Ambient Air Quality Monitoring and
Health Research:
Summary of April 16-17, 2008
Workshop to Discuss Key Issues
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Research and Development
Office of Air and Radiation/Office of Air Quality Planning and
Standards
DECEMBER 2008
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December 2008
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EPA452/S-08-001
December 2008
Ambient Air Quality Monitoring and
Health Research:
Summary of April 16-17, 2008
Workshop to Discuss Key Issues
U.S. Environmental Protection Agency
Office of Research and Development
and
Air Quality and Assessment Division
Health and Environmental Impacts Division
Office of Air Quality Planning and Standards
Office of Air and Radiation
Research Triangle Park, North Carolina
December 2008
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DISCLAIMER
This workshop summary report serves to document recommendations presented at a
recent workshop held by the U.S. Environmental Protection Agency's Office of Research and
Development (ORD) and the Office of Air Quality Planning and Standards (OAQPS) within the
Office of Air and Radiation (OAR). The recommendations described in this report have been
modified to reflect information developed during the review of this document and may be
modified in subsequent discussions with internal and external experts. ORD and OAR will use
the recommendations presented in this document as a tool to consider and prioritize both short-
and long-term actions for EPA and others to undertake in the development and implementation
of ambient air monitoring and health research strategies. Mention of trade names or commercial
products does not constitute endorsement or recommendation for use.
December 2008
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TABLE OF CONTENTS
LIST OF ACRONYMS Hi
AUTHORS AND CONTRIBUTORS v
INTRODUCTION. 1
Background 2
Workshop Structure, Objectives, and Outline of Session Summaries 2
Initial Successes and Next Steps 3
SESSION I: ELEMENTAL AND ORGANIC CARBON MEASUREMENTS 5
Background/Objectives 5
Session Overview 5
Major Points Raised by Participants 6
Recommendations/Actions for Consideration 9
SESSION II: ACCESSING AMBIENT AIR MONITORING DATA 11
Background/Objectives 11
Session Overview 11
Major Points Raised by Participants 12
Recommendations/Action Items for Consideration 14
SESSION III: AMBIENT AIR MONITORING FOR HEALTH RESEARCH 17
Background/Objectives 17
Session Overview 17
Major Points Raised by Participants 17
Recommendations 19
Action Items for Consideration 20
SESSION IV: THORACIC COARSE PARTICLE COMPONENTS AND POTENTIAL HEALTH
IMPACTS 22
Background/Objectives 22
Session Overview 22
Major Points Raised by Participants 24
Recommendations/Actions for Consideration 26
SESSION V: AMBIENT AIR MONITORING REALITIES - EPA/STATE/LOCAL
PERSPECTIVES-SUMMARY AND RECOMMENDATIONS. 30
Background/Objectives 30
Session Overview 30
Major Points Raised by Participants 30
December 2008 i
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Recommendations/Actions for Consideration 35
BACKGROUND MATERIALS 39
Appendix A: Workshop Agenda and Participant List A-l
Appendix B: Session I: Elemental and Organic Carbon Measurements - Chemical Speciation
Network (CSN) Carbon Issues B-l
Appendix C: Session II: Accessing Ambient Air Monitoring Data - Access to EPA's Air Quality
Data for Health Researchers C-l
Appendix C.I - Other Data Access Mechanisms C-10
Appendix D: Session III: Ambient Air Monitoring for Health Research - Air Quality Sampling:
Benefits and Costs of Daily Health Targeted Monitors for Fine Particle Components D-l
Appendix E: Session V: Ambient Air Monitoring Realities - EPA/State/Local Perspectives -
Ambient Air Monitoring Network: Network Design and Site Selection Approval E-l
Appendix F: Session V: Ambient Air Monitoring Realities - EPA/State/Local Perspectives -
Ambient Air Monitoring Method Implementation F-l
Appendix G: Preliminary Survey of Ambient Air Monitoring Sites Currently Being Considered in
EPA-funded Epidemiology Studies Feb 2008 G-l
December 2008 ii
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LIST OF ACRONYMS
The following acronyms have been used for the sake of brevity in this document:
AMTIC
AQS
ARMs
AQI
BC
BOSC
CAA
CAAAC
CASAC
CMAQ
CSN
DASH
DEARS
DRI
DRUM
EC
EPA
FDMS
FEM
FRM
GEO
GIS
HEI
IMPROVE
KML
MESA
MYP
NAAQS
NACAA
NARSTO
NCER
NCore
OAQPS
OC
ORD
PM
PM2.5
PM10
PMiQ-2.5
PQAO
QA
SEM
SLAMS
Ambient Monitoring Technology Information Center
Air Quality System
Approved Regional Methods
Air Quality Index
Black carbon
Board of Scientific Counselors
Clean Air Act
Clean Air Act Advisory Committee
Clean Air Scientific Advisory Committee
Community Multiscale Air Quality Model
Chemical Speciation Network
Denver Aerosol Sources and Health
Detroit Exposure and Aerosol Research Study
Desert Research Institute
Davis Rotating Uniform size-cut Monitor
Elemental carbon
Environmental Protection Agency
Filter Dynamic Measurement System
Federal Equivalent Method
Federal Reference Method
Group on Earth Observations
Geographic information systems
Health Effects Institute
Interagency Monitoring of Protected Visual Environment
Keyhole markup language
Multi-Ethnic Study of Atherosclerosis
Multi-year Plan
National ambient air quality standards
National Association of Clean Air Agencies
North American Research Strategy for Tropospheric Ozone
National Center for Environmental Research
National Core Monitoring Network
Office of Air Quality Planning and Standards
Organic carbon
Office of Research and Development
Particulate matter
particles generally less than or equal to 2.5 jim in diameter
particles generally less than or equal to 10 micrometers (|im) in diameter
particles generally larger than 2.5 and up to 10 jim in diameter
Primary Quality Assurance Organization
Quality assurance
Scanning electron microscopy
State and local air monitoring stations
December 2008
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STAR Science to Achieve Results
STN Speciation Trends Network
TC Total carbon
TEOM Tapered Element Oscillating Microbalance
TTN Technology Transfer Network
TOR Thermal-optical reflectance
TOT Thermal-optical transmittance
XRF X-ray fluorescence
December 2008 iv
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AUTHORS AND CONTRIBUTORS
AUTHORS
Dr. Dan Costa, National Program Director for Air Research, Office of Research and Development,
US Environmental Protection Agency, Research Triangle Park, NC
Mr. Neil Frank, Office of Air Quality Planning and Standards, Office of Air and Radiation, US
Environmental Protection Agency, Research Triangle Park, NC
Dr. Barbara Glenn, National Center for Environmental Research, Office of Research and
Development, US Environmental Protection Agency, Washington, DC
Mr. Tim Hanley, Office of Air Quality Planning and Standards, Office of Air and Radiation, US
Environmental Protection Agency, Research Triangle Park, NC
Ms. Beth Hassett-Sipple, Office of Air Quality Planning and Standards, Office of Air and Radiation,
US Environmental Protection Agency, Research Triangle Park, NC
Dr. Bryan Hubbell, Office of Air Quality Planning and Standards, Office of Air and Radiation, US
Environmental Protection Agency, Research Triangle Park, NC
Mr. Phil Lorang, Office of Air Quality Planning and Standards, Office of Air and Radiation, US
Environmental Protection Agency, Research Triangle Park, NC
Mr. Nick Mangus, Office of Air Quality Planning and Standards, Office of Air and Radiation, US
Environmental Protection Agency, Research Triangle Park, NC
Dr. Lucas Neas, National Health and Environmental Effects Research Laboratory, Office of
Research and Development, US Environmental Protection Agency, Chapel Hill, NC
Dr. Venkatesh Rao, Office of Air Quality Planning and Standards, Office of Air and Radiation, US
Environmental Protection Agency, Research Triangle Park, NC
Ms. Joann Rice, Office of Air Quality Planning and Standards, Office of Air and Radiation, US
Environmental Protection Agency, Research Triangle Park, NC
Mr. Geoffrey Sunshine, Health Effects Institute, Boston , MA
Mr. Tim Watkins, National Exposure Research Laboratory, Office of Research and Development,
US Environmental Protection Agency, Research Triangle Park, NC
Mr. Lewis Weinstock, Office of Air Quality Planning and Standards, Office of Air and Radiation,
US Environmental Protection Agency, Research Triangle Park, NC
December 2008 v
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CONTRIBUTORS
Dr. Sherri Hunt, National Center for Environmental Research, Office of Research and Development,
US Environmental Protection Agency, Washington, DC
Ms. Sascha Lodge, National Center for Environmental Research, Office of Research and
Development, US Environmental Protection Agency, Washington, DC
Ms. Stacey Katz, National Center for Environmental Research, Office of Research and
Development, US Environmental Protection Agency, Washington, DC
Dr. Marc Pitchford, Office of Air Quality Planning and Standards, Office of Air and Radiation, US
Environmental Protection Agency, Research Triangle Park, NC
Ms. Gail Robarge. National Center for Environmental Research, Office of Research and
Development, US Environmental Protection Agency, Washington, DC
Mr. Rich Scheffe, Office of Air Quality Planning and Standards, Office of Air and Radiation, US
Environmental Protection Agency, Research Triangle Park, NC
Ms. Laurel Schultz, Office of Research and Development, US Environmental Protection Agency,
Research Triangle Park, NC
Dr. William Wilson, National Center for Environmental Assessment, US Environmental Protection
Agency, Research Triangle Park, NC
Dr. Darrell Winner, National Center for Environmental Research, Office of Research and
Development, US Environmental Protection Agency, Washington, DC
December 2008 vi
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INTRODUCTION
Air pollution continues to have adverse impacts on the human and environmental health of
the United States, despite clear evidence that overall air quality has improved. To understand the
relationships between air pollutants and adverse health and welfare effects, researchers utilize
ambient air measurement data collected through monitoring networks operated almost exclusively
by State, local and Tribal air monitoring programs. These networks provide data for characterizing
ambient air concentrations of the criteria air pollutants (particulate matter (PM), ozone, nitrogen
dioxide, sulfur dioxide, lead, and carbon monoxide) as well as toxic air pollutants. However, the
ambient air monitoring networks do not capture data everywhere or every day. Thus, in April 2008,
the Environmental Protection Agency's (EPA's) Office of Research and Development (ORD) and
Office of Air Quality Planning and Standards (OAQPS) within the Office of Air and Radiation
(OAR) co-sponsored a workshop to discuss which modifications to the current ambient air quality
monitoring networks would advance our understanding of the impacts of criteria air pollutant
exposures on public health/welfare in the most meaningful way. In particular, EPA sought advice on
concrete steps that could be taken to prioritize monitoring sites and/or specific fine particle
components for more frequent monitoring in order to improve our understanding of the impact of
these components on public health1.
The purpose of this document is to present a summary of the April 2008 workshop and the
recommendations emanating from the workshop discussions. This document is not intended to be a
commitment to actually implement the recommendations but rather it will serve as a tool to consider
and prioritize near- and long-term actions for EPA and others to undertake. To the extent that these
recommendations can be incorporated into routine monitoring networks, they will help expedite
research, better inform future reviews of the national ambient air quality standards (NAAQS), and,
ultimately, reduce air pollutant exposures that are associated with adverse health and welfare
effects.2
1 Research specific to the protection of public health remains a top priority and EPA has targeted PM as a high-risk
pollutant. In the last PM NAAQS review, EPA focused on particle mass and primarily distinguished between two
categories of particle pollution based on size (i.e., fine- and coarse-fraction particles), and conducted parallel evaluations
of the available scientific evidence relating to each category. The importance of specific PM components and sources
was evaluated within the context of this basic size differentiation. In the current PM NAAQS review, EPA is
considering the extent to which new information has become available to assess and determine how particle pollution is
defined. Specific characteristics to consider will include particle size/mass, composition, and sources/environments
(e.g., urban and rural areas). This information will inform decisions related to whether sufficient evidence exists to
warrant consideration of alternative indicators for PM, and, if appropriate, the development of new NAAQS. See
http://www.epa.gov/ttn/naaqs/standards/pm/data/2008 03 final integrated review_plan.pdf for more information.
2 In a related effort, EPA recently issued the Clean Air Research Multi-Year Plan 2008-2012 which describes the
objectives of leading-edge research to support regulatory decision-making. This plan outlines research that will provide
critical information to add to the existing scientific foundation to inform the reviews of the NAAQS; develop regulations
and advanced tools and models to implement the NAAQS; and improve methods to track progress in achieving health
and environmental improvements. (See http://www.epa.gov/ord/npd/pdfs/Air-MYP-narrative-final.pdf) The multi-year
plan builds upon recommendations from EPA's OAR as well as several scientific advisory boards. See also National
Academy of Sciences (NAS) National Research Council (NRC): Research Priorities for Airborne Paniculate Matter at
http://books.nap.edu/catalog.php?record_id=10957; Board of Scientific Counselors (BOSC) Report on the PM-Ozone
Program Review: April 2005 at http://www.epa.gov/osp/bosc/pdf/pm0508rpt.pdf; and Clean Air Act Advisory
Committee (CAAAC) Report on Air Quality Management in the United States at
http://www.nap.edu/catalog/10728.html.
December 2008 1
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Background
The EPA is interested in having an open and continuing dialogue with representative experts
regarding health research priorities for ambient air quality monitoring data that would best advance
our understanding of the impacts of air pollutant exposures on public health. This dialogue was
significantly advanced at an initial meeting co-sponsored by the Health Effects Institute (HEI) and
EPA in late 2006. At that meeting, the primary focus was to discuss how the use of the
accumulating data derived from nationwide monitoring of fine particulate matter (PM2.5)
components could facilitate current and future health effects studies and improve comparisons of
risk estimates across studies. The workshop illuminated issues associated with accessing and
analyzing monitoring data and identified needs of the health effects research community regarding
monitoring of fine particle components.3
In April 2008, EPA's ORD and OAQPS co-sponsored a follow-up workshop bringing
together approximately 80 EPA and external air quality, monitoring, exposure, and health scientists4.
As briefly described above, the major goal of the workshop was to discuss recommendations for
modifications to the current ambient air quality monitoring that would advance our understanding of
the impacts of criteria air pollutant exposures on public health/welfare in the most meaningful way,
specifically for understanding the impact of PM and PM2.5 components. In addition, the workshop
was designed to continue to facilitate communication and scientific interactions across disciplines
(e.g., epidemiology, toxicology, atmospheric science, monitoring, risk/exposure assessment) to
improve the availability and interpretation of air quality monitoring data for air pollution health
studies dependent upon the national networks.
The impetus for these two meetings was the growing recognition that current and future
changes to the air quality monitoring system could significantly affect ongoing and future
epidemiology research. This research serves as a foundation for EPA's reviews of the NAAQS. Yet
resources at the Federal and State/1 ocal/Tribal levels for air pollution monitoring continue to
diminish, while increasing demands (such as potential expansion of the lead ambient air monitoring
network) tied to various aspects of NAAQS compliance continue to grow. Prominent health
researchers have increasingly questioned EPA's commitment to health research in planning its
monitoring programs, while the State and local monitoring experts who design and operate the
monitoring programs wonder why some current data resources are not fully or properly accessed to
explore opportunities to address some of the questions that are important to the health researchers
with existing data sets. Clearly, improving the understanding of the objectives/mandates of each of
these communities and fostering collaborative efforts between these communities is critical to
moving forward in a positive manner.
Workshop Structure, Objectives, and Outline of Session Summaries
The April 2008 workshop began with introductory remarks by EPA's National Program
Director for Air Research, Dr. Dan Costa, and by senior OAQPS managers, Ms. Lydia Wegman and
Mr. Richard (Chet) Wayland, expressing their support for this effort. Dr. Morton Lippmann from
New York University highlighted successful research that has benefited from collaboration among
air quality experts and health researchers. Thoughtful discussions centered around five specific
panel sessions, each facilitated by two co-chairs (one EPA and one non-EPA representative),
addressing the following topic areas:
3 See http://www.healtheffects.org/AQDNov06/AQDWorkshop.html for more information.
4 The workshop agenda and list of participants is included in Appendix A.
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• Session I: Elemental and Organic Carbon Measurements
• Session II: Accessing Ambient Air Monitoring Data
• Session III: Ambient Air Monitoring for Health Research
• Session IV: Thoracic Coarse Particle Components and Potential Public Health
Impacts
• Session V: Ambient Air Monitoring Realities - EPA/State/Local Perspectives
The primary workshop objectives included:
• To reexamine and assess progress to date on key issues identified at the earlier HEI/
EPA-sponsored workshop and in follow-up conference calls with the PM Research
Center Directors, HEI National Particle Component Toxicity (NPACT) Principal
Investigators, and other researchers.
• To discuss specific recommendations for concrete steps that EPA and other organizations
could take in the ambient air monitoring program to advance health research for the
criteria air pollutants.
• To seek constructive feedback on five draft "white papers"5 developed to aid in a
common understanding of the issues under discussion. These draft white papers are
included in Appendices B through F of this draft document.
The summaries of the five workshop sessions presented below include:
• Session overview,
• Panel members,
• Major points identified by the workshop participants, and
• Recommendations
o Presented at the workshop and/or
o Developed by EPA staff based upon the workshop discussions
Initial Successes and Next Steps
This workshop was a major step in a series of interactions to foster improved long-term
communication between air quality experts and health researchers. The air program has continually
emphasized integration across disciplines, labs and EPA programs. Although this requires
significant investment of time and effort for all involved, we believe such an investment is necessary
to ensure that the ambient air monitoring program offers, and health researchers use, the best and
most appropriate data possible to support the health research that serves as a foundation for EPA's
NAAQS reviews. While follow-up from this workshop continues, there are already demonstrable
outcomes from the efforts involved in planning, as well as holding the April workshop. A few
examples of initial successes include:
• Monitoring staff, in EPA and State/local agencies, are becoming aware of the need for
daily measurements of PM components and have expressed willingness to save filters
used for daily measurements of PM2.s mass so that they can be used to analyze PM
components for health researchers.
5 The draft white papers presented relevant background information and critical issues, opportunities for improvement
including draft EPA recommendations for possible short- and long-term activities, and, as appropriate, charge questions
to stimulate discussion at the meeting.
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• Science to Achieve Results (STAR) grantees voluntarily analyzed data sets to help
determine whether changes to the measurement method for organic carbon would affect
the findings of ongoing epidemiological studies.
• Based on information they received from air quality experts at the workshop, STAR
grantees have acknowledged making changes to the designs of their health studies that
will improve validity of results as a result of better information on spatial variability of
PM components.
• OAQPS monitoring experts and ORD epidemiologists and exposure scientists are
working together to examine the covariance of specific PM components (e.g. metals)
across various cities to develop a network of sites for refined epidemiological study.
• ORD's National Center for Environmental Research has conducted a preliminary survey
of EPA-funded health researchers to identify specific ambient monitoring sites that are
being used in current epidemiological studies6. This information will be useful to State
and local air agencies as they consider any future changes to their monitoring networks.
A draft of this document was distributed to the workshop participants for review and
comment. This final summary document incorporates the comments that were received. In
addition, EPA has identified additional next steps that will be taken, including:
• Stratifying the recommendations in this document as to their feasibility and prioritizing
actions to be taken. These include providing important input into the strategy for the
revised monitoring network (NCore7). EPA's ORD and OAQPS will work together to
facilitate the incorporation of these recommendations into both short- and long-term
monitoring strategies and leveraged program plans. This will include EPA staff briefing
and receiving direction from senior management during planning cycles for both
monitoring network design and health research planning.
• Implementing data access improvements and communication tools as soon as possible to
prevent disruption of data streams, loss of important monitoring sites, and developing a
clearinghouse for other datasets not readily formatted to the Air Quality System (AQS)
data system.
• Requesting periodic consultations on the enhanced monitoring program with the Clean
Air Scientific Advisory Committee (CASAC) and its Ambient Air Monitoring and
Methods (AAMM) Subcommittee.
6 See Appendix G.
7 See http://www.epa.gov/ttn/amtic/ncore/index.html for more information on the NCore Multipollutant Monitoring
Network.
December 2008
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SESSION I: ELEMENTAL AND ORGANIC CARBON
MEASUREMENTS
Background/Objectives
EPA has made changes in the urban Chemical Speciation Network (CSN) carbon sampling
and analytical protocols for measurement of particulate carbon in order to address inconsistencies
between the procedures previously used in that program and the rural Interagency Monitoring of
Protected Visual Environments (IMPROVE) program. Health researchers have expressed concerns
about this methodological change and potential impacts on monitoring data that are used for long-
term time-series analyses. The session objectives were to:
• find ways to minimize disruption to epidemiological studies, both current and future,
• determine the extent of measurement change/error that is problematic,
• determine which of the CSN changes are of most concern to epidemiological studies,
• assess whether past and planned measurement comparisons are adequate, and
• consider how blank filters and sampling artifacts should be handled.
Session Overview
This session contained two components: (1) a series of factual presentations on the
measurement methods, the changes and/or errors that may occur, and the importance of
carbonaceous aerosol sampling to the health research community and (2) a panel discussion to
address approaches that can be used to assess potential types of measurement error, needed
measurement comparisons, impacts on epidemiological studies, and additional steps necessary to
identify and address information gaps.
The presentations included:
• Overview and Introduction to Key Issues—Venkatesh Rao (EPA) and Barbara Turpin
(Rutgers University)
• A Health Researcher's Perspective: What's so Special About Carbon?—Ed Avol
(University of Southern California)
• CSN Carbon Monitoring Changes and Issues—Joann Rice (EPA)
• Carbonaceous Aerosol Sampling Artifacts in the National Monitoring Networks—John
Watson (Desert Research Institute)
• Transitions: Relating "Old" to "New" Methods—Warren White (University of
California-Davis)
• Predicting Carbonaceous Species Concentrations with Partial Least Squares—Philip
Hopke (Clarkson University)
• Impact of Method Transitions to Health Research—Michael Hannigan (University of
Col orado-B oul der)
• Air Quality Monitoring: Perspectives from East and West—Dirk Felton (NY Dept. of
Environmental Conservation)
Members of the discussion panel included:
• Venkatesh Rao, co-chair, EPA, OAQPS
• Barbara Turpin, co-chair, Rutgers University
• Ed Avol, University of Southern California
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• Michelle Bell, Yale University
• Judith Chow, Desert Research Institute
• Neil Frank, EPA, OAQPS
• Philip Hopke, Clarkson University
• Michael Kleeman, University of California-Davis
• Allen Robinson, Carnegie Mellon University
• Warren White, University of California-Davis
This session provided a good forum for the exchange of information among diverse interests
in monitoring, analysis, and health impacts. The panelists and those who spoke up from the floor
shared the view that the impact of measurement changes on health research is unknown and
complex, as is how much detail is needed to better understand the impact on epidemiological studies
and resulting correlations with health effects. There was also agreement among those speaking
about a need to: (1) collect more information from collocated measurements for an extended period
of time at multiple sites; in order to (2) better document uncertainty, and associated differences
between elemental carbon (EC) and organic carbon (OC) measurements for the new and old
methods. Spatial location and source dependent differences in monitoring sites are important
considerations, as are potential differences by season. Specific action items were not identified at
this session, but there was a general consensus amongst the speakers about the proposal (see below)
for more extensive co-located monitoring, and several researchers called for such analyses in a wide
range of locations and seasons. Little discussion took place of other related issues that had
previously been identified in the agenda (this included how to handle artifacts, how to handle field
blanks vs. back-up filter measurements, and which measurement errors are most problematic for
epidemiological studies). Additional background information is provided in Appendix B.
Major Points Raised by Participants
General
• Health endpoints are affected by the physical, chemical and toxicological properties and
attributes of carbon-containing particles as well as by the emission sources represented by the
air quality measurements. These characteristics are not routinely measured, and currently
can only be inferred—very indirectly—from the relative proportions of EC and OC. The
reported proportions of EC and OC are likely to change as a result of the changes in the
measurement protocol; this could be more important in assessing the impact on health than
individual uncertainties in the OC and EC measurements.
• Changes made in the CSN carbon measurements can be documented, but sampling artifacts
(organic vapors adsorbed within the filter) are still being explored. Charring of these vapors
within the filter is the main cause of differences between transmittance and reflectance
corrections for OC charring (Chow et al., 2004 Relating the results of "old" and "new"
methods is ongoing. The co-location of old/new monitors can provide information important
to understanding the nature of changes in measurements.
