measurements of
gas-borne particles (the
type we breathe!)
1. Not a mass measurement
method; should not be
considered as such
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Recommendations:
1. Consider adding the DRUM sampler, developed by Cahill and coworkers, for future
instrument evaluation studies. The DRUM can provide size and time resolved information on
elemental composition, absorption, and mass (through beta attenuation). Impactors operate on a
different sampling principle than those that have been studied to date. While all approaches have
pros and cons, impaction offers the benefit that coarse and fine particles are not mixed together
on the substrate, and that collected samples are less prone than filter samples to evaporative
losses. I am not aware that a commercial prototype of this instrument is currently available, but I
assume one could be developed quickly if the instrument were found to perform well.
2. Future atmospheric measurements should be carried out in airsheds that would provide new
challenges to coarse particle measurements. Examples of such phenomena include high
concentrations of coarse biological particles (e.g., pollens), the presence of volatile compounds
(e.g., organics, ammonium nitrate), etc.
3. I am of the view that the APS would not be an acceptable measurement instrument for coarse
mass compliance measurements, since it does not measure mass. Measurements are complicated
by variabilities in particle density and shape factor. These particle properties are likely to vary
spatially, temporally, and among particles of a given size at a given instant of time. I do not
think it will be possible to reduce to an acceptable level uncertainties in estimated masses
obtained with this instrument. On the other hand, the APS would be an excellent instrument for
intensive field studies of coarse particle size distributions, or of short-term temporal and spatial
variabilities coarse particles concentrations.
4. If measurements of spatial and temporal variabilities were to be carried out using an array of
APS instruments, consideration should be given to measuring the complete size spectrum (3 nm-
10 |im) at the same time. There is interest in spatial and temporal variabilities of ultrafine
particles, for example, and the sampling methodology and expertise required to study this
question would be the same as that required for studying coarse particles. The Supersite program
has led to the development of instrumentation systems and skilled personnel who could carry out
such measurements.
5.1 feel that EPA should require that all size-dependent efficiencies be characterized for any
instrument that they approve for compliance measurements of course particles. These include
size-dependent sampling inlet efficiencies, and size dependent collection efficiencies and
deposition losses in virtual impactors. In the latter case, measurements should be done for both
solid and liquid particles. Such information will provide valuable insights into measurements
obtained in different environments. Careful measurements of sampling efficiencies for the
standard PM10 inlet have been carried out. The modified PM10 inlet which is being used for the
high flow TEOM has not been studied with the same amount of care.
6. The report suggests that differences between the R&P PMc TEOM and PM FRM may be due
to the 9.0 - 9.5 jim cut point of the TEOMS' inlet. An effort should be made to determine
whether or not the measured size distributions substantiate this hypothesis.
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7. Can the measured size distributions provide insight into the extent to which coarse particles
may have intruded into the R&P dichot fine channel? This could be calculated from
measurements by making use of size-dependent efficiency curve for the R&P inlet (assuming
that they are known.)
8. I think it might be desirable to develop a PMc measurement technique that does not make use
of the PM2.5 FRM measurements. This would avoid confounding the PMc measurements by
fine particle sampling artifacts, which are known to occur in some environments. It would also
open the possibility to a methodology that provides automated measurements with higher time
resolution. Such measurements would provide data that would be useful for a wider range of
purposes than simply compliance monitoring.
9. I think future sampler characterization research should focus primarily on atmospheric
sampling as opposed to laboratory studies . The exception is measurements of size-resolved
sampling and collection efficiencies mentioned in point 5 above.
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Dr. Kimberly Prather
U.S. EPA/CASAC/AAMM Subcommittee
Kimberly Prather
Comments after the July 22, 2004 Meeting
The subject of a new coarse particle standard was discussed. We were informed the standard
would be PMc (PM10-PM2.5). The goals of the meeting were to help EPA identify a strategy to
allow them to choose the best measurement technologies for monitoring PMc.
First of all, the studies performed by EPA were clearly well thought out and conducted. I have
several comments with regards to the PMc standard as well as possible future studies:
1) The addition of the APS at all sites was a nice one as it could provide insight into changes in
size distributions. From the discussions, it sounds like it was chosen to help understand observed
differences between measurements and not to measure PM mass (although that was
investigated). I agree it would be useful to try and use these data to help understand the observed
differences. It seems like more could have been learned if the PM10 inlets had not been used (as
one committee member stated, you can always impose that cut-point later on) so you can directly
measure how shifting of fine and coarse size modes affects all measurements.
2) Differences in composition at all sites need to be better understood so the observed
differences in methods can be addressed. For the next round of studies, it would be worthwhile
for EPA to add semi-continuous measurements of nitrate, sulfate, and carbon using commercially
available instruments (rather than completely relying on filter-based sampling). It is likely the
instrument manufacturers would "loan" EPA the necessary instruments for these studies.
3) The issue of why/how PMc was chosen came up at the meeting. It appears (based on the
responses given) that the reasons for choosing this particular definition for the coarse standard
are not well founded. There could be issues with further convoluting two problems. Oftentimes
it is questioned as to why PM2.5 was chosen because it does not effectively separate the modes
(or PM sources) well (i.e. PM1 would be better at doing this). Now in choosing PM10 as a cut-
point, this does not pull out coarse mode particles well either (it is on the lower size shoulder of
the coarse mode a large fraction of the time). It would be most appropriate to use established
size distributions and source contributions to those distributions to establish the best cut-point.
The committee was told just to focus on the instruments (for this meeting) and that we could
come back to the PMc standard issue another time. My feeling is once we spend all the time and
effort choosing the best measurement methods and do further (costly) studies, no one will want
to go back and re-visit this issue. Since EPA is being forced to re-visit the best PM standard,
now would be the time to choose a cut-point that can be backed up by strong science. A
comment was made that we have the most health data for PM10. I would argue that since the
available health data yield conflicting conclusions on the health impacts of PM10, maybe this is
telling us there is a problem with where the line is being drawn. The key issue is to choose a cut-
point that "makes sense" based on the physical size distributions and source contributions to that
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size distribution (both of these area pretty well established). The proper cut-point which
separates sources will make understanding health impacts more straightforward and also allow
establishment of proper control strategies.