• In the past, EPA used several samplers in their chemical speciation network, which resulted
in good agreement for EC, but poor agreement for OC and total carbon (TC) - which was a
function of the differing sampling flow rates and having no good way to address the
sampling artifacts. In the future, one sampler that is IMPROVE-like will be used for CSN
with a higher flow rate, smaller filter, back-up quartz filters, and better field blanks (this will
December 2008 6
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likely ensure uniformity of the OC measurements, but more work will likely be needed to
understand what is actually being measured).
• The change in the analytical protocol will change the OC-EC split in some cases, yielding
more reported EC and less reported OC. The analytical change is not expected to change
reported TC. The changes in sampling protocol will affect the sampling artifact, and thereby
reduce reported TC and OC. The sampling change is not expected to change reported EC.
• Hannigan et al. [2008] suggest that in the DASH (Denver, CO) study, EC would remain
significant as an indicator for health endpoints. Of course, Denver is only one location in
space, and generalizations cannot be made about the health effects of EC until more studies
are completed.
• For considering impacts of PM on health, measures of bias and of uncertainty are both
important in determining health outcomes.
• There is a need for semi-continuous OC/EC measurements as well as other PM chemical
components. As important as the increased temporal resolution that will result from use of
semi-continuous methods is the affordability of these measurements (daily (and shorter
interval) sampling at multiple sites becomes more feasible). This can be important for better
understanding sources of carbon since more detailed spatial and temporal information can aid
in source attribution studies. The shorter sampling times associated with semi-continuous
instruments also produce samples with much smaller variation in sampling conditions (good
in terms of minimizing the potential for redistribution between the gas and particle phases).
• In addition, there currently are sites with Sunset semi-continuous OC/EC analyzers, McGee
aethalometers, and Thermo-Scientific MAAPs operating through the transition period and
beyond. Some researchers suggested that the locations of these analyzers at sites important
for epidemiologic studies (cities where health effects have been shown) and at other
important sites should be identified so that the usefulness of these data for epidemiological
studies and for "harmonizing" efforts can be identified. However, the Sunset Labs
instrument collects material at a somewhat higher than ambient temperature, like the TEOM
does. Thus, Sunset Labs' OC values may be somewhat smaller than those made at ambient
temperature. Others noted that health research should not be limited to the locations of on-
going studies due to the many studies that are national in design and the need to understand
regional variation in health effects.
• One presenter indicated that the sub-fraction measurements of OC and EC made in the
IMPROVE and new CSN protocols can also add valuable information to source attribution
studies. However, low temperature (i.e., volatile) OC1 and OC2 fractions are most sensitive
to temperature changes during the analysis and during sample handling (Chow et al, 2007,
Dillner et al., 2008).
• The type of measurement error that is most important to a specific epidemiological study will
depend on the study design, and on the scientific questions it aims to address (e.g., acute
versus chronic effects). In other words, there is no single factor or set of factors that is the
critical need for epidemiological researchers. The impact of measurement changes will
December 2008
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affect various epidemiological studies differently, depending on their design. This issue
needs to be sorted out and discussed further.
What types of measurement error are problematic for epidemiologic studies?
• All types of measurement errors are of potential concern. There is a need for a qualitative
understanding of what the measurement error is comprised of, and how this might vary
spatially. The analytical measurement error of the monitor is usually much smaller than the
measurement error associated with using a centrally located monitor to represent an entire
community, particularly when evaluating pollutants that are measured once every three or six
days and in assessing pollutants that are heterogeneously distributed in the ambient air.
Measurement error clearly matters when evaluating epidemiological evidence, but for time-
series analyses, most of measurement error is Berkson, and hence does not include bias, just
reduced power. The way to solve that problem is with more data, which again points to the
need for daily measurements.
• Changes in carbon components (bulk OC and EC) are important, as are changes in the
percent measurement error of the components. Are there differential errors in EC and OC?
We need to define "error" better.
• Representativeness of sites is affected by spatial variability and by source types.
• Balance can be attained by a central PM2.5 speciation monitor plus satellite sites (with lower
costs) that indicate how representative the information collected at the central site is (and
address how spatially divergent some EC/OC and other components are). Satellite sites must
be based on lower-cost methodologies, for example, should optical measurements from
Teflon filters be considered? Note: panelists did not address how we can be certain that
relationships between "reference" and "cheap" methods observed at central sites will be the
same at satellite sites.
Are past and planned measurement comparisons adequate?
• In evaluating the transition between carbon monitoring methods, workshop participants
voiced concerns that the current process to conduct two months of measurement comparisons
is too limiting, since seasonal variability is important; there is a need for more data to
compare the different methods and inform the epidemiological research. There are 6 co-
located CSN/IMPROVE sites ongoing (as identified in the draft White Paper for Session I,
see Appendix B) and more discussion of how these could be used would be helpful. It is
unlikely that we will be able to afford one year's worth of co-location at every site converted.
So, it's important to specifically know where longer-term comparisons are required for health
research or to know how to generalize from a small set of locations to the larger network.
(see next bullet as well).
• It might be useful to consider a whole year of data with less frequent measurements; also
consideration of a limited number of samples at more individual sites would be helpful. As
such, there is a need to know what kinds of locations are of most importance for health
research studies. Some suggested that a wide range of locations representing different
particle mixtures and sources would be useful.
December 2008
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• There is a need to better understand uncertainty information. Proportional changes in EC &
OC may create proportional changes in epidemiological studies; if changes are not
proportional for different components, then there may be a problem.
• In selecting sites for possible increased co-location of old and new measurements, the
influence of variations in sources needs to be considered. These could include sites
influenced by wood-smoke, mobile sources, large stationary sources, and a mix of all
sources.
Recommendations/Actions for Consideration
Suggested by Workshop Participants:
• There was general consensus on a proposal to run co-located "old" and "new" CSN
measurements for a whole year; with consideration of potentially measuring every 6th (or
12th) day at 6 - 9 sites. Many researchers felt it was important to ensure that the specific sites
include those currently being used for epidemiological studies where associations or
relationships with OC or EC are being studied, although many other researchers noted the
need for selection of sites that represent a wide array of locations and particle mixtures, as
noted below. Inclusion of locations where carbon has been found to be associated with
health effects may allow us to more easily determine if the measurement change has an
impact on their health impact findings. Potential sites include Seattle (Beacon Hill),
Rubidoux (CA), Bronx (New York City), Atlanta, Detroit, Cleveland, Denver, and an
additional southern location.
• Several individuals supported selecting sites based on source distribution/mix, not just on-
going or planned epidemiological studies. Using this approach, consideration could be given
to variations in soils, industries, and mobile sources in the source distribution. A site
selection process could incorporate both this concept and that listed above, to include
locations of ongoing studies.
• There was also general consensus that this proposal be reassessed after a year of data
collection in order to evaluate whether a longer study period or more sites are needed.
• A limited number of individuals also supported an investigation of the availability of semi-
continuous OC/EC data and its potential role in "harmonizing" old vs. new carbon
measurements.
• A limited number or participants also supported achieving better spatial resolution by
implementing a main site and satellite sites for species with large spatial variability, to better
characterize population exposures.
Developed by EPA Staff Based on Workshop Discussions:
• Continue UC-Davis (January 2008) "Carbon Summit" process:
o Selected archived samples from IMPROVE and collocated IMPROVE/CSN network
sites will be analyzed by the new IMPROVE carbon analysis methodology to develop
December 2008 9
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additional data, which will be used along with the routine data collected by the two
networks to better characterize the relationships between the various old carbon
analysis methods and the new IMPROVE analytical method (i.e. the approach that is
used now for both networks).
o Changes will be made in the operations of both IMPROVE and CSN networks to
increase the number and utility of carbon blanks and backup filters collected in order
to further investigate and if possible develop more credible approaches to adjust for
sampling artifacts.
o Continue to conduct periodic assessments of the quality and comparability of PM
carbon data from both networks and over time (i.e. as methods have changed) paying
particular attention to data from continued operations of collocated monitoring sites.
The results of these efforts to be the subject of possible future joint network
workshops, publications and web postings.
Further evaluate the performance of the Sunset EC/OC and other continuous analyzer and
determine the role of continuous measurements to support daily monitoring and harmonize
old and new CSN. Filter-based measurements will still be needed, but can be collected on a
much reduced frequency. This idea can then be extended to other semi-continuous
instruments, like the Aethelometer, MAAPs, and photoacoustic spectrometer which measure
light absorption, strongly correlated with EC.
Make a more targeted request (with much more specificity than was done for this workshop)
to health researchers to help us better understand the sensitivity of their results to changes in
carbon measurements.
Further discussion of the topic "optimal OC artifact correction for large networks" is
warranted.
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SESSION II: ACCESSING AMBIENT AIR MONITORING DATA
Background/Objectives
EPA's Air Quality System (AQS) is designed to collect and store ambient air monitoring
information. EPA recently introduced the AQS Data Mart to facilitate access to this monitoring
information. The AQS Data Mart is a generic "retrieval" tool that provides the ability to query any
information, but it does not provide significant data exploration or analytic capabilities. These
capabilities are left to the "analytical" tools. Various analytical tools, or interfaces, are available
including the Health Effects Institute's (HEI's) Air Quality Database, which focuses on ambient air
measurements of PM2.5 components and gaseous pollutants at and near STN and SLAMS sites.
This session focused on data access issues and how to help health researchers obtain monitoring data
for fine particle components and other critical pollutants more easily.
Session Overview
In general, access to ambient air monitoring data to support health research/ assessments falls
into four general categories:
• epidemiological studies,
• exposure/risk assessments,
• public health surveillance, and
• health impact assessments
Keeping these broad categories in mind, and understanding that the goal is to provide a
framework for delivering consistent, well-documented monitoring data to users including the health
research community, the broad topics discussed in this session included:
• To what key data do health researchers need access?
• What formats are most useful?
• What kind of access is most appropriate for health research uses?
• How can the overall data context be improved and preserved in delivering the data to users?
Members of the discussion panel included:
• Michelle Bell, co-chair, Yale University
• Bryan Hubbell, co-chair, EPA, OAQPS
• Sara Adar, University of Washington
• Kaz Ito, New York University
• John Langstaff, EPA, OAQPS, Health and Environmental Impacts Division
• Nick Mangus, EPA, OAQPS, Outreach and Information Division
• Richard Poirot, Vermont Department of Environmental Conservation
• Betty Pun, AER
• Rich Scheffe, EPA, OAQPS
The panel members were asked to be specific in their data needs, e.g., to clearly identify what
specific documentation is needed, rather than just providing general recommendations to provide
"more documentation." A special focus on versioning of the data was also recommended for the
discussion.
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To lead off the session, two introductory presentations were provided by Nick Mangus and
Rich Scheffe of EPA. Nick Mangus provided an overview of the draft white paper entitled "Access
to EPA's Air Quality Data for Health Researchers (see Appendix C)," while Rich Scheffe provided a
summary of the outcomes from a recent EPA-sponsored Air Quality Data Summit, held in March
2008.8
Major Points Raised by Participants
Several persons mentioned the high value of EPA air pollution datasets, and noted that access
has greatly improved over the years. Several broad themes emerged from the discussion:
Versioning
The most important theme seemed to be versioning of the EPA datasets. Several people
expressed concerns over the current system, where researchers can download different datasets
without realizing they differ. Currently, there is no way of knowing whether data files have changed,
or merely been updated. This causes problems with reproducible research and with researchers not
knowing whether to get new data due to corrections being made. It also is an issue when researchers
update an epidemiological analysis with a new year of data; they need to know if there have been
changes in the air quality database for previous years as well. It was noted that changes to data
include not only updates of new data, but corrections to old data, even 10 years previous. Multiple
options to address this issue were discussed:
• time/date stamp every observation
• version numbering (e.g., Version 10Jan08.7, etc.)
• maintenance of snapshot datasets that are static
• notice of changes to datasets
• maintenance of datasets used by researchers
Concerns were raised regarding the cost and storage of some of these options.
Data Availability
The draft white paper included in Appendix C provided background information for this
session. This paper described and contrasted the data available through the AQS Data Mart and the
HEI Air Quality Data Base, and other sources of information. Specific issues discussed related to
data availability included:
• EPA vs. other organizations' roles in providing access to data. Several participants
expressed support for HEI or some other organization outside the EPA continuing to support
analytical tools, or interfaces, to disseminate air quality data "packaged" with other relevant
data for health researchers.
• Information previously available through AQS that is no longer available. There were some
concerns expressed that certain types of data, e.g. the TEOM data had been removed from the
AQS archive website. EPA clarified that TEOM and FRM data were different and that the
TEOM data were removed so unknowing users would not incorrectly compare it to FRM
data. EPA has mitigated this issue by changing how we identify PM2.s data and will
8 See http://wiki.esipfed.org/index.php/Air Quality Data Summit#Documents.
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reconsider putting the TEOM data back on the website. It was noted that some local TEOM
data are being adjusted to match FRM data as part of the joint CDC/EPA PHASE project.
• Additional information for monitor locations. It was suggested that photographs of the
monitoring station be easily accessed and keyhole markup language (KML) files be created.
While the land-use around monitors could change, this would provide researchers with some
guidance on the monitor location.
• Information from other sources. The concept of a data clearinghouse providing links to non-
EPA data sets was suggested, for example, access to additional State, local, and tribal air
quality data that are not entered into AQS (e.g., AirNowTech data), special studies data,
Supersites data, etc. EPA clarified that the Supersites data are available on the NARSTO
website. However, workshop participants made it clear that the data on the NARSTO site is
not in a format that is comparable to the FRM data available on AQS. Workshop participants
noted that it would be helpful to have all the air quality data in one location, and provided in
a similar format or have scripts for reading data from different formats into common software
programs like SAS, R, or S-Plus. The need to leverage existing IT capabilities to gain
efficiencies in creating a data clearinghouse was highlighted. Please see discussion of this
issue below in the section Actions Items Under Consideration.
• Requesting data from State, local tribal air agencies directly. EPA maintains a listing of
regional, State, and local level air quality data contacts available on its website. Workshop
participants noted that when going to State contacts for data, some provide data faster than
others, and often in very different formats (sometimes still providing paper copies only). It
was suggested that it would help State/local air agencies if health researchers would share
final products (e.g., health studies) with these agencies so they could be better informed
about how the air quality data are used and future research needs.
Data Quality
Some mention was made of data quality, and the need for researchers to be aware of data
quality problems, even in EPA-vetted datasets. Examples provided included:
• Quality and accurateness of existing metadata are varied, metadata can be wrong, and that
there are often not even flags to note suspicious outlier values that might result from a
misplaced decimal. For example, if ozone data at low levels are a decimal place off, this
could play a major role in studies of thresholds and effects at low concentrations.
• Incorrect identification of latitude/longitude has been noted for a subset of monitor sites as
well as a high frequency of missing data in some of the critical fields (e.g. no monitoring
objective listed for some sites). EPA notes that all data in AQS passes the EPA's QA checks
for the tests we need for regulatory purposes. This may not be sufficient for some scientific
applications. All metadata (and data) are ultimately controlled by the submitting
State/local/tribal agency and EPA cannot change it. So improving quality is a community
effort. This is another reason why researchers might want to engage the agencies whose
monitors they are using for studies.
• It was recommended that the data be stored at the finest spatial and temporal granularity
possible, and then aggregated up as needed by the user.
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User Friendliness and Documentation
It was noted that some data users are novices, some are expert, and each needs different
levels of data access and, therefore, would have different needs. Many participants voiced the
opinion that epidemiologists tend to be "very opportunistic," making use of whatever air quality data
are provided. As such, epidemiologists will utilize available data, but would like more guidance as
to what uses of the data are valid and/or appropriate (e.g. provision of data on the percent valid
observations, monitoring objectives, monitor scale, use of flagged data).
The need to increase user friendliness was discussed, such as with the ability to download
specific States' data rather than data for the whole US for some data sources. Within the discussion
of user friendliness, there was a general consensus that documentation needs to be improved. In
addition, documentation is perhaps a bit scattered throughout various EPA sites. The point was made
that "user-friendliness" is dependent on the needs and skill level of the user, so that no standard form
will be perfect for all users.
The issue is that it takes more effort to find and interpret documentation of the data than it
does to obtain the data; and missing descriptive information can be devastating to an analyst. There
are many attributes of the data that are not well understood by users (metadata is not owned by EPA
and therefore can be outdated or data can be updated by the owner at any time; EPA data storage
labels are in flux with regulation and policy changes; etc.). Users having a single site for
documentation will greatly reduce the likelihood of incorrect interpretation.
Secondary Data
The panel discussed whether non-air pollution data would be useful to include in the air
pollution datasets. Examples were weather, land-use, and census data. Some people thought this
would be useful. Others thought this was less useful, given that such data are available elsewhere.
This seemed to be far less of an issue than the versioning. Thus, resources might better be spent on
the versioning issue than on incorporating secondary data.
Recommendations/Action Items for Consideration
The recommendations listed below include items discussed at the workshop as well as
additional recommendations developed by EPA staff based upon the workshop discussions.
Data Versioning
• Explore options for adding the date (and time) of last modification to all data measurements
in AQS (and the AQS Data Mart).
Notes: Data in AQS can change at any time. Generally, EPA limits the ability of data
submitters to change data for only the last few (3-5) years. However, we do open time windows
where older data can be changed. There are implications to the submitter for changing data and the
volume of older data changes is low, but they do happen. Researchers need to know if these data
changes affect their analyses. Adding a date-of-change to each measurement would allow a data
user to query the space and time domain of their initial query to see if any data had changed. EPA
would not keep a record of the change, but new or changed values would be indicated with their date
of creation/update. It would still be incumbent on the user to maintain the original data set and
compare changed data to assess possible ramifications. Any change to a measured value or metadata
(like the monitoring method or uncertainty) would trigger the date stamp to be updated.
December 2008 14
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Data Availability
• Continue to encourage HEI and other organizations support of analytical tools and interfaces
to make the AQS data more useable for the health research community. Follow-up with HEI
regarding the plans for continuing to support the HEI Air Quality Data Base.
• Explore options for creating and maintaining a Clearinghouse of non-EPA datasets
There are a lot of air quality data which are not in AQS. Some of these data have been
collected by State and local air pollution control agencies and have not been reported to AQS for one
reason or another (e.g., continuous data are collected on a minute basis, and EPA only requires that
hourly averages be submitted; the information does not pass the regulatory quality assurance
requirements, even though the quality may be high enough to fill a gap in a research time series; etc.)
Another source of data that EPA does not have is data collected from special research studies run by
academic or State/local public health organizations. This is generally high density data (in space
and/or time) used to better understand the variability of measurements over smaller scales than
regulatory monitoring requires. Even the other Federal air quality storehouse, AirNow Gateway will
have State/local/tribal data that does not meet the policy requirements of AQS. Having a single
location as the starting point for a dataset clearinghouse will dramatically help improve the inventory
of available air quality information.
• Location of Monitoring Sites. Make keyhole markup language (KML) files with AQS air
monitoring site locations should be made available.
KML files describe locations and can be used by most modern map-drawing applications.
Including links within the KML file will allow users to download the actual measurements. Making
these available would better allow people to visualize the monitoring network they would also
confirm the monitor location when the listed latitude and longitude were suspect.
• Develop new content and format for data on AQS archive page. Request feedback from key
users on how to improve the AQS "Data Archive" (data download) page on EPA's website
and update accordingly.
Users complain about the limited number of parameters available, the frequency of updates,
the format, and the size of the files. If agreement can be reached on how to improve these problems,
EPA could make the appropriate changes.
• Continue Data Summit follow-up to provide a system-of-systems for data integration and
display. OAQPS will provide base data from Federal monitoring networks via the AQS Data
Mart.
The purpose of the data summit work is to make use of "interoperability" frameworks like
GEO (Group on Earth Observations) for system developers to identify what part of the data value-
chain they belong in and successfully connect with those up- and down-stream from them. The
value-chain has roughly these divisions: base data provision, metadata provision, data integration,
data processing (aggregation), data visualization, and communication. It is expected that the
architecture standards board convened as a follow up to the Air Quality Data Summit will adopt
specific recommendations on web services (machine queries) of data that all participating systems
should support. If all systems agree to the web services, data integrators and interface builders
would have a much easier time obtaining data from multiple systems. For example, the HEI Air
December 2008 15
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Quality Database could be updated more frequently, and include more information about the
progeny of the data.
• Explore opportunities to encourage discussions between health researchers and State/local
monitoring experts. For example, encourage health researchers to share published results
with State/local monitoring community, specifically studies that use monitoring data
collected by the State/local air agencies and encourage participation of a variety of experts in
regional and national meetings (e.g., health researcher participation in monitoring meetings).
User Friendliness and Documentation
• Explore ways to highlight if a significant change in AQS has occurred. For example, EPA
could provide an explanation on the AQS website so that regular AQS data users would be
aware and know where to go to access the data.
• Use the IMPROVE metadata as a model for developing AQS metadata, including the
IMPROVE "Data Advisories" noting any changes or issues with the data.
• Request that supplemental data be presented in the same format as primary data.
December 2008 16
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SESSION III: AMBIENT AIR MONITORING FOR HEALTH
RESEARCH
Background/Objectives
The purpose of Session III was to provide a discussion among a panel of health and exposure
researchers who use ambient air monitoring data in their studies and to highlight the value of these
data for the continued progress of research linking particulate matter (PM) sources, exposure and
health effects. This session was intended to stimulate a discussion of the issues and identify creative
solutions for the provision of daily speciation data in key locations while working within current
resource constraints faced by local, State and Federal air quality agencies. For example, resolving
key policy questions about health effects of specific size fractions, components and gaseous co-
pollutants related to PM, requires more intensive temporal and spatial air quality data than are
currently available. Background information for this session is included in Appendix D.
Session Overview
The panelists discussed the major sources of uncertainty that must be considered when
designing and interpreting the results of studies of ambient PM mass, components and health. A
number of important research questions were raised in this context that could be addressed if some
changes were made to monitoring networks in some locations. If daily measurements of PM species
were available in informative locations, studies would be better designed to detect the relative
importance/toxicity of PM species and size classes. Studies designed to detect health effects of short-
term exposure would develop more precise and valid exposure estimates and would be better able to
generate hypotheses that address possible mechanisms for the observed health responses to PM.
Although the panelists primarily focused on issues as they relate to epidemiology studies, the point
was made that toxicological assessment of ambient particles must be generalizable to human
exposure and be interpretable in relation to epidemiological results.
Members of the discussion panel included:
• Barbara Glenn, co-chair, National Center for Environmental Research, EPA
• Joel Schwartz, co-chair, Harvard School of Public Health
• John Godleski, Harvard School of Public Health
• Patrick Kinney, Columbia University
• Lucas Neas, National Health and Environmental Effects Research Lab, EPA
• Roger Peng, Johns Hopkins University
• George Thurston, New York University
• Jay Turner, Washington University in St. Louis
Major Points Raised by Participants
Uncertainties
Epidemiology studies must deal with several sources of error in estimating personal exposure
to ambient PM. These include:
• Instrument measurement error. Is the monitoring technique adequately measuring pollutants
at the site of the monitor? The observation was made that while exposure measurement error
December 2008 17
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at the monitor introduces uncertainty in estimates of ambient concentrations, this error is
smaller than other measurement errors in epidemiology studies.
• Spatial variability. How geographically homogenous are the particle concentrations in the
cities where we are conducting epidemiology studies? Is the concentration of PM species
measured at the monitor representative of the ambient concentrations experienced by human
populations living and working in the city?
• Temporal variability. Monitoring schedules of every third or sixth day results in data gaps
that severely limit the ability to explore variations in the time lag of response for different
PM components. Different PM components are believed to target different biologic pathways
in generating health responses and the flexibility to explore a variety of time lags between
exposure and outcome is necessary to reveal these effects. In addition, health events relevant
to days with missing air quality data must be excluded, thus reducing sample size.
Alternatively, air quality concentrations may be interpolated in some manner which increases
uncertainty in exposure estimates. Interpolation results in a reduction of daily variability in
PM concentration data with resulting loss of statistical power, and increases the correlation
between concentration estimates for PM components in datasets. This inhibits efforts to
differentiate species-specific toxicity.
• Other errors in estimation of personal exposure based on ambient measurements. Infiltration
of PM components indoors, time-location during the day etc. The comment was made that
use of air conditioning during the summer months has a large effect on health models for
certain geographic locations.
Time scale
Research has shown that the health effects of air pollution exposure on one particular day are
spread out over several subsequent days. Therefore, the effects (e.g., death) observed on one day are
the result of air pollution that occurred during a period of days on and before the deaths were
recorded. Epidemiologists need to use pollution concentrations on a defined number of days before
the date of an effect to study hypotheses regarding the relevant time between exposure and effect.