4) It was not clear if EPA is trying to choose only one measurement technique for monitoring
coarse PM or a suite of instruments. Ideally, one should choose a suite of instruments that yields
information on short term variability, size distributions, and allows speciation to be performed.
The trade-off of course is as the cost of the combination of instruments grows, less sites can be
studied (yielding less data on the spatial variability of PM).
5) The report did not give meteorological data which would be important for linking short term
variability with sources impacting an area. Additional knowledge on source impacts on the
coarse particle standard will be important for developing appropriate control strategies for
different areas.
6) One cannot underestimate the importance of performing some control (lab) studies to
understand discrepancies observed in the field. By this, I do not mean using pure (unrealistic)
particles, but using particles that could potentially contribute to PMc and cause differences in the
methods such as resuspended dust. In these studies, one could generate known aerosol
compositions, concentrations, and sizes and introduce them to all instruments simultaneously.
The comment was made that lab studies are not as good as field studies since it is difficult to
simulate "real" particles. This is true, but I believe carefully conducted lab studies
(complementary to those conducted in the field) using "real" particles that could give you the
biggest discrepancies is important for isolating the issues contributing to the observed
discrepancies. Oftentimes in the field, too many things are changing at oncelab studies allow
you to isolate these changes and study their effects. The pre-warning is there are an infinite
number of possible lab studies that could be conducted, so careful planning (based on field
results) is important.
7) A key point in picking an instrument is deciding the benchmark which represents the "true"
answer. It appears the method being used in this capacity is the FRM which is troublesome since
it has known issues. It certainly has been used to obtain a wealth of data, but this is not a valid
reason for continuing to use it. It only propagates the problem. Choosing a method because it
agrees with (or is consistent with) another instrument that has problems is not the best approach.
Time needs to be invested in seriously choosing a PM material that can be used (with the chosen
instrument/s) that will allow one to ultimately operate the instrument/s with the best accuracy.
8) What will be the time resolution of the standard? I do not believe 24 hours is short enough
coarse particles (particularly those emitted from sources such as coal combustion) show rapid
variability and very high values over short time periods. These high concentrations will be
averaged out over 24 hours. Since we do not fully understand the health impacts of these
particles, capturing these fluctuations is extremely important. Thus, choosing an instrument that
does "real-time" measurements will be important.
9) It wasn't clear if all of the inlets were the same in the report. These inlets should be fully
characterized before any further field studies are performed.
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10) Semivolatiles could be important in some regions and possibly account for some of the
observed differences (especially in changing the PM2.5 fraction). Some of the instruments used
were heated, others were not. It would be worthwhile to study how these species affect the
results. The toxicology of these compounds is pretty well established so their contributions
should not be ignored.
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Dr. Armistead (Ted) Russell
Comments on PMc Methods Evaluation and DQO reports - Ted Russell
Since I am getting these comments in a bit late in the game, I will try not to go over the
type of input provided so far.
First, the EPA staff and their contractors should be congratulated for this piece of work,
both the methods intercomparison and testing, and the DQO tool. A few things, though.
First, in the spirit of no good deed goes unpunished, I have been on the NAAMS
Subcommittee, and the good work that EPA did as part of putting together the NAAMS, they
should thoroughly consider and emphasize how the various methods fit in with that strategy, and
ask the Subcommittee to address that issue as well. They do address this, though I would say
more tangentially than directly, and their questions to the subcommittee do likewise.
Next, an overarching consideration, in part building upon Warren White's comments, but
with my own concerns, is that the ordering of the questions suggests that determining a reference
method is more important than achieving multiple monitoring objectives. From a regulatory
standpoint that may be true, but from an overall air quality management perspective that is not
true. Along those lines, I would not like to see us start emphasizing the use of an integrated
sampling approach, providing 24 hour samples every 3-12 days, as opposed to a (semi)
continuous sampler. Adoption of a 24-hour approach can inhibit the deployment of more
informative methods. (On the other hand, the use of a 24 hour method that also includes
speciation has some attractiveness.) As noted by White, the ability to identify correlations with
other pollutants and atmospheric variables is important for understanding the problems, and thus
the solutions. Having only 24 hour samples, taken every 3-12 days, greatly inhibits that ability.
As a modeler, I should add that having a 24-hour mass measurement is not great for evaluating a
model. If you are off a long ways, that is bad. If you are close, it is difficult to say if the reason
is that you have the processes about correct, or if there are compensatory errors. Indeed, one can
get the diurnal behavior backwards and still find good performance.
I see a second problem with an integrated sampler in that it presupposes the form of the
standard, which is presumed, at this point, to take the form of an annual average limit and a 24
hour limit. First, for the annual standard, I suspect some of the (semi) continuous samplers can
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provide very comparable results to the difference methods, so that is less of an issue. For
example, the TEOMs from the evidence provided, and seeing their operation for PM2.5, give
very similar results averaged over that long of a period. Even over a one-day period, we see that
they agree pretty well. But then we should ask, why a 24-hour standard? If exposure to high
concentrations is the issue, 24 hours is probably not the period of interest: one to eight hours
likely makes more sense. PMc exposure will likely happen predominantly outdoors as many
ventilation systems will remove this fraction of PM, and it has a more limited lifetime indoors.
Thus, the likely exposure will be for more limited times. Integrated samplers using the
difference methods are less able to capture the needed data for the shorter time periods for two
reasons. First, unless one is committing to tremendous labor commitments, identifying the
maximum eight hour average in one year is not likely. Second, the difference method gets less
reliable as the masses on the filters become smaller.
Putting the two issues above, together, also lead to a third reason not to have an
integrated sampler become the method of choice. If we continue to have, predominantly, 24
hour samples upon which to conduct health-related research, we will continue to find
associations with 24-hour metrics, which would then support 24 hour standards. If we were to
have more short term measurements, associations with other intervals might be found to have a
stronger association, and thus argue for a different form of the standard. It should also be noted
that PMc has a shorter atmospheric lifetime than PM2.5, so we would expect to see more diurnal
variation, further suggesting the appropriateness of a shorter period for a standard, if, indeed,
health effects are related to a shorter term exposure. (For a long-lived pollutant with relatively
little diurnal variation, the difference between using a 24 hour or 8 hour standard has less impact
than if it is a short-lived pollutant.).
With the above in mind, my answers to questions one and two as posed to the
Subcommittee are intertwined, and I have to speak primarily as a modeler, not a instrumentation
expert. Obviously, I would like to see the use of a continuous sampler for both. The data
provided, and recognition that TEOMs enjoy relatively wide spread use in the field, suggest that
they are an attractive method, either as a reference method or equivalent method, e.g., after
undergoing the process outlined in the NAAMS draft. The APS is a non-starter, at present for a
number of reasons in terms of the documented performance, as well as reasons given by others.
The difference method appears to be very repeatable between samplers, even by different brands,
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and may provide speciated data as well if one chooses (though this can be provided by adding
speciation filters to the other systems as well).
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Dr. Jay Turner
CASAC AAMM Evaluation of Coarse Particle Monitoring Methods
Comments Submitted by Jay Turner on July 20, 2004
in Preparation for the July 22, 2004 Meeting
Per the June 18, 2004 memorandum from R.D. Scheffe to F. Butterfield, the Subcommittee has
been charged with three questions: "(1) what are the strengths and weaknesses of each method
tested in the ORD study for the purposes of using it as a reference method, a measurement
principle, and as a method that would provide the basis for approval of candidate reference and
equivalent methods; (2) what are the strengths and weaknesses of each method tested to meet
multiple monitoring objectives such as comparison to potential PMc standards, public reporting,
trends, chemical speciation, and characterization of short-term episodes and diurnal variation;
and (3) for the PMc DQOs, is the process the Agency took to develop the estimates of
uncertainty appropriate and are here factors the Agency has included that should not be
considered or are there other inputs that should be included?" My written comments submitted
prior to the July 22, 2004 meeting focus exclusively on the first two questions.
What are the strengths and weaknesses of each method tested in the ORD study for the purposes
of using it as a reference method, a measurement principle, and as a method that would provide
the basis for approval of candidate reference and equivalent methods? The question of a
benchmark methodology1 begs us to first consider our overarching objective(s), as a frame of
reference is needed to assess strengths and weaknesses of the tested methods. If PM2.5 serves as a
model, we cannot expect to accurately quantify PMc mass concentration by any single
measurement. Therefore, we first need a conceptual model for PMc - its physical and chemical
properties - and subsequently need to determine the salient features which we desire to capture
(possibly at the expense of accepting bias in other features). For example, in the case of PM2.5
FRM methodology the role of aerosol bound water is addressed by conditioning filter samples at
a fixed relative humidity; we are not measuring the true aerosol content (nor do we necessarily
desire to do so in this case) but rather bring all samples to common conditions which is at least
interpretable. In one light this might be considered a weakness (significant departures from
reality) and in another light it might be considered a strength (typically suppressing the role of
aerosol bound water and to a large extent harmonizing across the network). As another example,
we know that the PM2.5 FRM methodology can be susceptible to large losses of ammonium
nitrate; this is largely considered a weakness and thus another method that properly quantifies the
ammonium nitrate might be favored if any corresponding tradeoffs are deemed acceptable. The
relative roles of water, organics adsorption, nitrate losses, and so on are likely quite different for
fine PM and coarse PM; are we in a position to articulate the various factors that might influence
PMc measurement and prioritize the most important features which any benchmarking
methodology should capture? I am reticent to move to quickly and deeply into casting the
methods comparability in terms of strengths and weaknesses until we holistically (to the extent
11 intentionally lump the three stated uses (reference method, measurement principle, basis for approving reference
and equivalent methods) into simply "benchmarking methodologies", for the present time ignoring the important
distinctions between the three uses.
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practicable) describe the system we are trying to measure. That said, some comments are
warranted based on the ORD study.
Integrated filter measurements offer simplicity at the expense of labor intensiveness and data
incompleteness (the latter may or may not be an issue, depending on the outcomes from the
DQO Process). The PM2.5 and PMio measurements in the ORD study feature high precision and
thus the propagated uncertainty from the PMio minus PM2.5 differencing method for PMc mass
concentration might be acceptable. An underlying assumption - still to be verified - is that any
artifacts in the fine fraction of PMio are the same in the separately collected PM2.5 sample (that
is, there are assumed no matrix interactions between the fine and coarse particles on the PMio
sample). The dichotomous sampler method has reduced sensitivity to the PM2.5 precision - a
smaller adjustment is required to correct the coarse channel mass concentration for fine particle
intrusion than to correct PMio for PM2.5 - but there are differences in the flow rates for the fine
and coarse channels which may affect the relative role of fine PM artifacts between the channels
and bias the applied correction. Other dichotomous sampler issues warranting attention include
the virtual impactor design and losses in filter shipping for conditions typical of network
deployment (rather than a special research study). In the latter case, the ORD study results are
encouraging but more work is needed.