These time scales likely are different for different PM components and different health outcomes
(e.g., heart attack, asthma etc). The relevant time lags for a particular outcome also may vary by
season and location as well. The study of time lags informs mode of action.
In the absence of daily speciation data, PM components with a very acute or immediate effect
(short-lags) on health would have less measurement error and thus an apparently stronger association
with health outcomes than PM components with more delayed effects spread over several days
(long-lags). This differential error could lead to a misattribution of PM effects to specific sources.
Monitoring on a daily basis is needed in a reasonable number of cities to evaluate this variation.
Statistical Power to differentiate between components
The panel listed several key factors that influence the statistical power of an epidemiology
study. These include:
• Number of daily health events - need large population size.
• Variability in estimated exposure (indicated by variation in pollutant concentration across
space for long-term cohort studies or change in daily concentration for time-series studies).
We want to maximize variance and minimize covariance.
December 2008 18
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• Maximize measurement of concentration variance. Daily speciation measurements in a
location will provide a dataset that documents the day-to-day variation in concentration that
occurs at the monitoring site. The point was made that concentrations vary a great deal within
a 24-hour period and, therefore, temporal variation in PM species within an area could be
better quantified using continuous monitors. Continuous methods also would minimize
concerns about artifacts. Local sources are another determinant of variation in PM species
concentrations. Better spatial resolution in the data will better account for local sources.
• Minimize covariance between PM components in datasets. Panelists emphasized that studies
are not able to differentiate the relative importance of PM components in associations with
health effects if concentrations are highly correlated in a study location. To avoid this
problem they recommended that study sites be selected where covariance between PM
components is minimized or different. The point was made that meteorological factors are a
key determinant of correlation in daily concentration change for PM components, which
poses analytical difficulties. Toxicology studies will help to sort out relative toxicities among
PM components.
Spatial scales
For time-series studies, if daily change in pollutant concentration is homogenous across the
population, then one "central" monitor will adequately characterize daily change in ambient
concentration for that city. This assumption needs to be explored at multiple study sites. For long-
term cohort studies, better within-city spatial resolution provides better estimates of annual average
concentrations for individual study members, and increases the variation in exposure data. This
results in an increase in statistical power.
Some panelists emphasized that exposure estimates based on concentration data from one
PM speciation monitor in the middle of the city will not add much information for epidemiology
studies because exposure measurement error for the overall population is too large. Spatial
variability in PM component concentrations is an important issue. Participants also were urged to
integrate models that take into account meteorology and source information over space, such as
CMAQ, into exposure estimation. At the same time, toxicology studies will have to play a large role
in understanding PM component influences on health. Participants were cautioned that toxicology
studies also are complex, and the use of pure components in toxicology studies has been
disappointing. It is very difficult to generalize results from these studies to draw conclusions about
responses to the ambient mixture.
The influence of local sources in the vicinity of a monitor needs to be understood before
those data are assumed to represent exposure for the population in a city. One panelist emphasized
that during certain periods of time, local sources have been observed to highly influence ambient
concentrations of a PM component measured by a speciation monitor. The point was made that the
CSN monitors are sited to implement the NAAQS and assure compliance, not to support health
effects studies.
Recommendations
Desirable Attributes of Locations Selected for Daily Speciation Measurements
The following location characteristics were suggested as criteria for selecting a set of
metropolitan areas to support daily speciation monitoring:
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• Population size. This is the most important attribute since the number of daily deaths in a city
is the major determinant of statistical power of a time-series or case-crossover study. Large
metropolitan areas, including New York City, Los Angeles, and Chicago were proposed.
However, the comment was made that very large urban centers, such as New York City, have
characteristics, such as street canyons, that increase spatial variability. Other less large cities
with more homogenous geography may be better candidates. Once the population size is
relatively large, resulting in daily deaths of around 20, then other selection factors should
play a much greater role in the selection of urban areas for additional sampling.
• Different covariance structure between PM species in different cities.
• A variety of source contributions.
• Location of speciation monitor(s) in the city that minimizes the influence of local sources of
PM.
• Topography and city attributes that increase the likelihood that one or a few monitors will
characterize daily change appropriately.
• Expertise and support of State and local air monitoring personnel to collect daily filters and
maintain semi-continuous monitors.
• Existence of additional monitoring data in that location to supplement information on spatial
and temporal distribution of PM components and other pollutants.
• Special State/local or academic studies using multiple monitors may have been conducted in
the location to better characterize spatial and temporal variability and could be made
available for analysis. FRM filters may have been archived by State/local air quality agencies
and may be available for speciation analyses. The existence and availability of these studies
and filters needs to be explored. A panelist commented that these filters are a national
resource and need to be inventoried and kept, not thrown away.
• Consider the impact of weather and seasons, differences in behavior that affect exposure.
The Children's Health Study in Los Angeles was referred to during the discussion of site
selection for additional monitoring. Twelve communities were initially chosen based on the
hypotheses of interest for PM2.5 mass, the need to obtain a 3 - 5 fold difference in ambient
concentrations, and an emphasis on long-term health effects. Hypotheses concerning different
PM size fractions would result in selection of different communities. The design of the CHS was
considered innovative because a monitoring approach of 26 two-week sampling periods was
conducted which minimized the number of samples taken while capturing seasonal differences in
ambient concentration.
Action Items for Consideration
• Develop a recommendation on the best locations to conduct additional sampling. More
information must be collected to inform selection of proposed study sites and the minimum
number of locations to conduct additional sampling.
• Agree and prioritize important criteria for site selection.
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o Analyses and presentation of the correlation structure between PM components and
between PM components and other pollutants at the CSN speciation monitors. Kaz Ito,
New York University, volunteered to assist with this effort. Analyses are currently being
conducted by EPA's OAQPS and ORD.
o Identify locations with appropriate data from special studies on daily PM species
concentrations or spatial variability.
o Evaluate the feasibility of analyzing archived FRM filters from specific locations (for
certain components as appropriate).
o Develop a table summarizing relevant information. Information characterizing additional
criteria will be obtained and the table filled in (ORD, OAQPS).
* Kunzli, N.; Avol, E.; Wu, I; Gauderman, W.J.; Rappaport, E.; Millstein, I; Bennion, I; McConnell, R.; Gilliland,
F.D.; Berhane, K.; Lurmann, F.; Winer, A.; and Peters, J.M. (2006). Health effects of the 2003 Southern California
wildfires on children. Am. J. Respir. Crit. Care Med., 174(11):1221-1228.
Wu, J.; Lurmann, F.; Winer, A.; Lu, R.; Turco, R.; and Funk, T. (2005). Development of an individual exposure model
for application to the Southern California children's health study. Atmos. Environ., 39(2):259-273.
December 2008 21
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SESSION IV: THORACIC COARSE PARTICLE COMPONENTS
AND POTENTIAL HEALTH IMPACTS
Background/Objectives
In September 2006, the EPA revised the NAAQS for PM and amended the associated
national air quality monitoring requirements.9 As part of the amended monitoring requirements,
EPA finalized a Federal Reference Method (FRM) for thoracic coarse particles (i.e., PMio-2.s), even
though a NAAQS for PMi0-2.5 was not adopted. This was done to facilitate consistent research on
PMio-2.5 air quality and health effects and in promoting the commercial development of Federal
Equivalent Methods (FEMs) (71 FR 61212). The amended monitoring requirements require the
addition of PMio-2.5 measurements at 75 multi-pollutant monitoring sites (National Core or NCore
sites) starting on January 1, 2011. A subset of these monitoring sites will include speciated coarse
particle measurements. The purpose of this session was to discuss issues related to and relative
priorities for EPA to consider as speciation of thoracic coarse particles is added to the monitoring
networks to support future exposure and health studies.
When discussing issues and priorities of thoracic coarse particle measurements to support
health studies, it is important to acknowledge that we are starting from a different place than with
fine particle measurements. This presents both challenges and opportunities. First, while there is an
extensive network to monitor PMio and PM2.5, there exists no national network with the specific
intent to consistently and accurately measure PMio-2.5-10 As a result, the amount of PMio-2.5 air
quality data available and associated analyses are available at fewer locations than PMio or PM2.5
measurements. Second, there have been fewer health studies conducted to investigate relationships
between thoracic coarse particle concentrations and health endpoints, which is due in part to the first
challenge - limited available air quality data. Some health studies have been conducted using PMio
measurements in areas where the PM concentrations are dominated by thoracic coarse particles.
Other thoracic coarse particle air quality and health studies have relied upon data from locations
where co-located PMio and PM2.s monitors exist, but there are uncertainties in the consistency of
these data because the protocol for the PMio and PM2.s measurements is not usually identical.11
Despite these challenges, significant opportunities exist to inform the design of future thoracic
coarse particle monitoring programs and to better harmonize thoracic coarse particle measurements
with the needs of health effect researchers.
Session Overview
To investigate health effects associated with exposures to thoracic coarse particles will
require an improved understanding of the intra-urban, inter-urban, and urban-rural variability of
ambient thoracic coarse particle concentrations. Key uncertainties associated with intra-urban
ambient thoracic coarse particles concentrations include spatial, temporal, and compositional
variability, while key uncertainties with inter-city and urban-rural comparisons include variability in
9 See http://www.epa.gov/oar/particlepollution/actions.html for more information on amendments to EPA's National Air
Quality Monitoring Requirements.
10 U.S. EPA. (2005) Review of the National Ambient Air Quality Standards for Paniculate Matter: Policy Assessment of
Scientific and Technical Information, OAQPS Staff Paper (June 2005). U.S. Environmental Protection Agency,
Washington, DC, EPA-452/R-05-005.
11 U.S. EPA. (2005) Review of the National Ambient Air Quality Standards for Particulate Matter: Policy Assessment of
Scientific and Technical Information, OAQPS Staff Paper (June 2005). U.S. Environmental Protection Agency,
Washington, DC, EPA-452/R-05-005.
December 2008 22
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composition and temporal differences (e.g., seasonal). Ambient air monitoring networks can provide
insights to address these uncertainties. As a result, general questions that were posed to the panelists
in this session were as follows:
• What is the relative value of thoracic coarse particle speciation at planned monitoring
locations versus additional mass measurements?
• What is the relative value of understanding intra-urban versus inter-urban/rural variability in
thoracic coarse particle mass and composition?
• What recommendations can be made to inform the design of a thoracic coarse particle
monitoring network?
Members of the discussion panel included:
• Timothy Larson, co-chair, University of Washington
• Tim Watkins, co-chair, EPA, ORD
• David Diaz-Sanchez, EPA, ORD
• Richard Flagan, CalTech
• Terry Gordon, New York University
• Michael Hannigan, University of Colorado
• Thomas Peters, University of Iowa
• Joann Rice, EPA, OAQPS
• Jamie Schauer, University of Wisconsin
There was a general consensus that thoracic coarse particles are very complex and pose
significant challenges for both air quality and health scientists. Thoracic coarse particles differ from
fine particles and, in many cases, present greater challenges. For example, thoracic coarse particles
may exhibit larger spatial and temporal variability than fine particles and the composition of thoracic
coarse particles can vary in rural versus urban areas. The composition of thoracic coarse particles
can also differ from fine particles with introduction of biological materials and the increased
importance of metals. In addition, thoracic coarse particles also present new challenges related to
measurement technologies. The current FRM is based on the difference between measurements
taken with co-located PMio and PM2.5 integrated filter samplers. While this method provides a good
measurement of thoracic coarse particle mass, there is no direct thoracic coarse particle sample
collected. Other methods exist, such as dichotomous or continuous mass samplers, which can
provide valuable information about particle composition and temporal variability, but these methods
need to be evaluated further. In addition, the use of emerging passive monitoring approaches may
also provide opportunities for obtaining thoracic coarse particle data.
The thoracic coarse particle monitoring program is still evolving, and EPA's OAQPS is
seeking feedback and input on thoracic coarse particle network design issues. EPA should apply
lessons learned from the fine particle monitoring program and closely follow ongoing research
efforts, while moving forward with implementation of a thoracic coarse particle monitoring program.
The emerging thoracic coarse particle monitoring program should include continuous monitoring
technologies, where possible, and any speciation efforts should use consistent methodologies.
Ongoing research may also yield important insights for designing a thoracic coarse particle
monitoring program. The EPA's ORD recently awarded a series of grants to investigate the sources,
composition, and health effects of thoracic coarse particulate matter. These grants integrate
atmospheric measurements with toxicological and epidemiological investigations. ORD's in-house
research program will also produce relevant research results to consider when designing a thoracic
coarse particle monitoring program. Data from completed field efforts, such as the Detroit Exposure
December 2008 23
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and Aerosol Research Study (DEARS)12 and FRM evaluations, should be analyzed to provide
additional information about the intra- and inter-urban variability of thoracic coarse particles. Also,
ongoing and planned studies, such as the Birmingham Saturation Sampling study and Near Roadway
studies13, will also provide valuable information pertaining to thoracic coarse particles. Finally,
ORD's in-house health research program includes thoracic coarse particle toxicological studies
integrated with exposure and source apportionment analyses. While these research programs may
produce results to inform decisions regarding thoracic coarse particle measurements (e.g., methods,
monitoring locations, and components measured), the timing of the results will be critical since the
initial thoracic coarse particle measurements will be required at NCore sites on or before January 1,
2011.
Major Points Raised by Participants
The following questions and the accompanying responses summarize the major points of this
session:
What lessons can be learned from the PMi.g chemical speciation network and applied to
designing a thoracic coarse particle speciation network?
• Monitoring Methods
o Use consistent monitoring methods
o Use continuous methods, where possible
o Evaluate speciation methods now and conduct hypothesis driven pilots
• Frequency of measurements
o Consider conducting daily measurements at some thoracic coarse particle monitoring
locations.
• Archiving filters for future analyses
What is the relative value of thoracic coarse particle mass versus speciation measurements?
Initial efforts to monitor thoracic coarse particles should focus on mass measurements to
inform our understanding of spatial and temporal variability. We need to learn more about the
components of thoracic coarse particles and how composition varies across urban and rural areas, as
well as, speciation measurement techniques before making significant investments in speciation
monitoring. There is a significant amount of variation in thoracic coarse particle toxicity and we do
not know enough to invest heavily in speciated thoracic coarse particle measurements at this time.
What, potentially, are the most important thoracic coarse particle components to measure?
Based on toxicology studies, metals, especially soluble metals, may play an important role in
effects associated with thoracic coarse particle exposures. In addition, the role of biological
materials in thoracic coarse particle health effects is unclear but it is also potentially very important.
Analytical challenges limit our health-based hypotheses. Organics are quite different in thoracic
coarse particles and difficult to measure. Furthermore, a significant mass fraction of many thoracic
coarse particle samples is unknown/unidentified.
12 See www.epa.gov/dears for more information.
13.add reference/link
December 2008 24
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What methods are available to measure thoracic coarse particles?
There are several methods available for thoracic coarse particle measurements and we need
to continue to evaluate these methods for measuring thoracic coarse particles. The current FRM for
thoracic coarse particle is based upon the difference between PMi0 and PM2 5 measurements (using
identical protocols and flow rates). Dichotomous instruments provide a separate measurement for
thoracic coarse and fine particles. EPA studies show good agreement between the difference method
and dichotomous measurements for both thoracic coarse particle mass and speciation, although
additional analyses of speciation results from each method would be valuable. There are also
continuous methods (e.g., FDMS Dichotomous TEOM) available for thoracic coarse particle
measurement. In addition, other thoracic coarse particle sampling techniques may present
opportunities for collecting more data for potentially lower costs. For example, the DRUM sampler
may provide speciated size segregated measurements with a reduced operational burden. However,
additional research is needed to evaluate the DRUM (Davis Rotating Uniform size-cut Monitor)
sampler and compare results with other methods. Another promising monitoring approach uses
passive techniques in combination with scanning electron microscopy (SEM) to provide thoracic
coarse particle mass and speciation (elemental and morphological). These passive techniques can be
deployed at relative low cost in a variety of locations which provides opportunities for improved
spatial analyses of thoracic coarse particles.
In what locations should thoracic coarse particle measurements be made?
Consider monitoring in areas that are not in attainment for PMi0, but in attainment for PM2.s
(see Table 1 below) or at least areas with higher PMio and lower PM2.5 concentrations. Analyses of
data collected in these areas will provide insights into the thoracic coarse particle components or
sources which are driving non-attainment (or high levels of thoracic coarse PM). Subsequent health
studies can then provide information regarding the potential for health effects associated with
exposure to these thoracic coarse particle components and sources. Rural locations should be
included as well to improve understanding of differences between urban and rural thoracic coarse
particle concentrations.
At what height should thoracic coarse particles be monitored?
Additional research is needed to understand the vertical profile of thoracic coarse particle
concentrations and to identify the effect of monitoring height on thoracic coarse particle
measurements (current requirements for thoracic coarse particle monitoring height range from 2 to
15 meters).
How can the sources of thoracic coarse particles be identified?
Research is needed to identify tracers or marker compounds for thoracic coarse particles for
source apportionment analyses. Thoracic coarse particle sources likely fall into one of the following
categories: direct emissions from mechanical processes that crush or grind larger particles (e.g.,
from industrial operations, construction and demolition activities, and agricultural and mining
operations), resuspension of dusts (e.g., traffic-related emissions from tire and brake wear),
biological materials, and secondarily formed aerosols. The influence of local sources will likely be
relatively more significant for thoracic coarse particles than for fine particles.
December 2008 25
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Why should State/Local/Tribal agencies be interested in thoracic coarse particle monitoring?
Thoracic coarse particle monitoring in PMi0 non-attainment areas (particularly areas that are
in attainment for PM2.5) could provide valuable information for State and local agencies, while also
providing valuable information for air quality and health researchers that can be used in the design of
thoracic coarse particle monitoring programs.
What other tools are available for analyses of ambient thoracic coarse particle concentrations?
Tools such as land use regression models, GIS, satellite data, and atmospheric dispersion
models can supplement thoracic coarse particle monitoring data to provide enhanced information
regarding spatial and temporal distributions of ambient thoracic coarse particles. However, it is
important to note that there are uncertainties with outputs from these tools and while these tools were
acknowledged at the workshop, detailed discussions regarding their potential application were
beyond the scope of this workshop. The application of these tools may be included in future
workshop discussions, as appropriate.
Are there unique issues associated with thoracic coarse particle health studies?
There are some unique issues related to thoracic coarse particle health studies. First, in vivo
toxicology studies are more difficult because efficient animal models are not available, in part
because inhalation toxicological studies are not possible in rodents. Differences in thoracic coarse
particles typically found in rural versus urban areas also present challenges. Epidemiological studies
in rural areas may not have enough statistical power. Finally, the role of exposures to biological
components in health outcomes presents additional challenges.
Are there existing data available to analyze?
Various states, including Californian, New York, and Washington, have thoracic coarse
particle data available for mass and composition analyses. For example, the state of California has
collected and analyzed thoracic coarse particle measurements for spatial, temporal, and
compositional patterns.14 In addition, data and results from previous research studies that included
coarse particle measurements exist. One such study was conducted around major industries in
southern Chicago.15 EPA also has thoracic coarse particle data available from previous field studies
(e.g., FRM evaluations, source apportionment studies). Key science questions need to be developed
for thoracic coarse particle monitoring and then the existing data sets need to be identified that could
potentially be analyzed to address some of these questions.
Recommendations/Actions for Consideration
Building upon the general summary and major points above, the following is a list of
recommendations expressed at the workshop or developed by EPA staff based on the workshop
discussions.
14 Croes, B.E. (2003). Paniculate matter in California: Part 2 - Spatial, temporal, and compositional patterns of PM2.5,
PM10-2.5, and PM10. J. Air Waste Manage. Assoc., 53(12): 1517-1530.
15 Watson, J.G., Chow, J.C.; Kohl, S.D.; Kuhns, H.D.; Robinson, N.F.; Frazier, C.A.; andEtyemezian, V. (2000).
Annual report for the Robbins Paniculate Study - October 1997 through September 1998. Prepared for Versar Inc.,
Lombard, IL, by Desert Research Institute, Reno, NV.
December 2008 26
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• When making decisions regarding investments in speciated thoracic coarse particle
measurements, consider the following:
o Analyze existing thoracic coarse particle data sets first. Such analyses could begin to
address issues such as:
• How does thoracic coarse particle composition vary across cities and in urban and
rural areas?
• To what extent do speciation analyses using samples collected via the difference
methods differ from speciation of dichotomous samples?
o Conduct targeted thoracic coarse particle speciation monitoring which is hypothesis-
driven to decide what components to monitor and how.
o Consider evidence generated from toxicological studies to inform decisions on which
components to monitor.
• When evaluating potential locations for thoracic coarse particle monitors, consider areas that are
in attainment for PM2.5 but not for PMio. Speciated thoracic coarse particle monitoring in these
locations may provide insights regarding sources that may be contributing to non-attainment.
• Consider a thoracic coarse particle network design that includes a central site monitor collecting
mass and speciation measurements with satellite locations that could potentially use alternative
lower cost methods (e.g., passive methods).
• Consider collecting daily thoracic coarse particle measurements at some subset of locations.
• Encourage the use of thoracic coarse particle continuous methods, where possible.
• Continue to evaluate thoracic coarse particle sampling and analytical methods.
o Sampling methods to be evaluated include FDMS Dichotomous TEOM, DRUM, and
passive sampling approaches.
o Identify and evaluate potential methods to analyze biological components in thoracic
coarse particles.
• Incorporate thoracic coarse particle monitoring objectives in planned research field work.
Examples objectives include:
o Identification of marker compounds for thoracic coarse particle source categories.
o Characterization of vertical distribution of ambient thoracic coarse particles and the
associated implications for monitoring height.
o Identification of composition and sources of unidentified thoracic coarse particle mass.
• Consider application of land use regression models, GIS, satellite images, and atmospheric
dispersion models in conjunction with ambient thoracic coarse particle measurements to conduct
spatial and temporal variability analyses and to inform monitor locations.
• Closely follow ongoing research for potential insights for thoracic coarse particle monitoring
programs. The following research efforts may be particularly valuable:
o EPA STAR Grants16
16 See http://cfpub.epa.gov/ncer abstracts/index.cfm/fuseaction/displav.rfa/rfa id/450 for more information
December 2008 27
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Sources, composition, variability and toxicological characteristics of coarse
(PMio-2.s) particles in Southern California (University of Southern California)
Sources, composition, and health effects of thoracic coarse particulate matter
(University of Colorado at Boulder)
Cardiovascular effects of urban and rural thoracic coarse parti culate matter in
African American and white adults (University of Michigan)
Spatial investigation of sources, composition and long-term health effects of
thoracic coarse particulate matter (PMio-2.s) in the Multi-Ethnic Study of
Atherosclerosis (MESA) cohort (University of Washington)
Comparative Toxicity of Thoracic coarse Particles (New York University)
o EPA In-House Research17
Detroit Exposure and Aerosol Research Study (DEARS)
Birmingham Coarse Particle Study
Near Roadway Research Program
Cleveland Source Apportionment Study
Final County PMc Characterization Study (Region 9 RARE Project)
17 add a link to descriptions of these studies
December 2008 28
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Table 1. PMio Non-Attainment Areas with PM2.s Designations
(Source: EPA TTN Website)
Area
Phoenix, AZ
Ckrk Co, NV
Sacramento Co, CA
Salt Lake Co, UT
El Paso Co, TX
Utah Co, UT
Washoe Co, NV
Eagle River, AK
Coachella Valley, CA
Eugene-Springfield, OR
Imperial Valley, CA
Mun. of Guaynabo, PR
Yuma,AZ
Ogden, UT
Missoula, MT
Bonner Co (Sandpoint), ID
Butte, MT
Nogales, AZ
Sheridan, WY
Paul Spur/Douglas (Cochise Comity), AZ
Kalispell, MT
Miami, AZ
Juneaii, AK
Shoshone Co, ID
Ajo (Pima Comity), AZ
Coso Junction, CA
Owens Valley, CA
Mammoth Lake, CA
Hayden AZ
Flathead County; Whitefish and vicinity, MT
Poison, MT
Columbia Falls, MT
Trona,CA
Lane Co, OR
Anthony, NM
Ronan, MT
Pinehurst, ID
Sanders County (part) ^Thompson Falls & vicinity,MT
Fort Hall Reservation, ID
Lame Deer, MT
Rillito, AZ
Mono Basin, CA
Los Angeles South Coast Air Basin, CA
San Joaquin Valley, CA
New York Co, NY
San Bernardino Co, CA
Libby, MT
Approximate
Population
3,110,000
1,380,000
1,220,000
898,000
564,000
369,000
339,000
195,000
182,000
179,000
120,000
92,400
82,300
77,200
52,400
36,800
34,600
24,600
15,800
15,700
15,100
14,600
13,800
10,500
7,590
7,000
7,000
6,460
6,050
5,030
3,780
3,780
3,500
3,420
2,590
2,520
1,700
1,180
553
536
506
258
14,600,000
3,080,000
1,540,000
199,000
3,230
EPA
Region
9
9
9
8
6
8
9
10
9
10
9
2
9
8
8
10
8
9
8
9
8
9
10
10
9
9
9
9
9
8
8
8
9
10
6
8
10
8
10
8
9
9
9
9
2
9
8
PM10 Non-
Attainment
Classification
Serious
Serious
Moderate
Moderate
Moderate
Moderate
Senous
Moderate
Serious
Moderate
Serious
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Serious
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Serious
Serious
Moderate
Moderate
Moderate
PM2.5
Attainment?