Semicontinuous measurements are less labor intensive (assuming the instruments are indeed
field robust) and offer greater data completeness. The temporal averaging of near real-time
measurements may suppress certain artifacts inherent in the temporal integration of the substrate
sample collection. Within certain constraints, there are opportunities to tune the instrument
design (e.g., inlet heaters) to address the most desired features. On the other hand, if one can use
PM2.5 as a model it appears there might be no "one size fits all" approach to such tuning and
again we must consider strengths and weaknesses in light of the to-be-defined features. The
ORD study presents the semicontinuous methods to the filter methods (specifically, the
differencing method). If the goal is a semicontinuous method which quantitatively aggress with
the differencing method, the preliminary results are encouraging but there is much more work to
be done to reconcile the differences and hopefully improve the comparisons. It is not clear to me
at this point, however, that this is the explicitly-stated goal. Furthermore, the comparisons are
clouded by differences in inlet cutpoints and sample stream conditioning (e.g., use of inlet
heaters); it would be very helpful to have well-characterized inlets and a better understanding of
how the conditioning might affect the respective measurements.
The ORD study provides a rich data set for probing key questions concerning the status of PMc
measurements. The first report emerging from that work - "Multi-Site Evaluations of Candidate
Methodologies for Determining Coarse Particulate Matter (PMc) Concentrations" - covers
substantial ground towards presenting and interpreting the results. It is acknowledged that the
report cannot be exhaustive, and the following specific comments are offered towards placing the
presented results in context and suggesting items that could be probed in the existing data set.
(1) It would be very helpful to see scatter plots in addition to times series for most of the
comparisons provided in the report. Even in cases where the R2 is high, there is still
much to be learned from scatter plots in terms of the quality of the agreement between
different methods.
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(2) While the measurement methods evaluation should not presume a specific form for a
PMc standard (e.g., averaging time and threshold concentration), historical approaches to
regulating PM suggest that both long-term averages (quarterly, annual) and relatively
short-term averages (e.g., daily) might be considered. The first ORD report focuses
largely on study-average results (e.g., mean ratios)2; it would be very helpful to show
how the instruments compare for conditions that would be representative of possible
violations of a short-term averaging period. For example, is there any degradation in the
collocated precision at high daily-average concentrations? Is there any degradation in the
agreement between the semicontinuous measurements and filter differencing
measurements at high daily-average concentrations? The data set might be too small to
address these issues in great detail, but some elaboration would be helpful (again, scatter
plots would be a first step).
(3) One stated possible explanation for the Coarse TEOM underestimation of PMc (relative
to the FRMs) is the inlet cutpoint being closer to 9 |j,m rather than 10 |j,m. Can the APS
data shed light on the extent to which the discrepancy can be reconciled by the difference
in inlet cutpoints?
(4) While the Tisch SPM-613D units seem to perform well for PMc, the large differences for
PM2.5 are disconcerting. A clear understanding of the source of the discrepancy must be
elucidated before the instrument could be used with confidence.
What are the strengths and weaknesses of each method tested to meet multiple monitoring
objectives such as comparison to potential PMc standards, public reporting, trends, chemical
speciation, and characterization of short-term episodes and diurnal variation? The
aforementioned first report by ORD provides little insights into the data relevant to addressing
this question (although there is likely substantial information which could be mined from the
study); thus, my comments are presented in general terms. The filter methods are superior for
chemical speciation and the semicontinuous methods are superior for public reporting and
characterization of short-term episodes and diurnal variation. I make these statements with
qualification, however, as I am inferring robust methods are being used. For chemical speciation,
while the filter differencing method shows promise for having adequate precision in the PM2.5
and PMio mass concentration measurements such that the PMc mass concentration would have
acceptable precision, it is not clear whether this would be the case for all chemical components
of interest. An analysis is needed to determine whether the differencing method would be robust
for chemical components and not just total mass concentration. The semicontinuous methods
clearly are superior for public reporting if the goal is timely reporting; as for diurnal variations,
the semicontinuous measurements are valuable if the measurement biases (which may have
diurnal variation depending on their origin) do not mask the true diurnal structure. Both filter and
semicontinuous methods may be suitable for comparison to the PMc standards although there are
certainly tradeoffs and different forms of the standard may warrant different DQOs which in turn
influence the selection of a preferred measurement method.
! Note that C. V. values were not reported in Tables 6 and 7.
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In summary, the ORD study offers very valuable insights into measurement methods for PMc;
with additional conceptual work and mining of the data we can move towards evaluating the
suitability of these methods (in current form and subject to design/operation changes) and
possibly other methods.
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Dr. Warren H. White
Comments on PMc Methods Evaluation and DQO reports - Warren H. White, 7/10/04
I am happy with the homework EPA is doing on PMc measurements. The Agency is showing
admirable initiative in this area, and the technical quibbles I raise on the following pages are
intended mainly to reinforce the diligence it is already exhibiting.
An important concern for the methods evaluation and DQO process is that they not focus too
narrowly on NAAQS attainment decisions. The charge to the subcommittee takes note of other
monitoring objectives such as public reporting, trend detection, and episode characterization. I
would like to highlight another that is at least as important as any of these, the accurate
determination of coarse particles' covariance with other particle fractions and gas species. The
characterization of this covariance requires measurement precision well beyond that needed to
establish an annual mean, and at concentrations well below those of concern for a 24h standard.
PMc measurements should be as precise as feasible because (White, 1998, J. Air & Waste
Manage. Assoc. 48, 454-458)
(a) noise always depresses observed correlations with other measurements,
(b) historical PMc measurements have been much noisier than associated PM2.5
measurements, and
(c) crucial inferences about health effects and atmospheric behavior are routinely based on
the differences observed between correlations involving PMc and PM2.5.
(For a ready example of the last phenomenon, consider page 9-16 in the June 2004 draft Criteria
Document: "new data reinforce our earlier understanding that ambient concentrations of fine
particles (measured as PM2.5) are typically more highly correlated and/or are more uniform
across community monitors within an urban area than are coarse particles (measured as PMio-2.s),
... Thus, central site ambient concentration measurements are a better surrogate for population
exposure ...")
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1. The inter-manufacturer precisions given in Table 2 of Attachment 2 for the collocated
FRM samplers in the evaluation study are surprisingly good. I gather that these numbers come
from the formula in Appendix 3B of Attachment 3, which subtracts out the contribution resulting
from the mean difference between measurements. Is that correct? If so, it would be useful also
to tabulate the observed root-mean-square differences to give a more direct picture of the overall
measurement uncertainty.
It is also worth noting that the high PMc/PM2.5 ratios sampled in the evaluation study maximize
the precision of the PMio - PM2.5 difference measurement. They are not representative of
conditions in many of the studies that find little association between PMc concentrations and
health effects. The six cities analysis of Schwartz et al. (1996), for example, was based on PMi0.
2.5/PM2.5 ratios that averaged less than 1/2.
2. The foundational discussion of Figure 1 in Attachment 3 could be much clearer.
Defining the "gray zone" as the S-shaped region between the performance curves is, first of all, a
distraction because the visual area of that region is only weakly related to the amount of
indeterminacy facing the decision-maker.* The true gray zone for decisions is the interval on the
x-axis of measured concentrations where the Agency cannot tell, with the requisite confidence,
whether the actual concentration is above or below its action limit. This gray zone is better
indicated by the rectangle between the dotted vertical lines drawn through the intersections
between the power curves and the dotted horizontal lines that represent the decision error limits.
This is in fact the way "gray region" is defined in EPA QA/G-4, "Guidance for the Data Quality
Objectives Process."
* To see that the area of the existing "gray zone" is uninformative, consider the situation where
a perfectly accurate measurement is made once every six days for an annual-mean standard. In
this case the performance curves for positive and negative bias coincide, but there is still
sampling uncertainty about the actual mean value, which shows up in the curve's non-vertical
slope.
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A secondary source of confusion is the numerical example offered: "for an estimate that truly is
17 ug/m3 and the measurement system has a 10% negative bias, then 50% of the observed
estimates will be declared to be less than the 15 ug/m3 action limit." The situation described
generates data identical to those from a true value of 15.3 ug/m3 measured with no bias, and
something is fishy if 50% of all unbiased measurements yield estimates below 15 ug/m3 for a
true value of 15.3 ug/m3! This may seem a minor discrepancy in practical terms, but it hardly
contributes to a reader's confidence that he fully understands the concept it is intended to help
explain.
3. The details of the DQO modeling in Attachment 3 are sometimes murky. The values
given for seasonality ratio and population variability at the bottom of page 4 don't agree with
those in the "Sel." Column of Table 1. The first few columns of Table 1 have apparently been
mislabeled in copying from Table 4-1 in Appendix 3 A. It is not clear why the relatively high
value chosen for the PMc/PM2.5 ratio should be considered "conservative," since PM2.5 acts as an
interference in the PMc measurement. And given the a priori assumption of zero
autocorrelation, it is premature to conclude that sampling frequency is one of the factors "gray
zones are most sensitive to".
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Dr. Yousheng Zeng
Comments by Yousheng Zeng
1. Among the five PMc measurement approaches, the dichotomous beta gauge method
appears more attractive than the others. It is automated and provides continuous (strictly
speaking, semi-continuous) measurements as opposed to manual measurements of the
other two filter-based methods (FRM and the sequential dichotomous samplers).
Compared to the FRM, the dichotomous beta gauge method does not require two co-
located samplers. It also minimizes the potential errors and costs associated with filter
handling and weighing. Compared to the two non-filter-based continuous methods, the
samples of the dichotomous beta gauge method may be preserved for a further analysis of
physical and chemical properties. The performance of the dichotomous beta gauge
method tracks FRM significantly better than the other two continuous methods. The
weakness of the dichotomous beta gauge method appears in the PM2.5 area - it
overestimates PM2.5. I would like to offer the following thoughts that may or may not
help overcome the weakness.
The overestimation may be caused by (1) the different patterns of beta ray attenuation
between fine and coarse particles (due to the particle size and other physical and chemical
characteristics) or (2) the difference in particle mass load level between the fine and
coarse channels. If reason (1) is the dominating factor, it may be difficult to overcome
the overestimation issue. However, if reason (2) is the dominating factor, we may be able
to improve the method by increasing the mass load. Specific implementation of this idea
of increasing the mass load may be dependant on the linearity range of the beta
attenuation-particle mass load curve (see Figure 1 for illustration).
w
ฃ
3
S
a
M
Particle Mass Load on Filter
Figure 1. Hypothetic response curve of beta gauge monitor.
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It is possible that the overestimation is caused by the non-linear response (overly
sensitive response) in the low mass load region of the curve (Region A in Figure 1).
Depending on the upward extent (towards high mass load) of the linear region of the
curve (Region B in Figure 1), the above idea may be implemented differently.
If the linear range extends upward (towards high mass load) sufficiently, we may
evaluate the feasibility of changing the 1-hour cycle time to 3-hour cycle time. The mass
load of the PM2.5 filter will be tripled and hopefully enter into the linear range. A longer
cycle time (still less than 24 hours) can also be evaluated. This will reduce the time
resolution of the method, but should not cause any problem because the standard is on a
24-hour basis. Another possible consideration is to increase the overall sample flow
without increasing the diameters of the nozzles that deposits the particles to the filters,
therefore increasing the mass load to the filters.
If the linear range does not extend upward far enough or we don't want to change the
upper bond, we may consider increasing the cycle time for the fine particle channel only.
This should be feasible because the mechanism for advancing the filter tapes for the fine
and coarse channels can be separately controlled.
Linearity is not an issue for the other two filter-based methods because they rely on
gravimetric measurements. For a beta attenuation based method, linearity is an important
factor. If the EPA or the manufacturer has the linearity data, it will be interesting to
review the data to assess the feasibility of the above idea. If the linearity data is not
readily available, the detailed data collected during the EPA PMc multi-site evaluation
may offer some clues. In this case, the "true" mass load can be derived from the co-
located FRM. Hopefully the data sets are large enough and cover a reasonably wide
range of mass load. If the above hypothesis is true, we should see less overestimation
during days when the PM2.5 concentrations are high than the days when the PM2.5
concentrations are low.