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
December 2008
29
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SESSION V: AMBIENT AIR MONITORING REALITIES -
EPA/STATE/LOCAL PERSPECTIVES - SUMMARY AND
RECOMMENDATIONS
Background/Objectives
This session was designed for EPA and State/local staff who manage monitoring programs to
share their reactions (i.e., a reality check) to topics discussed in earlier sessions of the ambient air
quality monitoring and health workshop. This included providing recommendations for addressing
"low hanging fruit" as well as identifying significant challenges to making progress in ambient air
monitoring to advance health research for the criteria and related (e.g., chemical speciation) air
pollutants.
Session Overview
For this portion of the workshop a panel of experts in ambient air monitoring was assembled
to provide their reaction to the papers, presentations, and discussions at the workshop. The panel
included several staff and managers from EPA and State and local air agencies who are responsible
for implementing and overseeing the operation of routine ambient air monitoring programs for air
toxics, criteria, and other related air pollutants.
Members of the panel included:
• Dirk Felton, co-chair, New York State Department of Environmental Conservation
• Tim Hanley, co-chair, EPA, OAQPS
• Mike Gilroy, Puget Sound Clean Air Agency (Seattle, WA area local air agency)
• Richard Payton, EPA Region 8 (Lead Monitoring Region)
• Scott Reynolds, South Carolina Department of Health and Environmental Control
• Eric Stevenson, Bay Area Air Quality Management District (San Francisco, CA area local
agency)
• Susan Zimmer-Dauphinee, Georgia Department of Natural Resources
Major Points Raised by Participants
The panel provided input and reactions on a range of topics from the workshop. Common
themes across the panel members can be grouped into four categories: daily speciation sampling,
PM2.5 continuous mass data, data management, and communications.
Daily Speciation Sampling
One of the major needs identified by health researchers is to have daily speciation in up to
the largest 20 urban areas in the country. EPA currently works with State and local agencies to
provide a Speciation Trends Network (STN) operating at midnight to midnight every third day at 53
locations around the country, plus an additional 120 locations identified as "supplemental
speciation" that mostly operate on a sample schedule of every sixth day. Together the STN and
supplemental stations comprise the Chemical Speciation Network (CSN). The CSN data together
with collocated criteria pollutant gas measurements reported to AQS form the largest single source
of ambient air pollution data used by the health community in researching the health effects of air
pollution.
December 2008 30
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Concerns
The panel overwhelmingly supported the value of speciation data for use in health studies as
well as to directly support State and local data uses such as source apportionment of fine particles
and tracking control programs. However, the panel shared their concerns with the ability of State
and local agencies to take on the workload and cost associated with daily sampling via filter-based
speciation samplers. Even if funding were available to support the cost of the laboratory analysis,
monitoring agencies would have a difficult time supporting the field operations due to frequent site
visits and other logistical concerns. The panel identified that monitoring agencies were in some
cases already proposing to cut back on existing monitoring systems due to diminishing resources via
EPA grants and their own agency budgets. At the same time EPA's national contract laboratory has
a pre-negotiated price increase each year. Therefore, even under the best of scenarios, which is
usually flat funding, agencies are receiving less direct awards to support field activities every year.
While the panel expressed their doubts that anything substantive could be accomplished through
daily filter-based speciation, they did share thoughts on possible opportunities utilizing semi-
continuous speciation methods.
The panel also discussed the comparative uncertainty of health effect studies versus
uncertainty in the ambient air monitoring data. Panel members suggested, and no one disagreed, that
the uncertainty in the ambient air monitoring data was very small compared to the uncertainty in
health studies. Health researchers pointed out that it was not just an issue of not having daily data to
reduce uncertainty, but also an issue with interpreting the potential lag of health effects if daily
characterization of particle species is not available. After the meeting, panel members suggested that
through use of available PM2.5 filter-based mass, continuous mass, and filter-based speciation,
reasonably good estimates of daily speciation could be derived (e.g., statistically interpolating
chemical speciation on days 2 and 3 that the CSN sampler did not operate) for most major
metropolitan areas. EPA could work with a group such as HEI to make such estimates available.
Another option that could potentially support daily speciation is the rotating drum sampler,
discussed in the white paper found in Appendix D entitled "Air Quality Sampling: Benefits and
Costs of Daily Health Targeted Monitors for Fine Particle Components." The drum sampler was
identified as a possible alternative measurement technology to support high time resolution (every
six hours) of chemical speciation. The drum sampler measures various size classes of PM mass and
PM components which would allow for a more comprehensive characterization of the sizes of PM
by species. However, there is limited use of the drum sampler in applications other than special
studies with anecdotal stories that it does not perform well. The panel expressed concern that the
technology was not ready for routine application in State and local air monitoring networks, but were
open to reconsidering the technology farther down the road if additional development and testing
demonstrated that it would be easier to use than the current equipment and agencies would have at
least the same confidence in the data as is available from the existing speciation sampling platform
(i.e., the Met One SASS/SuperSASS for elements and ions). Even if the drum sampler advanced to
the point of having comparable sampling performance to the existing CSN samplers, there remain
two important issues that will need to be addressed. The first is the request made by health
researchers to have a consistent method over the entire study period that they are researching and the
second is ensuring data reporting to the AQS data system for integration with other ambient air
monitoring data.
December 2008 31
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Opportunities
Acknowledging the value of highly time-resolved speciation data, the panel did express an
interest in characterizing speciation on a daily basis utilizing some combination of semi-continuous
and filter-based methods. Such an approach could be beneficial to both the health research
community as well as supporting data needs of State and local agencies; which is very important in
order to gain the support of senior management across monitoring agencies. The panel discussed
how several agencies are reporting good results with semi-continuous methods such as the Sunset
carbon analyzer, Aethelometer, and Thermo sulfate analyzer. Other semi-continuous speciation
samplers exist, but have not been demonstrated to be comparable to filter-based methods or are so
complex that data completeness and quality suffer due the instrument needing constant attention in
the field. Utilizing some combination of semi-continuous carbon and sulfate characterization plus
providing for elemental analysis via XRF analysis on daily Teflon filters may provide a reasonably
complete characterization of the chemical speciation of most interest. The major species missing
from such a protocol would be semi-volatile nitrate and organic carbon. A possible surrogate for the
missing semi-volatiles could be the volatile channel of the Filter Dynamic Measurement System
(FDMS) monitor, which provides highly time resolved characterization of stable and volatile PM. A
protocol of semi-continuous and limited filter-based speciation for elements would be of interest to a
number of State and local agencies to provide a more complete characterization of the source
apportionment of their networks, especially on days above the NAAQS. Researchers are believed to
value such a data set on all days to improve our understanding of potential health/welfare effects
associated with ambient concentrations at or below the current standards. Review of existing
collocated continuous speciation and filter-based method data (CSN or IMPROVE) available in
AQS or from Regional or State monitoring organizations could provide an opportunity for health
researchers to evaluate the potential value of semi-continuous speciated data and the need for filter
based, co-located sampling.
PM2.s Continuous Mass Data
Several of the panel members commented on the availability of PM2.5 continuous mass data
and potential value to the health research community. Some panel members were surprised to learn
that these data were not being widely used in health studies. Across the country there are over 600
PM2.5 continuous mass monitors reporting hourly data with every major city covered by one or more
stations. These data are stored and reported in near real-time through State and local agencies web
sites and nationally through the AirNow program; see www.airnow.gov18. Long-term archiving of
PM2.5 continuous data is provided for in the AQS data base. Acknowledging the variety of PM2.5
continuous methods used and their performance according to climate and measured aerosol
components across the country, EPA set-up new AQS data storage protocols in 2006 so agencies
could store their data in a way that allowed data users to better utilize the PM2.5 continuous mass
data. Principally among the data storage protocols, data users can pull hourly data for the parameter
"Acceptable PM2.5 AQI & Speciation Mass" (parameter code 88502). This parameter code is
intended to represent the PM2.5 continuous mass data where the method meets performance criteria
18 www.airnow.gov provides illustrative maps of near-real time air pollution data and forecasts according to the EPA's
Air Quality Index (AQI). These maps are intended for the general public and as such are color coded according to the
AQI. Detailed near real-time air pollution and meteorology data from across the United States and Canada are available
through www.airnowtech.org. This site is intended for technical users of the data. Health researchers or any other data
user with a valid use for the information can request access to the site through the log-in screen on the web site.
December 2008 32
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suitable for reporting the AQI19. In March of 2008, EPA-ORD approved the first Federal Equivalent
Method (FEM) for PM2.5 (Met One BAM 1020). Data from this method will be stored under the
parameter code "PM2 5 at local Conditions" (88101) as are all currently reporting PM25 FRM's,
since these methods are approved for comparison to the NAAQS. All the available parameter codes
(sometimes referred to as pollutant codes) for storing PM2 5 continuous data in AQS are provided
below.
Parameter Name
PM2 5 LOCAL
CONDITIONS
PM2 5 TOTAL
ATMOSPHERIC
PM2 5 RAW DATA
ACCEPTABLE PM2 5
AQI & SPECIATION
MASS
PM2 5 VOLATILE
CHANNEL
Parameter
Code
88101
88500
88501
88502
88503
Purpose
Code for all FRM's, FEM, and
ARM's. Continuous FEM's
will be stored with this
parameter code. Data reported
to this parameter code are
generally eligible for
comparison to the NAAQS.
Valid data from methods
measuring total PM2 5 aerosols
in the atmosphere. FDMS is
the method currently stored
here
Valid uncorrected data that does
not meet DQO's for reporting at
least the AQI
Valid data that does meet the
DQO's for AQI reporting with
or without a correction or the
mass data from the CSN
network. Data reported to this
parameter code are not eligible
for comparison to the NAAQS.
Store important related data
such as the FDMS reference
channel.
~Active Samplers/
Monitors
940 FRM's
(-150 operate daily)
100
300
400 PM2 5
continuous
mass;
200 CSN
5020
In total,
there are
-600
operating
PM25
continuous
monitors;
some
monitors
dual report
With the availability of data from a large number of PM2 5 continuous mass monitors, health
researchers could utilize highly time-resolved data that can be used as a surrogate for the types of
exposures in an urban area and combined with FRM data as a tool to better understand PM2 5
continuous mass measurements with a positive bias relative to the FRM. PM2 5 continuous mass data
can be used to characterize different types of exposures by the time of day. For instance, weekday
morning rush hour would be an indicator of automotive emissions, while early mornings in the
winter might be an indicator of home heating (e.g., oil or wood smoke, depending on the
neighborhood). In some cases, the measurement principle and time resolution of a PM2 5 continuous
method results in data that have a positive bias relative to a 24-hour measurement on the filter-based
PM2.s FRM. The positive bias with PM2 5 continuous methods are most likely associated with semi-
volatile organics and nitrate that are not fully captured on the FRM due to evaporative losses that are
exacerbated in warmer months when the sample filter is exposed at ambient conditions. These
differences should be explored and included in analysis to health research data as the data are
19 Where bias is controlled to within +/-10% and correlation is at least 0.9 (R2 of 0.81) compared to collocated filter-
based FRM's
20 Every FDMS monitor provides outputs of the volatile channel; however, some monitoring agencies have data
management system limitations at their sample station; therefore only half the FDMS units (50 out of 100) in operation
are reporting this channel. EPA will be working with monitoring agencies to improve reporting of this channel.
December 2008
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already available and would help focus future research efforts on the most important particle species
(e.g., to what extent do the semi-volatile organics have a stronger or weaker association with health
effects?).
Data Management
The panel discussed several aspects of data management. One issue uncovered was the
discovery that at least two major metropolitan areas were providing for daily sampling and analyses
of the major fine particle species - elements, ions, and carbon (i.e., Los Angeles and Denver). The
panel suggested that the applicable State and local agencies would highly value this additional
speciation data being utilized by health researchers. To ensure data are utilized, it was suggested
that, where appropriate, these important measurements be reported to the AQS data system.
Specifically, in cases where health researchers are utilizing monitoring agency filters, plans should
be made to load the data to AQS. In cases where health researchers are performing their own
sampling, there would still be value in making the data available to a wider audience of users;
however, monitoring agencies and health researchers would need to ensure data comparability with
the existing network so other data users understand the usefulness of the data.
Panel members offered that many agencies use EPA's AQS data system as the sole long-term
repository of their data, but there were exceptions. While all routine State and local agency data
make their way to AQS, in some cases agencies have their own long-term data record with unique or
even routine measurements that are not in AQS. Health researchers should ask monitoring contacts
about any such data when discussing availability of ambient air monitoring data with State and local
agencies
Panel members also offered that they can usually assist health researchers in the retrieval of
data from their network, even when data are located on the AQS data system.
Communications
Each major stakeholder group (EPA health and monitoring programs, external health
researchers, State and local air programs) has dozens of other groups that they communicate with.
One of the goals of this workshop is to establish better communication between decision makers in
routine ambient air monitoring programs and researchers that perform health effects studies that are
used to inform NAAQS reviews. EPA, the routine ambient air monitoring programs, and the
external health research are all motivated to invest time to improve communication that can lead to
better use of ambient air monitoring data; however, all groups also have responsibilities to other
stakeholders as well. So while monitoring programs and health researcher are making important
strides to improve the use of data and therefore provide better products that can inform NAAQS
reviews, each group also has a responsibility to other data users and clients of the health effects
research studies.
The panel suggested that there are already examples of good communications between health
researchers and State and local air monitoring programs; however, communications could be
improved. Developing a collaborative relationship is critical to maximize the benefit of both kinds
of organizations. Health researchers will be able to better influence network decisions, and
monitoring agencies will be able to better utilize health research results produced from their own
ambient air monitoring system. Panel members cited examples where they had a good line of
communication between health researchers and cases where they did not, even with their own sister
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health agencies in State governments. Panel members strongly support having health researchers
review their annual monitoring network plans and to develop a line of communication as early as
possible for identifying the most important monitoring stations being used in a health study. Where
available, health and research community participation in plan review and development could be
advantageous to all parties. Panel members suggested that they do usually attempt to accommodate
the health research needs in cases where data are already being collected, especially if the
researchers can identify the specific need and product in advance of a planned change in monitoring
at a site.
A couple of panel members commented on State/local review of grant proposals and how
early involvement of air programs may help strengthen this review. For example, if EPA, the
external health community, and State and local air monitoring programs already had a good line of
communication, then research plans for a specific research study could be strengthened by making
use of existing State and local monitors and data. Research resources could then be focused on
augmenting the State/local data by targeting measurements and activities not planned for in the State
air monitoring network. While this workshop has had a good deal of focus on what data are missing,
there are actually some redundancies between monitoring data collected for specific health studies
and State/local air monitoring programs. Future research work should focus on maximizing the use
of the State/local monitoring networks and identifying the most critical "missing" data to collect.
Some panel members commented on the wide variety of requests being made by the health
community (e.g., lots of daily speciation, need for multiple speciation sites across a city, need for
other measurements not currently being conducted such as ultrafmes, etc.) and suggested that it
would be useful to have a prioritized strategic plan of health research needs for ambient air
monitoring data. Such a plan should include commentary on the usefulness of co-located
measurements such as pollutant gases and meteorological measurements.
Recommendations/Actions for Consideration
Daily Speciation Sampling
Develop a protocol that utilizes a combination of semi-continuous and filter-based methods
to characterize daily speciation. Ensure such a pilot has identified data users that can comment on
the expected data quality so that if successful, these methods can be applied in other areas. Initially
pilot this protocol in two or three major cities.
Specific actions to develop hybrid semi-continuous and filter-based daily speciation protocol:
• Inventory semi-continuous speciation methods operating across the country. Encourage
entry of data into AQS where possible (OAQPS)
• Perform data analysis and determine data quality on available co-located semi-continuous
and CSN data. (OAQPS)
• Evaluate intercomparability of Sunset carbon and Aethelometer data to CSN carbon data.
Provide recommendations for relative value of each method to provide carbon data on days
with no filter-based carbon sampling (ORD)
• Develop analytical protocol for XRF analysis on PM2.5 FRM filters (ORD)
o Test protocol by retrieving filters from a small number of monitoring agencies where
there are co-located FRM and CSN data. Perhaps prioritize stations with collocated CSN
sampler.
o Analyze comparability of CSN and PM2.5 FRM Teflon filters
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o Provide recommendation on usefulness of XRF analysis on PM2 5 FRM filter on days
when the CSN Teflon channel has operated and already has elemental assay
• Develop laboratory protocol for additional carbon assay - Thermal Optical Reflectance - on
PM2.5 Teflon filter (ORD)
• Identify 2-3 test locations to pilot hybrid semi-continuous and filter-based daily speciation
protocol. Prioritize based on both:
o State/local partner(s) that will value daily speciation characterization for their program
o Health research studies that are already underway, or soon to be underway, utilizing
speciation data from an existing station
Other related actions
• Develop research action plan to address research questions from PMi0-2.5 chemical speciation
Whitepaper (ORD)
• Analyze data from co-located hourly PMio-2.5 by difference
• Analyze data from FDMS Dichotomous monitor; focus on volatile channel
• Evaluate potential for a urban PM-10 Sunset co-located with a PM-2.5 Sunset
• Demonstrate long-term (1-2 years) successful operation of the rotating drum sampler and
Synchrotron XRF analyses, including loading elemental data to AQS and comparability to
CSN data (ORD).
PM2 s Continuous mass data
The availability of over 600 PM2.5 continuous monitors provides an opportunity to help
health researchers utilize an important data set that might provide insights on the most important
exposures of PM2.5 by time of day and averaging period. EPA has already set up data storage
protocols in AQS so that data can be retrieved according to the performance of the PM2 5 continuous
monitors and/or methods being utilized. EPA-OAQPS is actively working to store already reported
PM2.5 continuous data going back to 200421 under these new storage protocols.
Specific actions that may help facilitate better use of these data in health studies:
• Provide recommendations on storage of PM2.s continuous mass data from years 2004 through
2006 under the appropriate parameter code so that data users retrieve data as expected
(OAQPS).
• Execute storage of PM2 5 continuous mass data under new parameter codes in AQS from
years 2004 through 2006 (OAQPS and State/local agencies).
• Develop long-term plan for including PM2.5 continuous mass data in next iteration of national
ambient air monitoring dataset used by health researchers (OAQPS and F£EI).
Data Management
Availability of ambient air monitoring data to facilitate a two way communication of data can
be improved by taking on the following actions:
• EPA should include a discussion of data reporting expectations in the solicitation of any new
long-term health studies where speciation data are involved. (ORD)
21 2004 was the first full year of national PM2 5 data reporting on AIRNow; the official launch date was October 1 of
2003. Prior to this date many agencies were just bringing their PM25 continuous data on-line, including developing
statistical adjustments to have their data more closely resemble the PM2 5 FRM data.
December 2008 36
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o In cases where the grant recipient will be utilizing methods consistent with the CSN
network, the data will likely be highly valued, and provisions should be made to
quality assure and report the data to AQS.
• Localized health effects studies should establish a contact with the State or local monitoring
agency to facilitate access to data that may not necessarily be reported to AQS. (Health
Researchers)
• Data leads on health effects research teams should be encouraged to contact State and local
agencies for access to local data, even when the data are reported to AQS. (Health
Researchers)
Communications
Many of the possible improvements to communication are already underway. For emphasis,
the communication recommendations from the panel are provided here even if redundant or already
underway:
• EPA will facilitate a continued dialogue between health effects researchers and routine
ambient air monitoring programs (OAQPS and ORD)
o Invite health effects researchers to share their work at monitoring conferences
o Invite leaders in the ambient air monitoring community to attend health effects
research meetings
o Engage the National Association of Clean Air Agencies (NACAA) at the Air Director
level to emphasize the importance of this work (OAQPS and ORD)
o Continue dedicated meetings between health effects researchers and routine ambient
air monitoring programs. (All)
o Encourage (State or PQAO) Air Monitoring Staff representation on science advisory
boards for health effects research programs that have significant monitoring data
needs. (Health Researchers, State/local agencies)
• Facilitate input of health effects researchers into annual monitoring network plans22 (EPA
Regions, OAQPS, ORD, Health effects researchers)
o Maintain AMTIC web site with links to each State/local agencies' annual monitoring
network plan
o In early 2008, EPA ORD's National Center for Environmental Research (NCER)
conducted a preliminary survey of EPA-funded epidemiology studies to develop an
initial list of monitoring sites that are being used in current or planned health
studies23. ORD will consider options for expanding/updating this preliminary list and
ways to make it available to State/local air agencies when they are considering
changes to their monitoring networks.
o Health effects researchers should engage monitoring program early in the process
when they have specific needs for continued data availability.
Annual Monitoring Network Plans are due to the applicable EPA Regional Office by July 1 of each year. These
documents represent the plans for monitoring in the subsequent calendar year and are subject to EPA approval. Each
agency is required to make their plan available for public inspection for at least 30 days prior to submittal to EPA. See
40 CFR §58.10. An internet link to plans is available at: http://www.epa.gov/ttn/amtic/plans.html
23 See Appendix G.
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Develop strategic plan for health effects research ambient air monitoring data needs (ORD
and HEI with input from OAQPS)
o EPA and State/local agencies need to know the most valued data to help protect
monitoring resources and guide future monitoring investments. Such a plan would be
a guide for all monitoring investments including those by EPA-ORD, health effects
researchers themselves and routine monitoring programs. Example topics to address
include:
• are Aethelometer BC data adequate?
• is the UV channel useful?
• are nitrate data necessary at all, or only in certain geographical areas?
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BACKGROUND MATERIALS
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Appendix A: Workshop Agenda and Participant List
Ambient Air Quality Monitoring and Health Research:
Workshop to Discuss Key Issues
April 16 and 17, 2008
US EPA Main Campus, Building C-Auditorium, Research Triangle Park, NC
EPA is interested in having an open dialogue with a small group of representative experts
regarding health research priorities for ambient air quality monitoring data that could best
advance our understanding of the impacts of air pollutant exposures on public health. In
particular, we are interested in hearing comments and recommendations from experts on steps
that could be taken to improve our understanding of the impact of fine particle components and
other key air pollutants. These steps might include prioritization of monitoring sites and/or the
designation of sites for more frequent monitoring.
This meeting is another step in a series of interactions to foster improved long-term
communication between air quality experts and health researchers. This communication is
critical for ensuring that the ambient air monitoring program offers, and health researchers use,
the best and most appropriate data possible to support the health research that serves as a
foundation for EPA's reviews of the national ambient air quality standards (NAAQS).
Primary Meeting Objectives
• To discuss specific recommendations for concrete steps that EPA and other organizations
could take in the ambient air monitoring program to advance health research for the criteria
air pollutants.
• To reexamine and assess progress to date on key issues identified at an earlier workshop
sponsored by the Health Effects Institute (HEI) and EPA24 and in follow-up discussions with
the EPA-PM Center Directors, HEI National Particle Component Toxicity (NPACT)
Directors, and other researchers.
• To provide constructive feedback on the following draft "white papers" developed to aid in a
common understanding of the issues under discussion:
• Chemical Speciation Network (CSN) - Carbon Issues
• Access to EPA's Air Quality Data for Health Researchers
• Air Quality Sampling: Benefits and Costs of Daily Health Targeted Monitors for Fine
Particle Components
24 HEI and EPA co-sponsored a meeting in late 2006 to discuss how the use of the accumulating data derived from
nationwide monitoring of fine paniculate matter (PM) components can facilitate current and future health effects
studies and improve comparisons of risk estimates across studies. The workshop illuminated issues associated with
accessing and analyzing monitoring data and identified needs of the health effects research community regarding
monitoring of fine particle components. See http://www.healtheffects.org/AODNov06/AQDWorkshop.html for
more information.