In summary, if the overestimation of PM2.5 is related to the linearity of the beta
attenuation response to the particle mass load, the weakness may be overcome by
manipulating the amount of particle mass deposited on the filter tape using one or a
combination of the above approaches.
2. The intrusion of coarse mode aerosols into the fine channel (major flow) in virtual
impactors was discussed in the study. The issue is applicable to the sequential
dichotomous samplers, the dichotomous beta gauge samplers, or the TEOM samplers.
Has there been any study to evaluate whether or not the intrusion can be minimized by
increasing the diameter of the entrance of the coarse channel (minor flow) while
maintaining the same major/minor flow split? The separation of PM2.5 and PMc is
determined by the geometry of the separator and the face velocity at the entrance of the
coarse channel. The minor flow is just a carrier to transport the already separated coarse
particles to the coarse filter and its volumetric flow rate should not be critical for coarse
particle separation. The opening of the coarse channel (minor flow) can be larger than
the nozzle above it. A larger opening may prevent coarse particles from slipping into the
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fine channel. It should not increase the amount of fine particles entering the coarse
channel as long as the volumetric flow rate into the coarse channel (minor flow) remains
the same because the fine particles should be evenly distributed within the air flow. In
the extreme case where the minor flow rate is reduced to zero, the coarse particles should
still be captured in the coarse channel (essentially same as the 2.5 (j, impactor well in the
sampler of FRM for PM2.5). This analysis also leads to another related topic. If this
analysis is valid, the split between the major flow and the minor flow should be a little
more flexible (as opposed to 9:1) as long as both flows can be measured to the degree of
desired accuracy to keep track of partition of the fine particles between the two filters for
correction to the filter weights.
3. The Tisch SPM-613D dichotomous beta gauge samplers use mass flow sensors to control
the flow in the major and minor channels. In Section 2.3 of the PMc multi-site evaluation
report, it is stated that"... however, the effect of this lack of volumetric flow control is
minimal if ambient conditions do not differ substantially from those existing during the
flow calibration". Is data available to support this statement? How will the diurnal
temperature changes affect the accuracy of the desired volumetric flows? For a
temperature change from 70 ฐF to 100 ฐF during a day, the volumetric flow can be
impacted by about 6%. The face velocity at the separation point will also change by
about 6%. Depending on how steep the separation curve is, this kind of change should be
evaluated quantitatively. Is there any reason that this sampler design cannot use the
volumetric flow control?
4. I like the EPA DQO Tool and consider it very useful. I have some questions or
comments on the data input to the PMc DQO Tool. Some input data was derived from
the PM10 and PM2.5 data in the EPA Air Quality System (AQS) database. In the
Technical Report on Estimating Parameters for the PMc DQO Tool, data was derived
from 622 sites across the nation. My question is - Do all of these sites use the modified
low volume PM2.5 samplers for PM10 (i.e., the PM10 samplers used in the EPA multi-
site evaluation, which is the PM2.5 sampler without WINS fractionator)? Current FRM
for PM10 (40 CFR 50, App M) does not specify a specific type of sampler. A site may
continue to use a traditional high volume PM10 sampler for PM10 and a low volume
PM2.5 sampler for PM2.5. If significant portion of the input data to the PMc DQO Tool
involves high volume samplers, and portion of the input data is derived from the multi-
site study (which did not include the high volume sampler), is there a mismatch that may
cause a problem? The aerodynamic characteristic (the shape of the separation curves),
the performance (bias, precision, etc.), and other parameters between high volume PM10
samplers and the low volume PM10 samplers modified for this study are expected to be
different. What level of uncertainty will be introduced by these differences?
Furthermore, if we want to fully utilize the historical data collected by traditional high
volume PM10 samplers for assessment of PMc, it will be valuable to evaluate the
performance of the high volume PM10 samplers in the same context and the relationship
between the high volume PM10 sampler and the low volume PM10 sampler. We know
that PM10 samplers just mean their "cut diameter" is 10 ji and they do not separate the
particles at exactly 10 \i. The actual sizes of particles allowed to enter the samplers are
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determined by the "S" shape performance curve. The steeper the curve is, the better
separation the samplers will have. The curves for the high volume PM10 samplers and
the PM10 samplers modified from PM2.5 samplers are expected to be different. It would
be interesting to review these curves during the discussion to assess the impact and
usability of data. This kind of further analysis can also help the bridge the gap between
the historical data and future data.
U.S. EPA/SAB/CASAC/AAMM Subcommittee
Yousheng Zeng
Additional Comments Post July 22, 2004 Meeting
July 31,2004
The following additional comments are provided:
1. As stated in my pre-meeting written comments, the DQO Tool software seems to be a
very promising tool. However, if I understand the DQO Tool correctly, based on the
review materials distributed for the July 22nd AAMM Subcommittee consultation
meeting, one important element may be missing and improvements in this area should be
feasible.
PM sampler bias is an important input parameter to the DQO Tool. The bias may be
estimated based on field study data. However, if there is a known underlying physical or
chemical process that may produce a significant bias, this physical or chemical process
should be considered in the bias estimation. In this case, the interplay between the
sampler's fluid dynamic characteristics and the particle size distribution of the
measurement target may potentially be a significant source of bias. I have created a
simple spreadsheet based computer simulation program (hereafter referred to as "PM
Measurement Simulator" or "PMM Simulator" and submitted as a separate file) to
evaluate this type of bias. Attachment 1 to this comment includes an example of the
output of the PMM Simulator to illustrate both the importance of this type of bias and the
possibility of incorporating it into the DQO Tool.