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• Long-term communication strategies for improving interactions between health
researchers and air quality staff on changes in ambient air monitoring specifically:
o Network Design and Site Selection Approval
o Methods Implementation
Expectations for Meeting Participants
This workshop is designed to inform EPA staff plans for the criteria pollutant ambient air
monitoring program to ensure that the most effective approaches for providing monitoring data
to support health research studies are included. A large portion of the workshop agenda is
devoted to discussion - the goal of which is to talk about recommendations for concrete steps
that could be made to move the alliance between health and monitoring objectives forward.
Thus, to maximize the effectiveness of the meeting, workshop participants will be expected to be
familiar with background information distributed prior to the workshop, including draft white
papers. The workshop discussions will need to be forward looking - to identify specific near-
and long-term steps that EPA's health and monitoring staff, as well as external organizations and
science communities, can take to improve the ambient air monitoring program to appropriately
advance our understanding of the health impacts of criteria air pollutants. This workshop is
designed to be an honest and objective endeavor to address health research needs, however,
participants must also understand that EPA resources are, and will most likely continue to be,
limited. Therefore, providing prioritization of recommendations for EPA and other
organizations to consider is essential if we are to make some clear steps forward and, hopefully,
build from anticipated initial successes.
Wednesday, April 16,2008
8:30-9:45 Welcome/Introductory Remarks
8:30 - 8:45 Purpose of the Meeting/Overview of Key Issues/Summary of Progress
Dr. Daniel Costa, EPA National Program Director for Air Research/ORD
8:45-9:00 EPA Program Office Perspective
Ms. Lydia Wegman, Director, Health and Environmental Impacts
Division/OAQPS
Mr. Richard Way land, Director, Air Quality and Assessment
Division/OAQPS
9:00 - 9:45 Air Quality Experts and Health Researchers Working Together:
Why Communication is Critical - Stories of Success
Dr. Morton Lippmann, New York University
9:45-10:00 BREAK
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10:00-2:45 Session I: Elemental and Organic Carbon Measurements
Background
In 2007, EPA made changes in the monitoring network to address inconsistencies in
carbon sampling and analysis procedures used in urban CSN (STN/SLAMS) and rural
IMPROVE programs. Health researchers have repeatedly raised concerns to EPA regarding this
methodology change and potential interruptions in monitoring data used for time-series analyses.
This session will include: (1) a brief overview of what measurements are currently being made;
(2) highlights from a recent CSN/IMPROVE: Carbon PM monitoring workshop with emphasis
on issues of most interest to the health research community25; (3) approaches that are being
evaluated for relating different data sets and the potential impacts for on-going epidemiological
studies, and (4) opportunities to discuss steps that are being taken or could be taken to identify
and address information gaps, including continuous carbon measurements.
Background Information - Draft White Paper: "Chemical Speciation Network (CSN) -
Carbon Issues"
10:00 - 10:20 Overview and Introduction to Key Issues
Dr. Venkatesh Rao, EPA/OAQPS
Dr. Barbara Turpin, Rutgers University
10:20 - 10:40 A Health Researcher's Perspective: What's So Special About
Carbon?
Dr. EdAvol, University of Southern California
10:40 - 11:00 CSN Carbon Monitoring Changes and Issues
Ms. Joann Rice, EPA/OAQPS
11:00 - 11:20 Carbonaceous Aerosol Sampling Artifacts in the National
Monitoring Networks
Dr. John Watson, Desert Research Institute
11:20 -11:40 Transitions: Relating "Old" to "New" Methods
Dr. Warren White, University of California-Davis
11:40 - 11:50 Predicting Carbonaceous Species Concentrations with Partial
Least Squares
Dr. Philip Hopke, Clarkson University
11:50 - 12:10 Impact of Method Transitions to Health Research
Dr. Michael Hannigan, University of Colorado-Boulder
12:10-12:30 Air Quality Monitoring: Perspectives from East and West
Mr. DirkFelton, NY Department of Environmental Conservation
25 See http://vista.cira.colostate.edu/improve/Publications/Workshops/Carbon Jan2008/CarbonMeeting2008.htm for
more information.
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12:30-1:30 LUNCH
Session I: Elemental and Organic Carbon Measurements (cont.)
1:30-2:45 Panel Discussion
Dr. EdAvol, Univ. of Southern California
Dr. Michelle Bell, Yale University
Dr. Judith Chow, Desert Research Institute
Mr. Neil Frank, EPA/OAQPS
Dr. Philip Hopke, Clarkson University
Dr. Michael Kleeman, University ofCA-Davis
Dr. Allen Robinson, Carnegie Mellon Univ.
Dr. Warren White, University ofCA-Davis
Suggested Issues for Discussion:
• What types of measurement error are problematic for epidemiology?
• Knowing this, what is of most concern (to epidemiology) given the CSN changes?
Step changes in detection limits/precision?
• Step changes in OC and EC (but not TC)?
• Bias due to sampling artifacts?
• Other?
• Are past and planned measurement comparisons adequate:
• to aid epidemiology study analyses?
• to "harmonize" results from old and new methods?
• How should blanks and sampling artifacts be handled?
• What other types of carbon measurements are good candidates for examination in large
epidemiology studies?
2:45-3:00 BREAK
3:00-4:30 Session II: Accessing Ambient Air Monitoring Data
Background
EPA's Air Quality System (AQS) is designed to collect and store ambient air monitoring
information. EPA recently introduced the AQS Data Mart to facilitate access to this monitoring
information. The AQS Data Mart is a generic "retrieval" tool that provides the ability to query
any information, but it does not provide significant data exploration or analytic capabilities.
These capabilities are left to the "analytical" tools. Various analytical tools, or interfaces, are
available including HEFs Air Quality Database, which focuses on levels of PM2.5 components
and gaseous pollutants at and near STN and SLAMS sites. This discussion will focus on data
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access issues and how to help health researchers obtain monitoring data for fine particle
components and other critical pollutants more easily.
Background Information - Draft White Paper: "Access to EPA's Air Quality Data for
Health Researchers"
3:00 - 3:10 Overview and Introduction to Key Issues
Dr. Bryan Hubbell, EPA/OAQPS
Dr. Michelle Bell, Yale University
3:10 - 3:20 Overview of Draft White Paper: "Access to EPA's Air Quality
Data for Health Researchers"
Mr. NickMangus, EPA/OAQPS
3:20 - 3:30 Summary of Recent Data Summit
Mr. Rich Scheffe, EPA/OAQPS
3:30 - 4:30 Panel Discussion
Dr. Sara Dubowsky Adar, Univ. of Washington
Dr. Kaz Ito, New York University
Mr. John Langstaff, EPA/OAQPS
Mr. NickMangus, EPA/OAQPS
Mr. Richard Poirot, Vermont DEC
Dr. Betty Pun, AER
Mr. Rich Scheffe, EPA/OAQPS
Suggested Issues for Discussion:
In general, we see that access to ambient air monitoring data needs to support health
research/assessments falls into four general categories:
. epidemiological studies
. exposure/risk assessments
. public health surveillance
. health impact assessments
Keeping these broad categories in mind and understanding that the goal is to provide a
framework for delivering consistent, well-documented monitoring data to users including the
health research community, the issues discussed in this session will focus on data type/format,
access, and context including:
. What key data and formats do health researchers need access to?
. How user-friendly are the data bases currently available to health researchers? What
are the similarities/differences between the various data bases and how are they
communicated?
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. What potential changes could be made to improve access to ambient air monitoring
data?
. What mechanisms are currently being used to communicate the limitations associated
with the ambient air monitoring data? Are there specific recommendations for
improving how the limitations are characterized?
. How can we improve the understanding of the limitations, quirks, and context of the
ambient air monitoring data and assist end users in determining the subsequent
appropriate use of the raw data?
4:30 ADJOURN
Thursday, April 17, 2008
8:00-8:30 Summaries of Day 1 Discussions and Comments on Draft White
Papers/Next Steps
8:00 - 8:15 Session I: Elemental and Organic Carbon Measurements
Dr. Barbara Turpin, Rutgers University
8:15-8:30 Session II: Accessing Ambient Air Monitoring Data
Dr. Bryan Hubbell, EPA/OAQPS
Dr. Michelle Bell, Yale University
8:30 - 10:15 Session III: Ambient Air Monitoring for Health Research
Background
EPA has been measuring fine particle components in urban areas since 2001. The
network consists of 54 sites intended to capture long-term trends (Speciation Trends Network or
STN) and approximately 150 other State and local air monitoring stations (SLAMS).
Collectively the urban locations are part of the EPA Chemical Speciation Network (CSN). Due
to cost considerations the CSN was reduced in 2006 from its original size of approximately 240
stations to its present size. Currently, fine particle components are measured at each location
every third or sixth day. The Interagency Monitoring of Protected Visual Environments
(IMPROVE) network, covering background sites in national parks and wilderness sites in
addition to Washington, DC and the South Bronx in New York City, provide additional data of
great value to researchers. Health researchers have requested EPA implement daily fine particle
speciation measurements, however resource constraints have impeded any real plans to this end.
This session will explore opportunities to obtain these types of data in critical locations to make
true inroads in improving our understanding of the temporal variability of fine particle
components in ambient air. The monitoring issue presently is fine particles (and components),
but looming ahead is the issue of thoracic coarse particles - what steps can we make to prepare
for this new data-source?
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Background Information - Draft White Paper: "Air Quality Sampling: Benefits and Costs
of Daily Health Targeted Monitors for Fine Particle Components"
8:30-8:45 Overview and Introduction to Key Issues
Dr. Barbara Glenn, EPA/ORD/NCER
Dr. Joel Schwartz, Harvard School of Public Health
8:45-10:15 Panel Discussion
Dr. Robert Devlin, EPA/ORD/NHEERL
Dr. Patrick Kinney, Columbia University
Dr. Lucas Neas, EPA/ORD/NHEERL
Dr. Roger Peng, Johns Hopkins University
Dr. George Thurston, New York University
Dr. Jay Turner, Washington Univ. in St. Louis
Suggested Issues for Discussion:
. Sources of Error - which are the most limiting?
o For time-series studies that rely on air monitoring data collected every third or
sixth day from a single (or a few) central site monitors, which major sources of
error are the most important? Why? Which is the most important?
• Uncertainties in exposure assessment associated with:
> Missing days.
> Spatial variation.
> Monitor location.
> Instruments measurement error or analytic methods.
o Could exposure modeling to "fill-in" missing days adequately address
uncertainties associated with every third or sixth day monitoring data?
. If daily monitoring was going to commence in a few cities in the U.S., what is the best
monitoring plan to study the relative health importance of PM components in the ambient
mixofPM?
o What are the best sites? Why? What site criteria are the most important?
o What minimum number of locations for daily sampling is adequate to address a
particular research area?
o What components would you evaluate first?
. Integrating previous or ongoing data collection to obtain retrospective data.
o In some locations, FRM filters may have been archived and could be analyzed to
learn more about daily variation of fine particles and components.
o In some areas, data from continuous monitors for previous years is available.
o In previous years, special studies or grant-funded studies have collected data on
PM species.
o How could these data be integrated to provide daily data for time-series studies?
If data from different instruments or methods were combined to obtain a set of
daily ambient concentration data for a city, would this introduce a significant
source of uncertainty/error?
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o What components should be measured and what are the issues associated with
their measurement and use in analyses?
10:15-10:30 BREAK
10:30-12:00 Session IV: Thoracic Coarse Particle Components and
Potential Public Health Impacts
Background
On September 27, 2006, the U.S. Environmental Protection Agency (EPA) amended its
national air quality monitoring requirements.26 As part of these changes, EPA and the states will
add measurements of "thoracic coarse particles" (i.e. PMio-2.s) at 75 multi-pollutant monitoring
sites (National Core or NCore sites). Some monitors will provide at least hourly measurements in
near real-time (continuous mass concentration monitoring); while other monitors will sample the
air over a 24-hour period and require laboratory processing of the sample (filter based sampling).
Filter-based monitoring will enable development of PMi0-2.5 methods for chemical speciation of
thoracic coarse particles. This session will explore criteria to consider as EPA adds speciation of
thoracic coarse particles to the ambient air monitoring network.
10:30-11:00 Overview and Introduction to Key Issues
Dr. Timothy Larson, University of Washington
Mr. Timothy Watkins, EPA/ORD/NERL
11:00-12:00 Panel Discussion
Dr. David Diaz-Sanchez, EPA/ORD/NHEERL Dr. Michael Hannigan, Univ. of CO -
Boulder
Dr. Philippine, SCAQMD Dr. Thomas Peters, University of Iowa
Dr. Richard Flagon, CalTech Ms. Joann Rice, EPA/OAQPS
Dr. Terry Gordon, New York University Dr. Jamie Schauer, Univ. of WI-Madison
Suggested Issues for Discussion :
. What is the relative value of coarse particle speciation at planned monitoring locations
versus additional mass measurements?
. What is the relative value of understanding Intra-urban versus Inter-urban/rural
variability in coarse particle composition and spatial and temporal distributions?
. What are your recommendations for coarse particle network design?
12:00-1:00 LUNCH
26 See http://www.epa.gov/oar/particlepollution/actions.html for more information on amendments to EPA's
National Air Quality Monitoring Requirements.
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1:00-2:30 Session V: Ambient Air Monitoring Realities -
EPA/State/Local Perspectives
Background
EPA works with State, local, and tribal air agencies to design and implement ambient air
monitoring networks to meet several monitoring objectives including:
. Determining compliance with standards (i.e., the NAAQS)
. Providing air pollution data to the general public on a timely basis
. Supporting the development and tracking of the effectiveness of emission control
programs
. Providing input data for health and welfare effects and exposure research studies
. Providing input data for health and welfare risk/exposure assessments conducted for
NAAQS reviews
. Measuring overall progress of air pollution control programs
Opportunities are available for interested parties to provide comments on monitoring
network plans to ensure input from health researchers and other interested users is considered in
the design of these plans. Two draft white papers review the current processes for public
comments on the monitoring network plans and changes to monitoring methodologies,
respectively, as well as options for future efforts to improve communications with the health
research community regarding ambient air monitoring networks.
This session is designed for EPA and State/local staff who manage monitoring programs
to share their reactions (i.e., a reality check) to topics discussed in earlier sessions. This may
include providing recommendations for addressing "low hanging fruit" as well as significant
challenges that may need to be addressed in order to make considerable progress in the ambient
air monitoring program to advance health research for the criteria air pollutants.
Background Information - Draft White Papers: "Ambient Air Monitoring Networks:
Network Design and Site Selection Approval" and "Ambient Air Monitoring Method
Implementation"
1:00 - 1:15 Overview and Introduction to Key Issues
Mr. DirkFelton, NY DEC
Mr. Timothy Hanky, EPA/OAQPS
1:15-2:30 Panel Discussion
Dr. Philippine, SCAQMD
Mr. Michael Gilroy, Puget Sound CAA
Mr. Richard Pay ton, EPA/Region 8
Mr. Scott Reynolds, SCDHEC
Mr. Eric Stevenson, BAAQMD
Ms. Susan Zimmer-Dauphinee, GA DNR
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DRAFT 4/02/08 - For Discussion Purposes Only - Do Not Quote or Cite
2:30-2:45 BREAK
2:45-3:30 Summaries of Day 2 Discussions and Comments on Draft White
Papers/Next Steps
2:45-3:00 Session III: Ambient Air Monitoring for Health Research
Dr. Barbara Glenn, EPA/ORD/NCER
Dr. Joel Schwartz, Harvard School of Public Health
3:00 - 3:15 Session IV: Thoracic Coarse Particle Components and Potential
Public Health Impacts
Dr. Timothy Larson, University of Washington
Mr. Timothy Watkins, EPA/ORD/NERL
3:15-4:00 Concluding Remarks/Emerging Issues/Next Steps
Dr. Morton Lippmann, New York University
Ms. Lydia Wegman, EPA/OAQPS
Mr. Richard Wayland, EPA/OAQPS
Dr. Daniel Costa, National Program Director for Air Research, EPA/ORD
4:00 ADJOURN
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Ambient Air Quality Monitoring and Health Research: Workshop to Discuss
Key Issues - Participants
April 16-17, 2008
Last Name
Arnold
Avol
Baldauf
Baxter
Bell
Brook
Bucky
Chow
Costa
Devlin
Diaz- Sanchez
Dubowsky Adar
F el ton
Fine
Flagan
Foley
Frank
Garbe
Garcia
Gilliland
Gilroy
Glenn
Godleski
Gordon
Hall
Hanley
Hannigan
Hansen
Hassett-Sipple
Holland
Hopke
Hubbell
Ito
Jenkins
Katz
Kim
First Name
Jeff
Ed
Rich
Lisa
Michelle
Jeffrey
Barbra
Judy
Dan
Robert
David
Sara
Dirk
Phillip
Richard
Kristen
Neil
Paul
Val
Alice
Mike
Barbara
John
Terry
EricS
Tim
Michael
Craig
Beth
David
Phil
Bryan
Kaz
Scott
Stacey
Jee- Young
Affiliation
EPA National Center for Environmental Assessment
University of Southern California
EPANRMRL
EPA National Exposure Research Laboratory
Yale University
Environment Canada
EPA National Center for Environmental Assessment
Desert Research Institute
EPA Office of Research & Development
EPA National Health & Environmental Effects Research
Laboratory
EPA National Health & Environmental Effects Research
Laboratory
University of Washington
New York State Department of Environmental Conservation
South Coast Air Quality Management District
California Institute of Technology
EPA National Exposure Research Laboratory
EPA Office of Air Quality Planning & Standards
Centers for Disease Control & Prevention
EPA National Exposure Research Laboratory
EPA National Exposure Research Laboratory
Puget Sound Clean Air Agency
EPA National Center for Environmental Research
Harvard School of Public Health
New York University
EPA National Exposure Research Laboratory
EPA Office of Air Quality Planning & Standards
University of Colorado at Boulder
EPA National Center for Environmental Assessment
EPA Office of Air Quality Planning & Standards
EPA National Exposure Research Laboratory
Clarkson University
EPA Office of Air Quality Planning & Standards
New York University
EPA Office of Air Quality Planning & Standards
EPA National Center for Environmental Research
EPA National Center for Environmental Assessment
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DRAFT 4/02/08 - For Discussion Purposes Only - Do Not Quote or Cite
Last Name
Kinney
Kirrane
Kleeman
Kryak
Lamason
Langstaff
Larson
Lippman
Long
Lorang
Luben
Mangus
Martin
Mikel
Mintz
Mukerjee
Neas
Ozkaynak
Payton
Peltier
Peng
Peters
Pierce
Pinto
Poirot
Pun
Rao
Reynolds
Rice
Richmond
Robarge
Robinson
Ross
Sacks
Schauer
Scheffe
Schultz
Schwartz
Sheldon
First Name
Patrick
Ellen
Mike
David
Bill
John
Tim
Morton
Tom
Phil
Tom
Nick
Karen
Dennis
David
Shailbal
Lucas
Haluk
Richard
Richard
Roger
Thomas
Tom
Joe
Rich
Betty
Venkatesh
Scott
Joann
Harvey
Gail
Allen
Zev
Jason
Jamie
Rich
Laurel
Joel
Linda
Affiliation
Columbia University
EPA National Center for Environmental Assessment
University of California-Davis
EPA National Exposure Research Laboratory
EPA Office of Air Quality Planning & Standards
EPA Office of Air Quality Planning & Standards
University of Washington
New York University
EPA National Center for Environmental Assessment
EPA Office of Air Quality Planning & Standards
EPA National Center for Environmental Assessment
EPA Office of Air Quality Planning & Standards
EPA Office of Air Quality Planning & Standards
EPA Office of Air Quality Planning & Standards
EPA Office of Air Quality Planning & Standards
EPA National Exposure Research Laboratory
EPA National Health & Environmental Effects Research
Laboratory
EPA National Exposure Research Laboratory
EPA Region 8
New York University
Johns Hopkins University
University of Iowa
EPA National Center for Environmental Assessment
EPA National Center for Environmental Assessment
Vermont Department of Environmental Conservation
Atmospheric and Environmental Research, Inc.
EPA Office of Air Quality Planning & Standards
South Carolina Department of Health and Environmental
Control
EPA Office of Air Quality Planning & Standards
EPA Office of Air Quality Planning & Standards
EPA National Center for Environmental Research
Carnegie Mellon University
ZevRoss Spatial Analysis
EPA National Center for Environmental Assessment
University of Wisconsin
EPA Office of Air Quality Planning & Standards
EPA Office of Research & Development
Harvard University
EPA National Exposure Research Laboratory
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Last Name
Stanek
Stevenson
Stewart
Stone
Sunshine
Thurston
Tikvart
Turner
Turpin
Vandenberg
Vanderpool
Vette
Watkins
Watson
Way land
Wegman
Weinstock
White
Williams
Willis
Wilson
Winner
Wyzga
Zimmer-
Dauphinee
First Name
Lindsay
Eric
Michael
Susan
Geoffrey
George
Joe
Jay
Barbara
John
Robert
Alan
Tim
John
Richard
(Chet)
Lydia
Lewis
Warren
Ron
Robert
William
Darrell
Ronald
Susan
Affiliation
EPA National Center for Environmental Assessment
Bay Area Air Quality Management District
EPA Office of Air Quality Planning & Standards
EPA Office of Air Quality Planning & Standards
Health Effects Institute
New York University
EPA Office of Air Quality Planning & Standards
Washington University in St. Louis
Rutgers University
EPA National Center for Environmental Assessment
EPA National Exposure Research Laboratory
EPA National Exposure Research Laboratory
EPA National Exposure Research Laboratory
Desert Research Institute
EPA Office of Air Quality Planning & Standards
EPA Office of Air Quality Planning & Standards
EPA Office of Air Quality Planning & Standards
University of California-Davis
EPA National Exposure Research Laboratory
EPA National Exposure Research Laboratory
EPA National Center for Environmental Assessment
EPA National Center for Environmental Research
Electric Power Research Institute
Georgia Department of Natural Resources
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Appendix B: Session I: Elemental and Organic Carbon Measurements -
Chemical Speciation Network (CSN) Carbon Issues
Questions on this draft white paper should be directed to Neil Frank, EPA/OAQPS,
frank.neil(g)epa.gov, (919) 541-5560.
Introduction
The purpose of this draft white paper is to provide an overview of urban and rural carbon
measurement protocols and to identify issues associated with data reporting and usage,
particularly with respect to the CSN transition to IMPROVE-protocol for carbon measurements.
This document will also serve as a discussion piece to gather input from the health research
community on related issues and next steps.
Background
State and local air agencies, under EPA grants, have been measuring organic carbon (OC)
and elemental carbon (EC) in urban areas since 2001. The network consists of 54 sites intended
to capture long-term trends (speciation trends network or STN) and approximately 150 other
State and local air monitoring stations (SLAMS). Collectively, the urban locations are part of
the EPA Chemical Speciation Network (CSN). To support the regional haze and PM2.5
programs, EPA also funds a largely rural network called IMPROVE. Together with support from
NFS and other Federal agencies, the IMPROVE network provides carbon measurements at
approximately 160 national park, wilderness, and other rural locations nationwide.
Two different thermal-optical analysis methods are currently used by the CSN and
IMPROVE networks for the analysis of carbon. The IMPROVE method is based on the Desert
Research Institute/Oregon Graduate Center (DRI/OGC) thermal-optical reflectance (TOR)
method27. The CSN method has historically used a modified version of the National Institute for
Occupational Safety and Health (NIOSH) 5040 thermal optical transmittance (TOT) method28.
The latter CSN method is different from the NIOSH method in that it has a different thermal
temperature profile. The CSN is transitioning to the IMPROVE sampling and analysis protocols
for carbon. Currently 56 sites have changed. Additional sites will be changed in the future.
Research has shown that differences in the thermal profile, optical correction
(transmittance versus reflectance), and specific analyzer used will result in differences in the OC
and EC values obtained (Schmid et al, 2001; Currie, et al., 2002). In addition, sampling and
sample handling differences also have an impact, especially for OC. Other carbon monitoring
networks and measurement studies (e.g. SEARCH, Supersites) use variations of the CSN and
IMPROVE protocols and are not discussed here.