The FRM and other two candidate methods for PMc measurement are included in
Attachment 1. The two candidate methods can be any methods that separate PM2.5 and
PMc. The separation performance curves are presented in Figure 1 of Attachment 1. In
order to evaluate the bias, three hypothetical ambient air PM cases are also created and
presented in Figure 2 of Attachment 1. With the hypothetical samplers and ambient air
cases, the theoretic bias (before considering other biases such as evaporation losses etc.)
of the two candidate samplers with respect to the FRM can be evaluated and the results
are presented in Table 1 of Attachment 1.
B-87
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The results in Attachment 1 offer some insight and understanding of the potential sources
of bias. In the case of City 1, for example, Candidate Method 1 performs well. It has
less than 2% of bias from the FRM for all three particle size groups. However, when the
same sampler is used in City 2, it has 13.7% bias. This example illustrates the
importance of the interplay between the sampler separation curves (Figure 1) and the size
distribution of the measurement target (Figure 2). It also illustrates the danger of broadly
applying the bias data derived from limited field studies to all sites. A comparison
between Candidate Methods 1 and 2 in Attachment 1 also quantitatively shows a general
knowledge - the steeper the separation curve is, the better the separation will be. Since
the FRM has a good steep curve, the candidate methods should have similar curves in
order to minimize the bias.
2. As discussed above and illustrated by Attachment 1 (or the PMM Simulator), the
relationship between the sampler separation curves and the size distribution of the
measurement target is very important (for easy reference, we may call it "theoretic PM
sampler bias"). Particle size distribution needs to be established in order to estimate the
bias. However, we often don't know the size distribution of the measurement target.
This problem will significantly reduce our confidence in PMc measurement. An
alternative solution to particle size distribution is to use the separation curves of the FRM
as the reference and require all candidate methods to closely match the FRM separation
curves. Candidate methods can be automated and can provide better time resolution. If
they also match both the PM10 and PM2.5 separation curves of the FRM, we will have
much more confidence in these methods. A simulation using the PMM Simulator will
show that the theoretic PM sampler bias will be eliminated no matter what the ambient
PM conditions are if a candidate method has the same separation curves as the FRM.
Because there are no true values in PM measurements, the FRM measured value is
considered true value. Therefore the bias issue unique to the PM measurement can be
reduced to and managed as the same general bias issue in other air pollutant
measurements such as 862 monitoring.
In practice, separation curves of a candidate method probably will not exactly match the
separation curves of the FRM. EPA may consider compiling a set of ambient PM particle
distribution curves that represent a wide range of possibilities. With the compilation of
such size distribution data and the PMM Simulator, the potential bias of the candidate
method can be evaluated and a tolerance level for deviation of the candidate separation
curves from that of the FRM can be assessed. This can be accomplished by building a
size distribution database in the DQO Tool and incorporating the PMM Simulator into
the DQO Tool.
3. The approach discussed above and the PMM Simulator may also help us better
understand vast historical PM monitoring data collected using high volume PM sampler.
In order to do this, separation curves for these samplers will be needed (I assume that the
data may be obtained from the manufacturers). If this approach is incorporated into the
DQO Tool, the DQO Tool will be more comprehensive. Also, EPA may consider adding
a few typical high volume PM10 samplers in the next field (or lab) study for the purpose
-------
of possibly bridging the gap between the historical PM data collected by high volume
samplers and the data collected by new samplers.
As a follow-up to my pre-meeting written comment No. 2,1 made rough calculations
based on the nozzle dimension data provided by Mr. Tom Merrifield during the July 22nd
meeting. The acceleration nozzle ID. (Dl) of the Kimoto impactor is 3.2 mm, the coarse
receiver nozzle ID. (D2) is 4.0 mm, and the nozzle to nozzle distance (S) is 4.0 mm. The
ratios of D1/D2 and Dl/S are 0.8, relative close to 1. The similar ratios in the PM2.5
impactor in the FRM are much smaller (D1/D2 ~0.14; Dl/S -0.3; these numbers are not
confirmed, but the differences are larger enough to make a qualitative statement). I just
want to reiterate my pre-meeting comment No. 2. Increasing D2 may reduce coarse
particle intrusion and make the separation curve steeper (see my pre-meeting written
comment No. 2 for rationale).
B-89
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U.S. EPA SAB CASAC AAMM Subcommittee
Yousheng Zeng
Additional Comments
7/31/2004
Attachment 1. Relationship Between PM Sampler Separation Curves
And PM Monitoring Results
Assumed Particle Separation Curves of PMc Measurement Methods
Each PMc measurement method has two separation curves, one for PM10 (i.e., sampler inlet)
and one for PM2.5 (i.e., WINS in FRM or virtual impactor in Dichotomous type sampler). For
illustration purpose, the following separation curves are used. Although the actual curves may
be different; the conclusions drawn from this illustration should be valid.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
0.1
Figure 1. Separation Curves of Methods
-FRM PM2.5
-FRM PM10
-Candidate 1 PM2.5
Candidate 1 PM10
-Candidate 2 PM2.5
Candidate 2 PM10
Particle Diameter
10
100
Three
Hypothetical Cases for Ambient Air PM
0
0
o
(A
(A
TO
5
18 i
14
m
8 i
A
9
Figure 2. Hypothetical Ambient Air PM
City 1
City 2
r\
/ \
/ \
^ *\ X/"\X
/! ^ ^ / \ \
0.1
;//
^_^/
\ / V \.
^*~^' ^^~^^^~~~.
1 10 100
Particle Diameter
B-90
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Simulated Monitoring Results
If the above PMc measurement methods are used in the three cities, the following results are
expected:
Table 1. Simulated Monitoring Results
City/PM
FRM
Candidate Method 1
Result
DifffrFRM
% Diff
Candidate Method 2
Result
DifffrFRM
% Diff
City 1
PM10
PM2.5
PMc
49.0
15.5
33.6
49.8
15.7
34.1
0.8
0.3
0.5
1 .6%
1.8%
1.5%
49.9
16.9
32.9
0.8
1.5
-0.6
1.7%
9.5%
-1.9%
City 2
PM10
PM2.5
PMc
74.1
15.2
58.9
74.5
17.3
57.2
0.3
2.1
-1.7
0.5%
13.7%
-3.0%
71.7
20.7
51.0
-2.4
5.5
-7.9
-3.3%
36.0%
-13.4%
City 3
PM10
PM2.5
PMc
85.9
19.6
66.3
88.5
21.1
67.3
2.6
1.5
1.1
3.0%
7.7%
1.6%
89.4
24.7
64.8
3.5
5.1
-1.5
4.1%
25.7%
-2.3%
The results are also highlighted in Figure 3.
Figure 3. Deviations (%) of the two candidate methods from FRM
in three hypothetic cities
Deviation from FRM
40.0% -
C
"re
0)
-10.0% -
-20 0% -
-
City 1
J
PM10 PM2.5
PMc
Candidate Method 1
a Candidate Method 2
De
50.0%
40.0%
ฃ 30.0%
.1 20.0%
f 10.0%
0>
Q 0.0%
-10.0%
-20.0%
viation from FRM - City 2
i i
PM10 PM2.5
Pjtlc
Candidate Method 1
D Candidate Method 2
Dt
50.0%
40.0%
IS 30.0%
.1 20.0%
> 10.0%
HI
Q 0.0%
-10.0%
-20 0%
Aviation from FRM - City 3
I
_T
-..dl.-L.
PM10 PM2.5 PMc
Candidate Method 1
D Candidate Method 2
B-91
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Participate Matter Measurement Simulator (PMM Simulator)
Created by Yousheng Zeng. Member of Ambient Air Monitoring and Methods (AAMM) Subcommittee
U.S. EPA Science Advisory Board {SAB), Clean Air Scientific Advisory Committee (CASAC)
Preliminary - Verification or refinement (e.g. correction forfines in coarse channel) may be needed
July 2004
Input
MM
Am
suremcnt Method
Federal Ref Method (FRM)
Cand.dale Method 1
Candidate Method 2
Oaf
P-
P
DCJ
P
DCJ
P
-K'2.5
2.5
t
2.5
3
2.5
1.5
P\\ 1 0
10
6
10
4
10
1.5
Cut diameter of PM separation mechanism of the
method
Power parameter to determine how steep the separation
curve is.
Jient Air PM Size Distribution
City 1
City 2
City 3
Sigma=
F re mcce
F re mode
Coarse Tode
F re mode
Coarse rode
Dpcafc
0.7
11
0.7
11
1.1
K
Sigra
0.5
O1 5
0.8
0.8
1
0.5
A
20
10
18
20
25
20
Particle diameter corresponding lo the peak of the
particle size distribution curve for each mode-
A parameter describing how wide the particle size
distribution curve spreads.
Amplitude parameter lo determine the heighl of each
particle size distribution curve (simulated PM
concentration level).
Caution Because the distribution curves are on log
scale, the magnitude of the fine mode is smaller than it
appears.
Separation Curves of Methods
90%-
701*-
s ---
n.
'
~-~-
^"S
" t-VMP
' FRMP
Candid
~ Candid-
" Candid
Candid
JI2S
,110
me 1 PMI2.5
itel PM10
Jte2PM2.5
r.e'2 >;V1U
^\\ \
XA\ \
N^
y
Vyv \
\\ N. V
\>r^^^" V\.
X^""--^^
01
1 1C 100
F'article Diameter
16-
14- -
O ,.
E J
Ambient Air PM Size Distribution
/~\
City '.
Cjty3
i
/I
/ *
/U
r_
i
\
\ >'^^NV
> s/r^\^
\ / \
s/ \ / x K
f^ Xv^^w^ "V^ "^V^,,
0.1
1 10 100
Particle Diameter
PMMS Zeng
Simulation Panel
1 of 2
B-92
-------
Sir
nulated Monitoring Results
If (tie above PMc measurement methods are used in the three cities, the following results are
expected:
Crty'PM
City 1
PM10
PM2.5
PMc
City 2
PMtt
PM2.5
PMc
City 3
PM10
PM2.5
PMc
FRM
49.0
15.5
33.6
74.1
15.2
53.9
85.9
19.5
66.3
Candidate Method 1
Result |Diff fr FRM |% Diff
49.3
157
34.1
74.5
17-3
572
885
21 1
673
0.8
0.3
0.5
0.3
2.1 1
1.7
2.6
1,5
1.1
Candidate Method 2
Result |Dlff fr FRM |7- Diff
1.6% M10 PM25 PMc ,QOH
andidate Mefic-a 1
andidate uaptx 2
i i -T ^^ ^~"MB r~" ฐ ^
PM10 PM2.fi Pljlc | ]Q ^^
Canddate Method '
DC*--2<2ate Method 2
n
i 1
i r~i
PM10
i
cLL
PKZ.5
' ""T i
Pile
C^iadate Method 1
HCanaciate Msthod 2
PMMS Zeng
SimLilation Pane!
2 of 2
B-93
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NOTICE
This report has been written as part of the activities of the Environmental
Protection Agency's (EPA) Clean Air Scientific Advisory Committee (CASAC), a
Federal advisory committee administratively located under the EPA Science Advisory
Board Staff that is chartered to provide extramural scientific information and advice to
the Administrator and other officials of the EPA. The CAS AC is structured to provide
balanced, expert assessment of scientific matters related to issue and problems facing the
Agency. This report has not been reviewed for approval by the Agency and, hence, the
contents of this report do not necessarily represent the views and policies of the EPA, nor
of other agencies in the Executive Branch of the Federal government, nor does mention
of trade names or commercial products constitute a recommendation for use. CAS AC
reports are posted on the SAB Web site at: http://www.epa.gov/sab.
B-94
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