For the first 6 years of CSN operation, urban and rural carbon have been collected with
different samplers and analyzed by different thermal optical methods. For chemical analysis,
CSN has used the NIOSH-type thermal optical transmittance (TOT) method for measures of OC
27 The current IMPROVE_A method for Organic and Elemental carbon is described by the Standard Operating
Procedure (SOP) provided at: http://vista.cira.colostate.edu/improve/Publications/SOPs/drisop2005.asp
28 The CSN method for Organic and Elemental Carbon is described in the SOP provided at:
http://epa.gov/ttn/amtic/files/ambient/pm25/spec/ocecsop.pdf
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and EC. IMPROVE measurements are based on thermal optical reflectance (TOR) with a
different thermal evolution temperature profile. Both measurement protocols provide
operationally-defined measures of OC and EC. The IMPROVE protocol generally results in a
lower OC/EC ratio and also provides 4 sub-fractions of OC (OC1, OC2, OC3, OC4), pyrolized
organic carbon (OP), and 3 fractions of EC (EC1, EC2, ECS). These sub-fractions have been
used in source attribution studies (Kim and Hopke, 2006). Starting in 2005, however, IMPROVE
switched to an upgraded TOR analyzer with more accurate temperature settings. This change to
"IMPROVE_A" results in approximately the same total OC and total EC but relatively different
amounts of the sub-fractions (Chow et al., 2007). The new IMPROVE analyzer also provides
TOT measurements which may have value in relating the two networks' data.
In addition, the two networks have their different approaches to address various sampling
artifacts and public reporting of its carbon data. While CSN collects field and trip blanks (but
with limited ambient exposure) at all sites as a measure of passively collected organic vapors
(positive artifact), the IMPROVE program uses longer duration field blanks at all its sites, and
additionally has deployed secondary carbon (sc), i.e. backup quartz filters, at 6 of their sites to
provide a network-wide measure of sampling artifacts. IMPROVE data shows that sc is greater
than field blank carbon (fbc) and IMPROVE uses the monthly median sc value (by carbon sub-
fraction) to correct the entire network's organic and elemental carbon values. The adequacy of
using 6 sites to represent the entire network is currently under review by IMRPOVE. IMPROVE
reports publicly only the artifact adjusted data. EPA has been publicly reporting in AQS carbon
data produced by the primary collection filters, and separately the carbon values for the field and
trip blanks.29 All field and trip blank data since 1999 are now available in AQS. Until CSN sites
are transit!oned to the new IMPROVE protocol, CSN sites are not measuring carbon on backup
filters. To correct for urban sampling artifacts, CSN data users have used the CSN fbc data
together with sampler specific flow rates to "blank correct" the reported CSN data and have also
used material balance or statistical approaches (Frank, Solomon, Kirn). These sampling artifact
procedures have also considered the need to differentiate particulate carbon collected on quartz
vs. Teflon filters. Some users have not made any adjustments in some work (Pun). The
correction for sampling artifact can be as much as 30% of the organic carbon, as reported at the
2006 HEI meeting in Boston (www.healtheffects.org/AQDNov06/AQDWorkshop.htmn. The
new CSN samplers whose quartz filters are analyzed with "IMPROVE_A" are deploying sc and
24-hr duration fbc filters. The potential use of those filters to adjust for sampling artifact is
currently under study. EPA is also exploring what adjustments can/should be applied to old-
protocol-CSN data to best correct for sampling artifacts and whether a single universally
acceptable approach or multiple approaches for artifact corrected data exists. A question to
pursue is: What is the impact of using CSN data that have not been corrected for sampling
artifacts in epi studies (i.e., inclusion of a large and variable positive bias which may possibly
have a seasonal component)?
To help understand the differences between CSN and IMPROVE carbon-protocol
measurements, EPA has collocated CSN samplers with IMPROVE samplers in various urban
and rural environments over a 1-3 year period (See Table 1). Because of the many separate
influences on carbon measurements (e.g., sampler, specific analytical method, and artifact
29 Arifact corrected CSN OC data, using network average fbc values, are available on
http://www.epa.gov/airexplorer
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correction), the only definitive data to show comparisons between CSN and IMPROVE are these
data generated by the CSN and IMPROVE networks. From data analyzed to date, the results
show that the IMPROVE-protocol EC is generally higher (+10 to +30%), except at 3 locations
(Phoenix and Tonto, AZ and Rubidoux, CA) where the IMPROVE EC is lower (-2 to -8%). The
average differences appear to vary by location and the difference may therefore be related to the
type or composition of the carbon aerosol. On the other hand, OC concentration is greatly
affected by sampling artifact, sampler flow rate and filter size, and therefore the inter-network
differences are more difficult to characterize. Application of simple adjustments, say using field
blanks, may not be sufficient to adjust CSN data to look like IMPROVE-protocol concentrations
(Flanagan). Chow, Watson (at DRI) and White (at UC Davis) are also examining this issue for
EPA and recommendations will be forthcoming.
National consistency in carbon measurements for source attribution, model evaluation
and urban-rural comparisons is very important. Starting in calendar year 2007, EPA began
transitioning the urban CSN to the IMPROVE analytical protocol, with an IMPROVE-like
sampler (i.e. URG3000N sampler, with identical PM2.s particle size separator, filter size and flow
rate, but with mass flow control) and will be employing secondary filters and 24-hr duration field
blanks to help estimate carbon sampling artifacts. Fifty-six sites have been established and
produced two months of collocated data during May-June 2007. Preliminary analysis of these
collocated data show similar IMPROVE-CSN relationships as discussed above. The transition of
CSN will continue in two additional phases. Phase 2 will begin early 2008 with the conversion of
about 65 sites and Phase 3 (the last phase of about 65 sites) will begin late 2008-early 2009. See
http://www.epa.gov/ttn/amtic/files/ambient/pm25/spec/faqcarbon.pdffor more information on
the conversion. EPA has also reorganized the parameter codes and data field definitions in AQS
to better differentiate current and future carbon measurement data according to collection
sampler, analytical protocol and adjustments if any for sampling artifacts.
Closing the Gap in EC Monitoring to Support PM Health Effects Research
The association between ambient concentrations of EC in PM2.5 and human health effects
is a subject of considerable interest. This section describes a number of possible steps that could
be taken towards minimizing the affects of CSN protocol changes on the ability of epidemiology
projects to report useful results for consideration in planned periodic reviews of the PM2.5
NAAQS. The purpose of this section is to facilitate communication about next steps along the
lines of these steps or alternatives that are more promising. In addition, the OAQPS Air Quality
Assessment Division (AQAD) convened an in-person workshop of CSN and IMPROVE
monitoring program experts and selected atmospheric scientists in January 2008 to discuss
outstanding issues related to EC and OC measurement in the two networks. The participants in
this workshop are continuing the discussion by e-mail and conference calls to develop a 1-2 year
research plan, possibly leading to changes in the operation of one or both monitoring networks
and/or the post-processing of their monitoring data.
In order to improve the data usability of EC for epidemiological studies and subsequent
PM NAAQS reviews, the following steps may be explored:
Possible Data Analysis Steps
1. Relating Old CSN TOT and New CSN TOR Data: Available data should be rigorously
analyzed to determine if there is a reasonably good method for predicting the former from
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the latter (including using sub-fraction information and possibly using site/day variables)
or vice versa. Data that includes the effect of the sampler difference should also be
analyzed. The purpose would be to determine whether double laboratory analysis of the
sort described in Items 2 and 3 below is actually needed to close the time series
discontinuity, versus relying on a mathematical conversion or algorithm.
a. There are some data sets available now that can be used to address this question.
Discussion Question: If new and old protocol CSN carbon data cannot be quantitatively
related or adjusted so that a consistent time series is available for OC and EC, can epi studies
use the unadjusted time series as long as it recognizes or accounts for the changes or
intervention(s) in the measurement process?
2. Epidemiological Sensitivity Analysis: After some number (TBD) of months of doubly-
analyzed samples are available from Items 5 and 6 below, epidemiologists should test
whether the two physical measures of EC are similarly associated with health effects of
interest. This would help determine the length of time and scope needed for items 4, 5
and 6 below.
Discussion Question: Can sensitivity testing of epi models be done to explore whether the
change in carbon measurements has a significant impact on results?
Possible Pilot/Investigative Studies
3. Sampling Effects on EC measurements: Test the hypothesis that sampler model,
artifact correction (subtraction of field blank or backup filter blank values), and quartz
filter brand (Whatman QMA vs. Pallflex) have a small enough effect on measured EC,
such that epidemiology studies can span discontinuities in these aspects provided there is
consistency in the EC thermal analysis, by taking second punches from relevant filters
already in cold storage. There are 517 filter pairs available from 56 sites in May and June
2007, each pair consisting of an old-CSN filter and a new-CSN filter. The two kinds of
filters have already been tested for EC once each, with the old and new laboratory
method respectively. Of these, 53 sites used the old CSN method with the MetOne
sampler, which has the flow rate most different from the new URG3000N sampler (-6.7
vs. -22.7 L/min). The sensitivity of EC to sampler type (independent of lab analysis) can
be tested by taking a second punch from the old CSN filters and analyzing them with
IMPROVE_A, and comparing the results to the IMPROVE_A result on the new CSN
filter. Alternatively or in addition, the comparison can be done the other way by taking
the extra punch from the new CSN filter. This comparison may not be indicative of
sensitivities during other seasons.
4. There are about 250 site-months of collocation data between old CSN TOT and
IMPROVE TOR (up until January 2005) and IMPROVE_A TOR (after January 2005),
spanning all seasons, in selected urban areas.30 Some of these filters could be analyzed a
second time, as described immediately above to evaluate sampling effects on EC
measurements.
Discussion Questions: What are the most important data assessment attributes or metrics for
the comparison to satisfy the needs for health studies? Can sensitivity testing of
30 Sites are in Atlanta, Birmingham, Allen Park, MI, Fresno, New York City, and Pittsburgh.
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epidemiologic models be done to explore whether the change in carbon measurements has a
significant impact on results?
Possible Gap-Filling Data Collection (Short and Long-term) to Extend the Continuity of
the OC/EC time series
5. At some number (TBD) of converted CSN sites of most importance to ongoing
epidemiology studies, analyze some number (TBD) of quartz filters from the new
URG3000N sampler with the old CSN-TOT method, in addition to the IMPROVE_A
measurements. Double analysis is possible because a single filter can usually allow three
separate analyses using three separate punches from the filter. This would give a
continuous time series using the old CSN-TOT lab method. There would be a
discontinuity in sampling method.
a. Same-time, double analysis would be implemented for newly collected filters as
they are received.31
b. Filters collected since conversion that have completed analysis would be retrieved
from cold storage and re-analyzed also.
6. (Additionally or Alternatively to Item 2 above) At some number (TBD) of converted
CSN sites of most importance to ongoing epidemiology studies, retrieve pre-conversion
filters from cold storage and perform a second analysis for EC using IMPROVE_A. This
would give a continuous time series using the new IMPROVE_A lab method. There
would be a discontinuity in sampling method.
7. Depending on the outcomes of steps 3 and 4, the number of sites subject to double
analysis could be reduced (because no important differences are discerned) or increased
to include more sites of interest (because it becomes clear that only consistent physical
measurements are useful.)
8. Presently, EPA has no plan for long term operation of any sites at which the old CSN
method (using the dominant old sampler type and the old TOT analysis protocol) and the
new CSN method (URG300N and EVIPROVE_A) are collocated. A possible step is to
establish some such sites and commit to their operation until these EC (and related OC)
issues are well settled. Depending on logistics and monitoring agency agreement, these
could be the same 6 sites where IMPROVE and one CSN samplers already operated on a
collocation basis.32
Discussion Questions: What are the number and location of sites that are of most interest or
importance to ongoing epidemiological studies? If long-term comparisons of old and new
CSN are needed, where should collocated measurements be obtained, and at what frequency,
and for how long? What are the most important data attributes or metrics for the comparison
of new vs. old CSN protocol measurements to satisfy the needs for epi studies (e.g.
sufficiently high correlation; consistent day-day and seasonal variability)? What is judged to
be sufficiently high correlation; what is "consistent" temporal behavior?
31 It is not urgent to begin this same-time double analysis because any filters tested only with IMPROVE_A can be
retrieved from cold storage later if necessary.
32 Sites are in Atlanta, Birmingham, Allen Park, MI, Fresno, New York City, and Pittsburgh.
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9. As presented by Frank at the 2006 meeting in Boston, carbon by material balance
between non-carbon species and FRM mass ("SANDWICH" technique) may have value
in providing a consistent and independently derived time series of carbonaceous mass as
measured on Teflon filters. This alternative indicator can minimally assist with quality
control of newly derived procedures. To help isolate the OC and EC portions of the mass
balance estimates, new measurements from archived Teflon filters may be needed to
compensate for network changes in the thermal optical procedures used on collocated
CSN measurements (e.g., optical measures of black carbon in combination with statistical
procedures to establish site specific correction factor for "EC").
What About Daily EC?
EPA ORD is investigating the feasibility of limited speciation on daily FRM collected
Teflon filters, where available. This may involve performing XRF analyses and possibly an
additional optical measure of black carbon in combination with statistical procedures to establish
a site-specific correction factor for "EC". This work has not started and is not expected to be
available by April 2008.
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Table 1. List of EPA CSN/IMPROVE Collocated Study Sites
Site Name
Atlanta
(Decatur), GA
Birmingham,
AL
Detroit (Allen
Park), MI
Fresno, CA
New York
(IS52), NY
Pittsburgh, PA
Houston, TX
Chicago, IL
Rubidoux
(Riverside), CA
Phoenix, AZ
Tonto NP, AZ
Seattle, WA
Mt. Ranier, WA
Washington DC
Dolly Sods, WV
AQS Site ID
13-089-0002
01-073-0023
26-163-0001
06-019-0008
36-005-0110
42-003-0008
48-201-1039
17-01-0076
06-065-8001
04-013-9997
04-007-0010
53-033-0080
53-053-0014
11-001-0042
54-093-9000
Urban/
Rural
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Urban
Rural
Urban
Rural
Urban
Rural
CSN Sampler
Andersen RAAS
until 1/2006 then
MetOne SASS
MetOne SASS
(URG3000N for
carbon May 2007)
MetOne SASS
MetOne SASS
R&P2300 until
1/2006 then
MetOne SASS
(URG3000N for
carbon May 2007)
MetOne SASS
URG MASS
URG MASS
MetOne SASS
MetOne SASS
MetOne SASS
URG MASS
URG MASS
Andersen RAAS
Andersen RAAS
Start
Date
4/2004
4/2004
1 1/2003
9/2004
8/2004
4/2004
5/2004
1 1/2003
9/2004
10/2001
10/2001
10/2001
10/2001
10/2001
10/2001
End Date
Ongoing *
Ongoing
Ongoing *
Ongoing
Ongoing
Ongoing *
9/2005
9/2005
9/2005
12/2003
12/2003
12/2003
12/2002
12/2003
12/2003
* Continuing with IMPROVE carbon aerosol measurements starting July 2005. Full IMPROVE speciation at other
sites.
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December 1, 2006 http://www.healtheffects.org/AQDNov06/AQD_Frank.pdf
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Mountains area; Science of the Total Environment: 368: 781-794.
Pun, B. HEI Air Quality Database. http://hei.aer.com/aboutDatabase.php
Schmid, Heidrun, Lothar Laskus, Hans Jiirgen Abraham, Urs Baltensperger, Vincent Lavanchy,
Mirko Bizjak, Peter Burba, Helene Cachier, Dale Crow, Judith Chow, et al. (2001). Results of
the "carbon conference" international aerosol carbon round robin test stage I. Atmos. Environ.
35:2111-2121.
Solomon, Paul. Organic Carbon Artifacts. Presented at Mid-Atlantic Regional Air Management
Assoc., June 29, 2004. http://oaqpswww.epa.gov/tom/wiki/pmteam/files/MARAMA PC Artifacts 6-29-
04 Conf Call.ppt
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Appendix C: Session II: Accessing Ambient Air Monitoring Data - Access to
EPA's Air Quality Data for Health Researchers
Questions on this draft white paper should be directed to Nick Mangus, EPA/OAQPS,
mangus.nick@epa.gov, (919) 541-5549.
Introduction
A common refrain from policymakers, analysts, and scientists is that obtaining the air
quality data which they need is a challenge. This paper outlines the current collection and
dissemination framework for air quality data and poses "charge questions" to the health-research/
epidemiology community. The answers to these questions will help us at the EPA improve our
offerings.
To frame the charge questions, this document describes a relatively new EPA system, the
AQS Data Mart, and contrasts it with the HEI Air Quality Database, which was put in place to
provide access to PM components and other data for health researchers. Finally, the charge
questions are presented.
Background
The collection, storage, and dissemination of air quality data is a complex process
achieved by a series of separate groups of hardware, software, and people. As technology has
advanced and the number of distinct sets of user groups (those with different data or analytical
needs) have proliferated, the problem for any individual finding precisely what they need has
only gotten more complex. Adding to this complexity are intermediate "value-added" providers
who may integrate, visualize, or otherwise post-process data from various sources. Thus, users
can invest in their own data gathering and processing or they can rely on an array of intermediary
providers. We also have data from special studies. The quality is (probably) high, but the data
may not be readily available to others. So, EPA will always be the provider of certain base data,
but we may not have it in the desired form, integrated with other desirable data (emissions or
population), or presented in the desired manner. There will always be the possibility for a value-
added provider to enhance the EPA data or integrate it with other data.
The following diagram is a simplified view of the components that accomplish the
collection and dissemination tasks at the EPA. It will be used to explain how data are collected,
stored, and provided by EPA and how the HEI acts as a value-added post-processor.
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AQS Data Flow Diagram
Monitors /
Analyzers
(in the
field)
Data acquisition,
LI aid
analysis, and QA collection
(State, tribal, and
local agencies plus
labs)
facilities
Inward & outward
facing databases
Rest of EPA
AQS Application
TIN Data Page
AirData
AQS Query
Trends !
NAA Designations
FOIAs & Requests j
AQSDM Direct Interface
Air Compare
Software Application
Process
EPA partners
Others
The main part of the diagram shows the major components of the EPA's Air Quality
System (AQS). Beginning from the left hand side, samples are collected in the field by monitors.
Some of these samples are analyzed in situ, others are collected by the State, tribal, or local
agency responsible for the monitor and analyzed at laboratories. Either way, the agency
responsible for the monitor is also responsible for ensuring the measurements are reported to
AQS. It should be noted that only monitors within the EPA national ambient air quality
monitoring network must have their data reported to AQS, for other monitoring networks or
special studies (e.g., The Texas PM2.5 Sampling and Analysis Study) it is optional and the
information may be stored in another system (e.g., NARSTO).
AQS is the EPA system designed to collect and store the monitored information. When
users are allowed unlimited access to download information from such collection systems, the
demands put on the system by voluminous requests can compromise the ability of the system to
fulfill its collection function. To alleviate this problem, software engineers developed the AQS
Data Mart which stores a copy of the information from the AQS and allows users to download
data. It is a generic "retrieval" tool that provides the ability to query any information, but it does
not provide significant data exploration or analysis capabilities. These capabilities are left to
downstream "value-added" tools.
EPA is in the process of transit!oning our user applications designed for downloading
information from the AQS database to the AQS Data Mart database. The right hand side of the
diagram represents the several places to query or download air quality information that EPA
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provides. Each has been targeted to a specific audience: the general public, data analysts, or
researchers. The diagram indicates which ones are still connected to AQS and the ones that have
been transit! oned to the AQS Data Mart. Note that the small cylinders by three of the systems
still getting their data from AQS indicate that they must copy data and store it separately so as
not to impose large loads on AQS. One of the advantages of using a data mart is to alleviate the
need to store these data again.
As an example, raw PM2.5 data collected by EPA is available to external users in three of
these EPA "front-ends". Large text files can be downloaded from our website (The TTN Data
Page at http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm). The AirExplorer
site can be used to query, plot, and map these data. Finally, the Data Mart Direct Interface can
be used to query the data. Each of these tools has advantages and disadvantages depending on
the needs of the user. For more information about all of the front-ends listed in the diagram,
please see Appendix A.
Beyond AQS and the related EPA systems, there are many other stakeholders involved in
the collection and dissemination of air quality data, each with their own activities and possibly
systems. AQS is likely the largest repository, but there may be additional information of interest
to health researchers stored in other places. These additional stakeholders are represented by the
other "layers" in the diagram. Elsewhere in EPA there are data collection and dissemination
systems (CASTNET and AirNow in the Office of Air and Radiation; RSIG and PHASE in the
Office of Research and Development; and Environmental Geoweb in the Office of
Environmental Information). Additionally, EPA has other systems that present public and
management views of air quality data.
The next layer out represents EPA partners, those who operate in cooperation with EPA,
like the Health Effects Institute, Colorado State University, etc. who maintain data dissemination
systems (many that integrate data from outside of AQS). Also in this layer are special studies
(DEARS, NMMAPS, etc.) that manage the full lifecycle of air quality data management from
collection to dissemination. Generally these non-governmental partners and EPA communicate
with each other and the action that one takes may influence the other. Considering again the
PM2.5 example, the HEI Air Quality Database uses the EPA provided data for PM and the nearest
gas phase monitors, and integrates EPA emissions and non-EPA population and meteorological
information. This is a value-added service to provide a custom-tailored solution to a specific
community. Finally, there is the layer entitled "Others," which represents those stakeholders
who operate independently. These are the "unknown unknowns" in terms of additional data that
may be collected or made available.
Each of these groups brings with them a different list of what they can do easily, what
they can do with difficulty, and what they cannot do. That is, each provides a degree of
flexibility or constancy that makes them the best at providing a particular product or service.
Collaboration, building on the strengths of each organization, is critical and one organization
may have to take up the role of integrator and communicator so the research community knows
where to get vital information. That is, if a clearinghouse listing all available databases, datasets,
and access systems is needed, someone will have to manage its creation and maintenance.
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The remainder of this paper discusses only one EPA access mechanism, the AQS Data
Mart Direct Interface, which was designed specifically to address the needs of the research
community. EPA perceived these needs as primarily the ability to locate and extract large sets of
data. The Data Mart was made available for internal EPA use in mid-2006 and for external use,
along with the Direct Interface, in early 2007. Use has been growing steadily since then.
Overall, it has been well received by most of those who have accessed and used it. Initially a
pilot project, the reaction from users has been positive enough that EPA management has
committed to ongoing support for the system. Most of the negative reaction falls into two
categories: the user friendliness of the system and the documentation of the data. To address the
first, we continue to add features and improve usability to make the Data Mart as friendly as
possible to the research community. Documentation of the data is not a problem inherent to the
Data Mart, but we realize it is much needed, so we are also addressing this as we can.
The remainder of this paper will introduce the Data Mart Direct Interface, compare it to
the HEI Air Quality Database, and place "charge" questions to the research user community to
help us continue to improve these systems to meet your needs.
Contents of the Data Mart
The Data Mart contains every measured ("raw") and aggregated ("daily and annual
summary") value reported to AQS from January 01, 1980 to the present. It also contains all of
the same site and monitor descriptive data and measurement metadata in AQS. We have
converted most data-entry codes to plain English words to help with the interpretation of
downloaded data.
There are no additional quality assurance steps performed on the data in the Data Mart, as
the data in AQS are generally considered to be of the highest quality. Data must undergo many
quality control steps as part of the loading process before it is saved in the AQS database.
Likewise, submitters are required to assure that the monitor is operating properly and has passed
precision and bias checks before loading the data. Finally, each year, EPA and the submitter
review the data for completeness and correctness before the data are "certified" for regulatory
use.
It should be noted that IMPROVE (visibility network) and SANDWICH (modeled PM2.5
species) data are not generally reported to AQS. However, EPA staff has recently loaded the
IMPROVE data for 1988-2005 into AQS and the loading of SANDWICH data is planned. As of
January 14, 2008, there were 1.67 billion raw measurements for 885 different parameters in the
database (there is a profiling spreadsheet under the documentation section of the web page).
The Data Mart is refreshed from AQS each weekday night, so it always has the latest
available information. However, since data up to 4 years old can be submitted to AQS at any
time, and there are special windows for "historical" data updates, any of the contents can change
at any time. That is, there is no freezing or snapshotting of data into a static version in the
database.
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Accessing the AQS Data Mart
The AQS Data Mart can be accessed by visiting the webpage,
http://www.epa.gov/ttn/airs/aqsdatamart, and following the "Access" link. Registration is
required, and a user ID and password needed for access. You may sign up for your own account
or use a guest account with user = aqsdatamart@epa.gov and password = AQSdatamartl (case
sensitive). Access is provided by an application that you can either run in your web browser or
download and run on a PC. The application is used to submit a query. A query lets the user
select the geography, substance (parameter), time, metric, and optional data to return. The Data
Mart currently has five queries, summarized below.
Query
Values
Monitor
Annual
Summary
Raw Data
Sites by
Threshold
Description
Recommended, returns any single raw, daily, or annual variable with metadata and is very
efficient
Returns descriptions of the monitoring site and equipment
Returns all annual summary aggregate statistics for the monitors selected
Returns raw data in the AQS transaction format - recommended only for AQS users
Returns a list of sites that meet a specific data-related threshold that you specify
When the query is complete, results can be downloaded using the application or by
following a link in an email message sent to the user. All output is in XML format, but with
embedded links to stylesheets for user-friendly display.
The Data Mart is intended as an extraction system only and EPA does not plan to provide
analytic or graphical capabilities with the Data Mart. However, some of the other tools that EPA
provides do have these capabilities (see Appendix A for details).
Contents of the HEI Air Quality Database
In September 2005, a group funded by the Health Effects Institute (HEI) and led by
Christian Seigneur and Betty Pun at Atmospheric and Environmental Research (AER) launched
a website/database to facilitate health effects studies that require detailed knowledge of air
pollutant levels and other relevant information at selected sites across the US. The HEI Air
Quality Database combines information on PM2.5 components collected at monitoring sites in the
Chemical Speciation Network (CSN); meteorological variables; and levels of gaseous pollutants
(SC>2, Os, NOx, and CO) from monitoring sites at or near each CSN site. Metadata are provided
for each monitoring site, such as its geographic coordinates, state, as well as county, city location
information, population, and emissions data for nearby point, area, and mobile sources. AER
updates information in the HEI Database every few months and is currently funded to do this
through 2008.
Accessing the HEI Air Quality Database
The HEI Air Quality Database can be accessed by visiting the webpage,
http://hei.aer.com. Once you obtain an account by following the instructions on this page, you
can access the site browser and list building, database queries, and users' guides. The general
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data retrieval process consists of four steps: browsing sites, defining and saving a list of sites,
extracting data for the sites in a saved site list, and, downloading the extracted air quality data.
Comparison of AQS Data Mart and the HEI Air Quality Database
The HEI Air Quality Database represents a value-added service over what EPA provides
for a scientist looking for specific speciated PM2.5 data to evaluate in health research studies. So,
a natural starting point for such a user would be the more tailored FIEI system. If, however, that
system does not have some particular information that the user needs, they can revert to using the
EPA system. The EPA system is broader, but less refined; the closer the user gets to the source,
the more raw material they must process to get a finished product. The following table compares
some of the features of the HEI Air Quality Database and the AQS Data Mart to illustrate some
of these trade-offs.
Feature
Site browser
Site finder
Query from saved list
Query by any geography
Air quality data for PM2 5, O3,
CO, NOx, NO2, & SO2
Air quality data for all other
parameters
AQS met data
Integrated non-EPA met data
Emissions data
Census data
On-line help
Off-line help
File format
Data returned in one file
Update frequency (versions)
Build your own query
HEI Air Quality Database
Yes, with maps to help
Yes, with multiple-variable filter
Yes. Station lists may be saved and
re-used
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
CSV
No
Quarterly
Yes
AQS Data Mart
No
Yes, via a single-variable "sites by
threshold" query
No. Query based on geography and
parameter or single site
Yes
Yes
Yes
Yes
No
No
No
No
Yes
XML (CSV planned)
Yes
Daily
No
To summarize the key differences:
. The HEI interface is more tailored to the PM2.5 analyst.
. The HEI interface contains emissions, census, or NCDC meteorology data, the Data Mart
does not.
. The Data Mart contains all ambient data reported to AQS (not just PM, meteorological,
and NAAQS gases).
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. The Data Mart only contains special studies data (e.g., supersites) if it has been loaded
into AQS.
Interpreting the Data
Between data element names, report headings, and data transfer formats, there are almost
2000 named data elements relating to air quality that EPA makes available. In addition, some of
the values in those fields need individual documentation to properly describe them (for example,
what is the difference between a SLAMS and a NAMS monitor type). To help the user identify
and perhaps understand the data they have, the EPA created an annotated, cross-referenced index
called the "Field Guide to Air Quality Data". It is available in the documentation section of the
Data Mart web page. There is also a list server that can be used to ask questions or monitored
for system status.
Charge Questions - Introduction
To help prioritize and define future activities so that we can better meet the needs of the
members of the research community, EPA has compiled a list of "charge questions" for invitees
to this conference to consider. The overarching issue is connecting the data users to the data
providers. For EPA and our partners to improve on this, we need to fully understand the data
needs of the health research community. The more specifically the needs can be elucidated, the
more concrete actions that can be taken to improve the situation. We are interested in feedback
from users and potential users of air quality data and retrieval tools. This paper is concerned
only with access to existing data; possible new data collection activities are covered elsewhere.
Standout Charge Questions
In previous interactions with data users and the health research community, three questions
repeatedly come to the forefront as seemingly ubiquitous and critical. These issues are also at a
high level and decisions on them will potentially impact decisions on the other charge questions.
To complicate matters, there is not a single unifying idea that all agree is progress in the right
direction on these issues. Thus, these questions are presented in more detail and with possible
solutions to initiate discussions.
1. Data versioning/snapshotting: How often should EPA release data and how should we
indicate that it has changed? The EPA, HEI, and others currently provide data via many
applications. The data in those applications are generally updated on a schedule or as
new data become available. For example, the AQS Data Mart is updated every day with
new submissions and changes to AQS. However, new data or changes coming into AQS
may be 10 years old. So a value in the AQS Data Mart representing a sample taken in the
late 1990s may change today. Likewise, the HEI Air Quality Database is generally
updated as the EPA makes new AQS "flat file" data extracts available on our web sites.
This is usually done quarterly and without notice, thus the HEI database changes about
quarterly; and the same 10 year rule applies. The key difference is that if you get data
from the AQS Data Mart and your colleague gets the "same" data the next day, the data
may have changed. If you are using the HEI database, the data may also have changed in
one day, but the odds are less and the data vintage is clear in the "about" pages of the
website. The stability of data for verifying and comparing research is essential, so the
charge question is this: How often should EPA release data and how should we
indicate that it has changed? One solution to this issue is to only make new data
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available outside EPA once per year. These data would be released on Independence
Day and would be up-to-date through the prior year. This option provides greater
stability to the data but may not be timely enough for particular studies or NAAQS
revisions. A second solution is for EPA to continue to release data as it is received. Each
value would be date-stamped with the date it last changed along with the date it
represents. This allows for comparisons of data sets but requires more data to be
downloaded and analyzed by the user. There are many intermediate options that could be
implemented.
2. Topic-focused portals: Are topic focused portals needed for air quality data? If so, what
should those portals be and what should they contain? A strength of the HEI Air Quality
Database is that it is geared towards health researchers evaluating speciated PM2.5 data
and the user interface provides tools and information specifically targeted to this user.
The AQS Data Mart, on the other hand, is generic and targeted at anyone wishing to
download air quality data. An annotated map of the PM2.5 speciation sites on the HEI
page helps the user understand and find the data they need. An analogous map of all
5,000 sites represented in the AQS Data Mart would only overwhelm and confuse users.
Custom tailored "portals" into data, like HEFs, are very helpful to the user, especially
when they have an interest limited to less than everything available. The EPA is
reasonably good at providing data but is often constrained in the technology we can use
to provide descriptive and analytical tools. Likewise, we are sometimes not able to
quickly secure funding to add tools to respond to developing areas of interest. This may
be a place where the flexibility of external organizations can be used to provide a more
custom, and therefore useful, experience. Are topic-focused portals needed for air
quality data? If so, what should those portals be and what should they contain? For
example, there could be portals specific to PM2 5 speciation, ozone and precursors, toxics,
organic compounds, etc. Given the new technologies, a portal that resides outside of the
EPA can have live access to a single, consistent, stable database within EPA.
3. Accessibility of non-AQS data: The AQS Data Mart stores data from the national
ambient air quality monitoring network(s) and, as previously mentioned, has recently
begun to add some data from other networks and "special studies." Is it important to
have access to data from local, short-term, air quality special studies? Examples
include MESA-Air, DEARS, Supersites, and ultrafme particle projects. If these data
should be included, how should it be done? For example, to be loaded into the AQS Data
Mart the data must match the monitor paradigm (no remote sensing or mobile monitors),
it must meet format and quality requirements, and it must have associated descriptive
data (e.g., method used, sampling schedule). Getting new data to match EPA's data
standards are often labor-intensive activities - are they worth it? Would EPA have to
correct and load these data into the AQS database (or a "research" copy of the AQS
database)? Would EPA be able to commit the resources to doing this? As an alternative,
EPA can provide information to managers of new studies about the data format and
content standards we have so that the data can be collected in a way that could be more
easily shared and compared with AQS data or other new data collected using the
standards. If this special study data remains outside of EPA systems, is there a role for a
clearinghouse? The clearinghouse could keep an up-to-date list of monitoring efforts,
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databases, contents, and appropriate uses. Issues to be considered include: how resource
intensive would this effort be and who would develop and maintain this clearinghouse?
Other Charge Questions
The remaining charge questions are more straight-forward than the standout charge
questions. They are related to how individuals gather and use data rather than community-wide
concerns.
4. What are the key data that you need? Is any of this currently collected but not
available?
5. Is there a particular way that you need data organized, grouped, or formatted?
6. What data elements other than measurements do you need?
7. What is the typical domain of the data you need (time, space, and parameter
selections; for example, 3 years, several cities, and 4 parameters; or 1 year, national,
44 parameters)?
8. Are there "profiling" reports - descriptions of which sites collect which data, how
complete the data are, etc. - that you need?
9. Would you rather query a database or have a large list of files that you can select
from to download (like
http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm but with more
geographic resolution)?
10. What would your ideal query builder/interface look like?
11. Are there pieces of data that we provide or questions that we ask that confuse you?
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Appendix C.I- Other Data Access Mechanisms
EPA has many places to access air quality data. Each of these websites or applications was
designed for a specific target audience, for example, the general public concerned with acute health
issues, the general public concerned with long-term air quality where they live, the general public
interested in air quality comparisons between multiple locations (for living, vacationing, etc.), data
analysts concerned with regulatory compliances, data analysts contributing to policy decisions, and health
researchers. We consider a researcher to be someone who is looking to download raw data; either in large
volume or in small, discrete sets that are difficult to tease out of large published datasets. Each of these
websites or applications presents a unique front end for queries, charts, or maps that are geared toward
their target audience.
EPA is developing a "portal" to list all of the sources of air quality (and emissions) data that are
available and link directly to their access pages. This portal is at the following web address:
http://www.epa.gov/oar/airpolldata.html. Below is a table comparing key information about each of the
available EPA-maintained systems for air quality data (including AirNow and CASTNET which contain
data not in AQS). The systems are described at the link above. (Key: a filled circle means "yes" and an
empty circle means "some".)
System
AQS
AirNow (Tech)
AirData
AirExplorer
AirCompare
AQS Data Page
NATA (modeled)
Air Trends
AQS Data Mart
CASTNET
Level of
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Appendix D: Session III: Ambient Air Monitoring for Health Research - Air
Quality Sampling: Benefits and Costs of Daily Health Targeted Monitors for
Fine Particle Components
Questions on this draft white paper should be directed to Dr. Barbara Glenn, EPA/NCER,
glenn.barbara@epa.gov; (202) 343-9721.
INTRODUCTION
EPA's air quality datasets are generally recognized and valued primarily for their use in
ascertaining compliance with the National Ambient Air Quality Standards (NAAQS), developing
State Implementation Plans for the improvement in air quality, and providing timely air quality
data to the public. EPA's air quality datasets also are essential to extramural and intramural
health research addressing scientific uncertainty related to the current NAAQS and to the
assessment of the possible health benefits of any new air quality standard. These health-related
uses of EPA's air quality datasets are an important consideration in the design and conduct of the
national air quality monitoring network. As specialized monitoring networks have begun
providing information on the composition of particulate matter, epidemiologic researchers are
striving to address a major research priority defined by the National Academy of Sciences
National Research Council (NRC)—assessing the health effects of PM components and sources.
The NRC reports on "Research Priorities for Airborne Particulate Matter" repeatedly
emphasize the importance of research to assess the relationships between particle composition
and health responses. According to the fourth report, "Progress on assessment of hazardous PM
components is central to the national research portfolio and to any refinement of the current
mass-based NAAQS for PM... .A better understanding of characteristics that modulate toxicity
could lead to targeted control strategies specifically addressing those sources having the most
significant adverse effects on public health." (NRC 2004)
On November 30, 2006, the Health Effects Institute (HEI) and EPA, in conjunction with
the annual EPA PM Centers meeting, convened a meeting of the research and air quality
management communities to discuss the use of EPA's air quality datasets for health research on
particulate matter (PM). Participants raised several issues that complicate the design and
interpretation of epidemiologic research on PM2.5 mass, components, and sources. Participants
emphasized that the lack of daily concentration measurements for fine particle mass and
components in key locations was severely affecting their ability to design and conduct
epidemiologic studies that would address issues of scientific uncertainties highlighted in air
quality standard setting at Federal and state levels. This paper summarizes these issues,
proposing a range of options to address the need for daily data based on the November 2006
meeting and subsequent information exchange with EPA grantees, state/local air quality
monitoring representatives, HEI and EPA staff. These challenges would not exist if resources to
collect daily, speciated monitoring data were readily available. The pressing needs for these data
are increasing at a time when resources are decreasing and monitoring costs are generally
increasing. This draft white paper provides background information to facilitate a broad
discussion on the benefits of obtaining daily fine particle speciation measurements and to
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encourage health researchers and air quality experts to work together to creatively identify
solutions that address the need for daily data while understanding the resource constraints and
competing needs for monitoring data.
The goal of this discussion paper is to draw attention to the importance of specific
monitoring data needs in planning health research studies, specifically related to evaluating
potential public health impacts of fine particles. Epidemiologic studies relating daily variation in
ambient air pollutant concentrations with disease-specific mortality or morbidity have been very
important for providing the scientific basis for recent standard-setting for PM. Some of the key
policy relevant issues considered in evaluating the PM NAAQS include:
• What are the potential public health impacts associated with exposures to specific size
fractions, chemical components, sources and/or environments (e.g., urban and non-urban
areas) of PM?
• What is the relationship between various health endpoints and different lag periods (e.g.,
less than one day, single day, and multi-day distributed lags)?
• How does spatial and/or temporal heterogeneity of PM exposures vary with different
size fractions and/or components?
Providing daily ambient air monitoring data for fine particle components from several
cities to health researchers would reduce exposure misclassification, allow the use of all health
events in statistical analyses, and thereby increase the precision of risk estimates. In addition, the
availability of these data would significantly decrease the length of time necessary to produce
study results.
BACKGROUND
Epidemiologic studies of the adverse human health effects of short-term exposures to air
pollutants have generally relied upon air quality monitoring systems established to ensure
compliance with ambient air quality standards. These epidemiologic studies contributed to
decisions in 1987 to change the indicator for the PM NAAQS from total suspended particles
(TSP) to PMio and to decisions in 1997 to add new standards to consider fine and coarse
fractions of PMio separately, using PM2.s as the indicator for fine particles and using PMio as the
indicator for purposes of regulating thoracic coarse particles.. As regulatory efforts have
increasingly focused on reducing the mass of fine particles from combustion sources, the air
quality monitoring network has successfully responded, at considerable cost and human effort, to
the monitoring challenges. Since promulgation of the fine particle NAAQS in 1997, subsequent
epidemiologic and toxicologic research has confirmed the earlier scientific findings and
validated the substantial investment in ambient air monitoring.
The 2004 Air Quality Criteria Document (CD) for PM highlighted the importance of
epidemiologic studies in its evaluation of the scientific evidence. In particular, the CD
emphasized new multi-city studies that investigated the effects of short-term human exposures to
PM on mortality and morbidity using data from multiple locations with varying climate and air
pollution mixes. These epidemiologic studies were valued because they provided information
about areas not previously studied, reported risk estimates for all study locations, and used the
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same analytical approach at each location allowing comparisons. In addition, multi-city studies
contributed to an increased understanding of the role of various potential confounders, including
gaseous co-pollutants, on observed associations. These studies, which combined risk estimates
across all locations, provided more precise estimates of the magnitude of an effect of exposure to
PM than most smaller-scale individual city studies because of their larger sample size. Because
model results were reported for all study locations regardless of the magnitude of the observed
risk estimate, these studies also avoided the potential for publication bias.
The National Mortality and Morbidity Air Pollution Study (NMMAPS) was the first
multi-city time-series study of air pollution and health and serves to illustrate the data-
availability issues highlighted in this discussion paper. NMMAPS, funded by HEI, evaluated
associations between daily mortality rates in 90 U.S. cities with the largest population and the
daily level of PMio reported for that locality in the EPA Air Quality System (Samet et al, 2000,
Dominici et al, 2003). Mortality data from 1987 to 1994 was obtained from CDC's National
Center for Health Statistics. While location-specific risks were reported, the objective was to
construct precise national and regional estimates of mortality risk from daily changes in ambient
PM and other criteria pollutants, thus increasing confidence in the values of the disease-specific
risk estimates, and that these estimates were representative of those experienced by the U.S.
population as a whole. In 14 cities with daily monitoring on at least 50% of study days,
NMMAPS also evaluated the association of hospital admissions with PMi0 (Samet et al., 2000;
Schwartz et al., 2003).
Despite its national scope, NMMAPS was limited by the amount of air quality data
available for analysis. The 90-city mortality analyses were based on air quality data in the AQS
primarily collected using l-in-6 day sampling schedules. The 8-year mortality dataset was
necessarily restricted to only those days where PMio data were available between 1987 and 1994
in each county. County-specific mean PMio concentrations were calculated for each day with
PMio measurements contributed by one or more monitors. Almost half (43) of the 90 cities had
data from only one or two monitors and only 28 cities had the equivalent of two or more years
(730 days) of monitoring days available. The dataset for the 20 city analysis of PMio adjusting
for other pollutants was further restricted for multi-pollutant models because data on all
pollutants had to be available on the same day. Consequently, these adjusted risk estimates were
less precise. Even with these limitations, the risk estimates were determined to be robust in
several sensitivity analyses to investigate residual confounding and exposure misclassification.
An NMMAPS sub-study often cities with PMio monitoring on a daily schedule (New
Haven, Birmingham, Pittsburgh, Detroit, Canton, Chicago, Minneapolis/St. Paul, Colorado
Springs, Spokane, and Seattle) systematically evaluated the potential bias associated with the use
of a single day PM concentration (Samet et al., 2000, Appendix B, pp. 54-61). Mortality data
was fit using a generalized additive Poisson regression model and polynomial constrained and
unconstrained lag models for ambient PM. Risk estimates from these models were compared to
those obtained using 1 or 2 day means. Overall effects estimated using the distributed lag models
were larger compared to effects estimated using the single or two-day mean lags. These analyses
showed that the mortality effects of an increase in pollution levels on a single day are spread
over several succeeding days, or conversely, that deaths on a single day are the result of pollution
over several preceding days. For the 90-cities, the NMMAPS mortality analyses estimated
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increased mortality per each 10 |ig/m3 increase in daily PMio using a specified lag structure (0, 1
or 2 days prior to the day deaths occurred). The 10 city sub-study using distributed lags
demonstrated that the larger multi-city study underestimated the risk associated with PMio. In
addition, consistent with previous reports, the NMMAPS results for morbidity in 14 cities
demonstrated that use of PMio concentrations on a single day (a one day lag) resulted in an
underestimation of the cumulative PMio effect on hospitalizations. Therefore, the flexibility to
analyze effects in relation to PM concentrations over several days is key to a complete
understanding of the magnitude of risk and the relevant time period for exposure to PM
components.
Many of the other times-series studies reviewed in the CD also relied on l-in-6 day PM
measurements including the Canadian eight cities study (Burnett et al., 2000; Burnett and
Goldberg, 2003). The 2004 CD was able to cite very few studies that relied on daily ambient
concentrations for PM indicators. As a consequence, the CD discussed in depth the trade-offs of
increased representativeness and precision provided by the larger, multi-site studies with the
increased uncertainties in the reported risk estimates due to exposure misclassification.
MISSING AMBIENT CONCENTRATION DATA AND STATISTICAL POWER
Data collection frequency is a key component of statistical power for time-series studies,
and missing data results in increased uncertainty in study results (discussed in PM Staff Paper,
Dec. 2005, p. 3-39 and CD, p. 9-41). The Staff Paper concluded that, "consistent with the CD's
observation that uncertainty is increased in studies using infrequently collected PM data, staff
judges that greater weight should be placed on those studies with daily or near-daily PM data
collection in drawing quantitative conclusions." Daily PM measurements in locations where
enough health events occur will support future health studies that reduce uncertainties and
thereby improve our understanding of the public health impacts of PM. Such studies will
provide important information on specific components within the ambient mix of particles to
inform the review of the PM NAAQS and strategies to implement these standards.
Statistical Power and Potential Bias
The statistical power of any proposed study is the probability that the completed study
will correctly reject the null hypothesis with a specified confidence level, usually 95%
confidence or a p-value of less than 0.05. In the calculation of statistical power, one must
specify the expected magnitude of the exposure-health association, the expected exposure
gradient, the sample size, the variability of the health outcome measure, and the specified
confidence level. The minimum desired statistical power is usually 80%, and the formula may
be inverted to calculate the necessary minimum sample size for a specified statistical power.
Generally, increased exposure variability is associated with an increased exposure gradient and
with increased statistical power. However, exposure variability due to measurement error and
unmeasured exposure variability does not increase statistical power. For many epidemiologic
study designs, sample size is directly related to the person-time of observation. For time-series
studies of air pollution in a large metropolitan area, the usable sample size is largely determined
by the number of days with complete exposure information. The variability of the health
outcome measure depends on the type of measure (e.g., mortality counts or continuous biological
indicators) and on the precision with which the outcome is measured. For time-series studies of
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the daily variation in mortality for large urban areas, the variability of the outcome measure is
largely determined by the average number of daily deaths. Everything else held constant,
statistical power for a time series study increases relative to the square root of the average
number of daily deaths. Hence, the weights in tables 1 and 2 are the square root of the estimated
number of daily deaths, or 1% of the population (In the US, about 1% of the population dies each
year) / 365.
For air pollution time-series studies, when air quality measurements are missing, the
deaths or heart attacks that would be studied for those days must be excluded. Therefore, the
sample size (number of mortality-days) available to analyze in a locality is reduced because the
exposure data may not exist for the desired lag days. For example, if one wants to evaluate
deaths in relation to ambient pollution levels on the same day, the day before, and the day before
that (lags 0, 1 and 2), then air pollution concentrations must be available at that location for three
consecutive days. Statistical power has implications for the selection of cities in epidemiologic
studies. The cities that can be analyzed become restricted to locations with a high number of
daily events (e.g., deaths, hospital admissions, etc.) so that the required sample size will be
obtained in a reasonable time period.
Dr. Kazuhito Ito, NYU, presented a slide at the November 2006 meeting that showed
statistical power curves for hypothetical time-series studies by the number of daily deaths in a
location and study duration (see Figure 1). These curves indicate statistical power achieved to
evaluate a hypothesized increase in daily total (non-accidental) deaths of 2.5% per 25 |ig/m3
increase in PM2.5. The hypothesized increase in mortality rate was derived from city-specific
relative risks reported in the PM literature. With l-in-3 day monitors, six years of monitoring
data would be required to achieve 80% statistical power to evaluate non-accidental mortality in
cities with 100 mean daily deaths (6 years x 365 days/year x l/3 monitoring days = 730 days). For
example, a study in New York City, with 180 to 200 deaths per day, would achieve 80% power
to evaluate the effect of PM2.5 on nonaccidental mortality in about three years (3 years x 365
days/year x % monitoring days = 365 days). Conversely, a study conducted in a city the size of
Seattle, with about 30 deaths per day would not achieve 50% power even if the study were
extended beyond six years! Furthermore, study power decreases when the focus of study is
cause-specific mortality or the identification of susceptible subgroups.
For many fine particle components, temporal and spatial variation within and between
localities may be different than the variation for PM2.5 mass. With greater temporal exposure
variation and spatial variation between cities, the statistical power to study specific fine particle
components is likely higher than for PM2.5 mass. Statistical power is also enhanced if the
mortality risk associated with a specific component is higher than for PM2.5 mass.
However, exposure error caused by uncertainties in a study's exposure assessment can
result in an attenuation of risk estimates and an inability to reject the null hypothesis. Exposure
error also may lead to biased risk estimates. Within a metropolitan area, some PM ambient air
measurements, such as total PM2.5 mass or sulfates, show less spatial variability and a
metropolitan area may be well characterized by a single central-site monitor. Other PM2.5
components, such as elemental carbon, show considerable spatial variability and a metropolitan
area may not be well characterized by a single central-site monitor. However, this uncertainty in
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exposure assessment related to the unmeasured spatial variability will be differential with respect
to PM2 5 component; and PM2 5 components with less spatial variability would have less
exposure uncertainty. For a multi-city study focused on regional air pollution gradients, city-to-
city differences in exposure uncertainty related to monitor location could affect city-to-city
differences in the observed associations with health outcomes and hence could be misinterpreted
as related to city-to-city differences in PM components.
Within the PM2 5 ambient air monitoring network, there are approximately 900 Federal
Reference Method (FRM) filter-based samplers that provide 24-hour PM2.5 mass concentration
data and about 600 continuous PM2 5 mass monitors that provide hourly data on a near real-time
basis. Due to the complex nature of fine particles, EPA implemented the Chemical Speciation
Network (CSN) to better understand the components of fine particle mass at selected locations.
Chemical speciation measurements are made at 54 "Speciation Trends Network (STN)" sites
that are intended to remain in operation indefinitely and about 150 other, potentially less
permanent sites used to support State Implementation Plan (SIP) development and other
monitoring objectives.33 Specific components of fine particles also are measured through the
Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring program
which supports regional haze characterization and tracks changes in visibility in Class I areas as
well as many other rural and some urban areas. Together, the CSN and IMPROVE data provide
chemical species information for fine particles that are critical for use in health and
epidemiologic studies to help inform reviews of the PM NAAQS.
The cities where the CSN monitoring sites are located are very important for studying
health effects associated with fine particle exposures. There are more than 200 sites in the CSN.
Table 1 (see associated pdf file) lists the Primary Metropolitan Statistical Areas (PMSAs) and
Metropolitan Statistical Areas (MSAs) ordered by population size and a weight determined by
the contribution to precision that area would make to a statistical analysis of risk. The site
locations for CSN monitors are listed within the relevant PMSA where they operate. It is
encouraging that more than 50% of the U.S. population resides in census areas with at least one
CSN monitor. There also are some large population centers where PM components are not
measured such as Orange County and Oakland in California, northern New Jersey, and Long
Island, New York (PMSAs and MSAs where no speciation monitors are located are highlighted
in red on Table 2). Currently, the CSN sites measure fine particle mass and components every
third day or every sixth day. A change to daily sampling would increase the statistical power for
time-series studies.
In evaluating criteria for identifying potential locations for increased monitoring,
consideration could be given to CSN locations representing varied fine particle sources in the
eastern, western, mid-western and southern parts of the U.S. Future epidemiologic studies that
examine PM exposures at or below the current level of the PM2 5 NAAQS will contribute
significantly to reducing scientific uncertainty concerning health effects. Most major US
metropolitan areas are below or close to the current PM NAAQS and can contribute useful
information on the public health impact of PM exposures. The collection of daily measurements
for PM2.5 mass and key PM2.5 components in metropolitan areas with high numbers of deaths and
33 See http://www.epa.gov/ttn/amtic/speciepg.html for more information on the PM2 5 speciation monitoring
program.
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in locations with variation in ambient concentrations near the current standard and in the mix of
PM2 5 components would inform our understanding of the relative health significance of specific
PM2.5 constituents and sources of PM2.5.
MISSING DAYS: IMPACT ON VALUE OF RISK ESTIMATES
Missing days of sampling data presents problems for epidemiology studies of health
effects associated with PM exposures over and above a decrease in statistical power and the
problems are compounded for studies of fine particle components. PM2.5 components are likely
to present a large degree of variation involving associations with different health endpoints and
time from exposure to response. This requires flexibility in constructing statistical models and
lag structures. In addition, variation between localities presents complexities in the interpretation
of the results of multi-site time series studies. Different components predominate in different
regions of the U.S. and the correlation between PM components in each area will vary. Finally,
the temporal and spatial variability of each PM component of interest within a city will vary and,
if not adequately captured in sampling data, will result in exposure misclassification and an
effect on the value of risk estimates.
The NMMAPS study and other time-series studies of mortality and morbidity indicate
that risk estimates may vary between metropolitan areas or regions. There are multiple
explanations for these observations including, random variation (chance), residual confounding,
exposure misclassification, and the existence of real source-specific differences in risk. The
differences in the precision of city-specific estimates of mortality risk associated with daily
change in PM concentration complicate the interpretation of heterogeneity in risk reported by
multi-city time-series studies or when single-city estimates show differences between localities.
Some authors have used interpolation approaches to fill in the missing days of ambient
concentration data in order to avoid excluding cases for days with no air quality measurements.
These imputation methods are often based on hourly or daily air quality measurements of PM
mass or gaseous co-pollutants. Unfortunately, imputed values never carry as much information
about population exposures as measured values and any evaluation of the improved health
associations with speciated PM would be diluted by the high proportion of imputed values.
Moreover, the error in imputation is not likely to be constant for each specific PM component;
some components will be imputed with more error than other components. For example, filling
in missing data with imputed values may be associated with more error in studies of coarse
particle mass and fine particle components which are associated with larger spatial and day-to-
day variation than fine PM mass. Along with the simple imprecision of the imputed values, any
use of the gaseous co-pollutants in an imputation algorithm will necessarily increase the co-
linearity between the measured values for the gaseous co-pollutants and the imputed values for
various PM components. Thus, imputation methods will generally tend to bias any
epidemiologic studies of the differential associations of PM components with human health
outcomes.
The 2004 CD discussed results from a study conducted in Chicago, IL, which illustrates
the impact on risk estimates caused by the use of l-in-6 day ambient concentration data. In this
study, a significant association was reported between daily change in PMio concentration and
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mortality between 1985 - 1990 using data from one monitor collected on a daily basis (Ito et al.,
1995). However, when the data set was divided into 6 subsets representing a l-in-6 day
monitoring frequency, the effect estimates for the PMio-mortality association were quite
variable. Moreover, the confidence intervals were wider and analysis of only one of the subsets
indicated a statistically significant association. This analysis indicates reduced precision due to
the markedly decreased number of events available for analysis, but also indicates that risk
estimates may be affected by exposure misclassification, chance, or selection bias. Selection bias
could occur if the analyzed group associated with any particular sampling schedule were
different from the unanalyzed group in a way that was systematically associated with exposure
estimates. However, selection bias is not as prevalent a concern for time-series studies as is
exposure misclassification due to inadequate characterization of the spatial variation of the PM
exposure measure within a locality.
Exposure Misclassification and Spatial Variability
Exposure misclassification can occur when ambient concentration from one or only a few
monitors in a geographic area is assigned to estimate the PM exposure of the individuals who
died in that area. If the ambient concentration that is calculated for a particular day is higher
than what some of those who died actually experienced, but lower than what others who died
experienced, the resulting "noise" in the PM indicator makes it harder to distinguish a statistical
association with mortality. If the calculated ambient concentration is not consistently higher or
lower than the concentration experienced by those who died on that day (that is, nondifferential
misclassification), the size of the relative risk may be attenuated. The importance of the
attenuation depends on the degree of spatial variability characteristic of the pollutant under
analysis and the resulting amount of exposure misclassification. While the impact of spatial
variability on estimates of exposure is of less concern for studies of fine PM mass, a PM
exposure with relatively homogenous local distribution, this is an important issue for
epidemiology studies of PM2.5 components or thoracic coarse particle mass and components. The
NCER STAR program is funding five studies beginning in early 2008 that will provide
information about spatial variability in coarse particle mass and components and effects on
health. Additional studies, to be awarded in 2008, will address strategies to incorporate data on
spatial and temporal variability of PM components in atmospheric and exposure models.
Evaluation of Cumulative Effects of Air Quality on Health
Studies using distributed lag models indicate that risk estimates using zero or one-day
lags may underestimate the magnitude of mortality associations with air quality. Distributed lag
models allow the examination of the combined effect of air pollution across a range of prior days
on mortality for one particular day, e.g. today's mortality with today's air quality (lag 0),
yesterday's air quality (lag 1), and day before yesterday's air quality (lag 2). The lagged effects
of air quality over multiple previous days are compared with each day's mortality throughout the
study period. With l-in-3 or l-in-6 day monitors, the relationship with the health measurements
is disordered; the lagged effects of air quality on a single day must be compared with mortality
on different days. For example, today's air quality is compared to today's mortality at lag 0,
with tomorrow's mortality at lag 1, and with the day after tomorrow's mortality at lag 2.
Distributed lag models using daily monitors are advantageous because a specific lag structure for
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modeling the association of a pollutant with health does not have to be selected in advance. This
modeling flexibility will be especially important for the study of PM components, which may
have differing lags between exposure and health outcome.
SUMMARY
The purpose of the preceding discussion has been to highlight the importance of time-
series studies using air quality data obtained from EPA's Air Quality System to identify health
risks associated with ambient PM mass concentration and the limitations of the national
monitoring networks for similar studies of fine particle components as indicators of PM sources.
This issue was raised by the health research community at the Fffil/EPA workshop in Boston
November, 2006 and in subsequent discussions. The lack of daily speciation monitoring for
PM2.5 components is an important research need identified by the epidemiology community.
This issue has been highlighted because the number of PM2 5 speciation monitors per location is
much smaller and variability (temporal and spatial) for many fine particle components is much
greater than for PM2 5 mass. Obtaining daily PM2 5 speciation monitoring in a set of key
locations will enhance our understanding of the health effects associated with fine particles by:
• providing improved statistical power for epidemiologic studies of PM components
within a reasonable time period,
• providing analytical flexibility to examine distributed lags, and
• reducing exposure misclassification to improve the validity and precision of health effect
estimates.
In addition, targeted studies in some metropolitan areas will help to characterize the
spatial variability of PM2.5 components and quantitative impact on risk estimates.
POTENTIAL OPTIONS FOR OBTAINING DAILY PM2.5 SPECIATION
MEASUREMENTS
A: Retrospectively fill in the missing data
Actions could be taken to construct a dataset containing daily values for PM2.5 mass,
metals, elements, sulfate, nitrate, and carbon for previous years. Options could include analyzing
archived daily PM2 5 mass (Teflon) filters collected at CSN or nearby sites, using data collected
at nearby continuous (hourly) monitors. These efforts most likely could be done at most at a
limited number of sites due to resource constraints and the limited historical use of the relevant
samplers.
1. Analyze archived filters to obtain daily measurements of metals and elements (XRF)
The 54 STN monitors operate on a l-in-3 day sampling schedule. Some non-STN
monitors in the CSN network may also operate on this schedule. There are some things that
could be done to achieve daily measures at some of these sites retrospectively (back to 1999) for
PM2.s and some components that are key source indicators. XRF analysis of archived daily PM2.s
mass (Teflon) filters collected from an adequate number of locations of daily FRM monitors
(primarily ones co-located with some of the l-in-3 day CSN monitors) would provide critical
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information about daily concentrations of metals and elements. There are alternative methods to
measure particle mass, EC, and XRF-elements that may address the data needs for epidemiology
studies and be cost-effective. For example, Joel Schwartz and George Thurston recommended
using reflectance, a measure of black carbon, at the CSN sites to obtain estimates of daily EC
(where daily samples for PM2.5 mass are available). Reflectance or other optical analyses of the
same filters may provide an indicator of EC. It must be noted that the existence of all historical,
Teflon filters of interest remains to be determined and gaining access to those filters that have
been archived will require a collaborative effort with State and local agencies.
2. Evaluate continuous measurement data to obtain daily organic carbon (OC)/elemental
carbon (EC) at TRENDS sites.
Continuous (hourly) data for sulfate, nitrate, EC, and OC are available in some cities
including, Chicago, IL, Indianapolis, IN, Davenport, IA, Bar Harbor, ME, Cedar Rapids, IA,
Raleigh, NC, New York City, Seven Oaks, SC, Greenville, SC, Rockwell, NC, Seattle, WA, and
Detroit, MI. However, all components are not measured at all cities. Daily measures for sulfate,
nitrate, EC, and OC could be obtained over multiple years for Chicago (2002-2007), Bar Harbor
(2004-2007 for sulfate, 2004 -? for OC, EC, & TC), New York City (2001 - 2007 for sulfate &
nitrate, 2005 - 2007 for OC & EC), and Raleigh (2003 - 2006 for OC, 2003 - ? for sulfate, 2003
- 2007 for nitrate, and 2003 - 2006 for total carbon). These data could be used to construct a
data set containing daily concentrations. Of these sites, STN monitors are located in the vicinity
of the continuous monitors at Chicago, New York City, and Raleigh. OAQPS notes that
continuous speciation monitors have their own measurement uncertainties, which may include
systematic biases that are not well characterized; data from them cannot simply be merged with
CSN data to fill in missing days. However, the continuous data could be used on a site-by-site
basis if a relationship between the continuous analyzer and the filter-based monitor was
established.
B: Expand current monitoring schedules at selected locations in order to conduct daily
speciation measures in selected metropolitan areas.
1. Locations
Population size and the number of health events that occur each day are location
attributes that contribute the greatest amount to the power of a time-series study to detect an
association with exposure to an air pollutant if one exists. The top 22 PMSAs or MSAs with the
highest weight were selected from Table 1 and are listed in Table 2 along with any CSN
monitors currently operating in that location. It should be noted that there are three PMSAs
(highlighted in red) in this group where there is no CSN monitor located. All of the other
locations, except for Los Angeles, have an STN monitoring site. In addition to population size
and mortality and the opportunity to take advantage of an existing CSN monitor, there are a
number of factors that are important to consider for site selection for daily speciation monitoring.
Important information might include:
• What are the major sources in an area? - Are components of interest present in
measureable concentrations?
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• Regional representation of study locations under consideration
• Area characteristics - topography, which influences spatial variability
• Existing data collection at a location -
• Are data available on a daily timescale for components of interest retrospectively?
• Are multiple speciation monitors operating in the location?
• Are special studies (government or grant-supported) being conducted in the location?
Participants at the April 2008 workshop will be asked to provide feedback about the
importance of daily monitoring for fine particle components for advancing our understanding of
the impacts of air pollutant exposures on public health and criteria to consider for prioritizing
locations for daily PM2.5 speciation monitoring. For discussion purposes, three columns have
been added to Table 2 indicating significant area characteristics, notes on predominant PM
components, and any special studies known to be conducted in the location that may contribute
information on temporal or spatial variability.
2. Components
For what PM species would it be beneficial to have daily ambient measurements? For
discussion purposes, Ito (HEI/EPA workshop, 2006) suggested the following components: OC,
EC, nitrate, sulfate, Se, As, Si, Fe, Mn, Cr, Zn, Pb, V, Ni based upon information from
toxicology and source apportionment studies. Some of these components may have more
homogenous distributions in certain regions but others are likely to have a high degree of spatial
variability. CSN and IMPROVE currently analyze for these components and EPA plans
are to continue to do so.
3. Costs
The estimated annual cost for shipping and lab analysis to add daily PM2.5 mass and
speciation monitoring at one CSN site that is currently operating on a l-in-3 day sampling
schedule would be $100,000 based on current EPA contracts. The State/local monitoring agency
would incur additional labor and equipment costs to operate the monitors as well.
OTHER MONITORING ISSUES
Two additional issues should be mentioned in this discussion of the use of air quality
monitoring data in time-series studies.
A: Spatial Variability: Set up additional monitoring sites within certain cities to
increase understanding of spatial variability of specific components.
Several components of research interest will be associated with a high degree of spatial
variability across a location. A small number of monitors (4 - 6) distributed to capture
concentrations throughout an area could give enough information to conclude whether or not a
specified component has a uniform distribution in that area. If the distribution appeared uniform
for a certain component, multi-site time-series analyses could be conducted using data from one
or more centrally located monitors per location. If not, a more detailed exposure analysis would
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be necessary in certain locations to obtain a finer spatial resolution and develop exposure
models. Such a detailed, nonroutine study could potentially involve a research grant. Based on
current EPA contracts, the estimated annual cost to establish a CSN-like site operating on a daily
schedule in a new location but probably where other monitors are located is $150,000 for
shipping and lab analysis.
B. Alternative Measurement Methodologies
There are alternative methods to measure particle mass, EC, and XRF-elements that may
address the data needs for epidemiology studies and be cost-effective. For example, Joel
Schwartz and George Thurston recommended using reflectance, a measure of black carbon, at
the CSN sites to obtain estimates of daily EC. In addition, the use of a rotating drum sampler
which measures various size classes of PM mass and PM components with a finer time
resolution (six hours) could be considered. At a meeting with State/local monitoring managers,
reservations were expressed about whether this sampler has demonstrated adequate
repeatability.
BENEFITS OF OBTAINING DAILY PM2.5 SPECIATION DATA
If resources can be secured or re-programmed to support daily PM2.5 monitoring at a well
prioritized set of monitoring sites of most value in health studies, the following benefits would be
obtained:
1. Time-series studies will have enough statistical power to determine which particles are
more toxic than others without having to wait ten or more years for results.
2. We will develop more accurate estimates of health effects that fully address lag issues
due to the availability of daily health and monitoring data. Studies have shown that the
use of distributed lag models evaluating several consecutive days prior to the occurrence
of death result in a higher estimated relative risk. Studies of PM components need more
flexibility in choice of lag models because not all components are predicted to have the
same lag structure for effects. This has obvious implications for RIA, accountability
studies, and basis for NAAQS decisions.
3. With multiple daily speciation monitoring sites in some of the larger cities, especially
those with more complicated geologic features, it would become possible to improve our
understanding of the impact of spatial variability on exposure estimates for PM
components. Studies of within-city spatial variability will allow assessment of whether
the "noise" in exposure estimates is so large for some components that no excess risk is
observed.
4. Researchers could base their analyses on actual data, rather than using creative
approaches to get around the fundamental issue of missing data. These methods are
helpful, but introduce more uncertainty into the exposure estimates by increasing the co-
linearity with co-pollutants.
5. The primary recommendation in the final NRC report was the need for EPA to
systematically examine which PM components and sources are most important for public
health. Since PM components may exert their adverse influences over different lags
between exposure and outcome, the ability to correctly evaluate lagged effects may be
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crucial to the correct assessment of the relative toxicity of PM components. The
availability of daily air quality information would enable the differential assessment of
PM components and PM sources, and eliminate a potential bias in favor of the
assessment of those components with very immediate effects.
6. EPA is investing heavily in studies of key components and sources of PM (e.g., Hopkins
PM Center studies, HEI's NPACT study, recent STAR RFAs). The return on this
investment would be increased significantly if daily PM2.5 data are available to increase
confidence in the findings and reduce uncertainties in the estimates as explained above.
7. Accountability: Assessing the health improvements attributable to reduced air pollution is
already a difficult challenge. Without daily data, such research will be even more
difficult and take many years to demonstrate benefits.
REFERENCES
Burnett RT, Brook J, Dan T, Celocla C, Philips O, Cakmak S, Vincent R, Goldberg MS, Krewski
D. 2000. Association between particulate- and gas-phase components of urban air pollution and
daily mortality in eight Canadian cities. Inhalation Toxicol 12 (Suppl4): 15-39.
Burnett RT and MS Goldberg. 2003. Size-fractionated parti culate mass and daily mortality in
eight Canadian cities. In: Revised Analyses of Time-Series Studies of Air Pollution and Health.
Special Report. Health Effects Institute, Boston, MA.
Dominici F, McDermott A, Daniels M, Zeger SL, Samet JM. 2003. Mortality among residents of
90 cities. In: Revised Analyses of Time-Series Studies of Air Pollution and Health. Special
Report. Health Effects Institute, Boston, MA.
Ito K, Kinney PL, and Thurston GD. 1995. Variations in PM-10 concentrations within two
metropolitan areas and their implications for health effects analyses. Inhalation Toxicol 7:735-
745.
National Research Council (NRC). 2004. Research Priorities for Airbourne Particulate Matter
IV: Continuing Research Progress. The National Academies Press, Washington D.C.
Samet JM, Zeger SL, Dominici F, Curriero F, Coursac I, Dockery DW, Schwartz J, Zanobetti A.
2000. The National Morbidity, Mortality, and Air Pollution Study, Part II: Morbidity and
Mortality from Air Pollution in the United States. Research Report 94. Health Effects Institute,
Cambridge MA.
Schwartz J, Zanobetti A, Bateson T. 2003. Morbidity and Mortality Among Elderly Residents of
Cities with Daily PM Measurements. In: Revised Analyses of Time-Series Studies of Air
Pollution and Health. Special Report. Health Effects Institute, Boston, MA.
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Figure 1. Implication of every-6th-day and every-3rd-day data: Statistical power for time-
series studies.
Slide provided by Dr. Kazuhito Ito, presented at Fffil/EPA Workshop on Air Quality Data,
Newton, MA, November 2006. Adapted slightly for this discussion paper.
c/i
•
S
ra
a;
un
(a) non-accidental mortality
3 yrs of every-
3rd-day data
6 yrs of«very-
3rd-day data
200
400
600
800
number of monitoring days
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Table 2. Twenty-two MSAs with largest population and associated speciation monitors.
# 2000 Pop Wgt34 MSA Local Site Name
City Name
State
Site
ID
Sample Collection County Name
1 9,3 14,235 16.0 New York, NY PMSA; New York-Northern New Jersey-Long Island, NY-NJ-CT-PA CMSA
IS 52
DIVISION STREET
QUEENS COLLEGE 2
2 9,519,338 16.1 Los Angeles-Long Beach. CA PMSA:
New York NY 0110 Met One SASS Teflon Bronx
New York NY 0134 Met One SASS Teflon New York
New York NY 0124 R&P MDL2300 PM2.5 Queens
SEQ SPEC
Los Angeles— Riverside— Orange County, CA CMSA
STN? Area characteristics
Large urban increment
plus NE background
STN
Complex coastal and
mountain topography,
extreme traffic, ozone.
and sun
Components
High sulfate and
organics, low
nitrates
Extremely high
nitrate and
organics, low
sulfate
Special
Studies
NYDOC,
MESA-AIR,
Supersite
GARB, PM
Center, CHS,
MESA-Air,
Supersite
NULL Los Angeles CA 1103 Met One SASS Teflon Los Angeles
3 8,272,768 15.1 Chicago. IL PMSA: Chicago-Garv-Kenosha. IL-IN-WI CMSA
SPRINGFIELD PUMP STATION Chicago
COM ED MAINTENANCE
BLDG
Chicago
IL
IL
NORTHBROOK WATER PLANT Northbrook IL
0057 Met One SASS Teflon Cook
0076 Met One SASS Teflon Cook
4201 Met One SASS Teflon Cook
CITY HALL Naperville IL 4002 Met One SASS Teflon DuPage
4 5,100,931 11.8 Philadelphia. PA-NJ PMSA: Philadelphia-Wilmington-Atlantic City. PA-NJ-DE-MD CMSA
CAMDEN LAB
AMS Laboratory
Philadelphia PA
ON AMTRAK RIGHT OF WAY - Philadelphia PA
NEAR AIRPORT HI SPEED LINE
(ELECTRIFIED)
A420450002LAT/LON POINT IS Chester
OF CORNER OF TRAILER
Camden NJ 0003 Met One SASS Teflon Camden
0004 Met One SASS Teflon Philadelphia
0136 Met One SASS Teflon Philadelphia
PA 0002 Met One SASS Teflon Delaware
STN
STN
Industrial center with lake High sulfate and
influences, windy organics, low
nitrates
Industry plus NE
background
High sulfate and
organics, low
nitrates
MESA-Air
34
Weights (Wgt) are proportional to expected inverse-variance regression weights: sqrt((population * 0.01) / 365)
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# 2000 Pop Wgt MSA Local Site Name
Site Special
City Name State ID Sample Collection County Name STN? Area characteristics Components Studies
CHESTER COUNTY Not in a city PA 0100 Met One SASS Teflon Chester
TRANSPORT SITE INTO
PHILADELPHIA
CORNER OF MLK BLVD AND Wilmington DE 2004 Met One SASS Teflon New Castle
JUSTISON ST, NO TRAFFIC
DATA AVAILABLE
5 4,923,153 11.6 Washington. DC-MD-VA-WV PMSA: Washington-Baltimore. DC-MD-VA-WV CMSA
MCMILLAN PAMS Washington DC 0043 Andersen RAAS Teflon District of Columbia STN
HOWARD UNIVERSITY Beltsville MD 0030 Andersen RAAS Teflon Prince George's
6 4,441,551 11.0 Detroit. MI PMSA: Detroit-Ann Arbor-Flint. MI CMSA
NULL
Allen Park MI 0001 Met One SASS Teflon Wayne
PROPERTY OWNED BY Dearborn MI 0033 Met One SASS Teflon Wayne
DEARBORN PUBLIC SCHOOLS
STN
NE background plus High sulfate and Supersite
traffic organics, low
nitrates
Industry and high traffic High sulfate and EPA
organics, low
nitrates
DEAN ROAD DEAD-ENDS AT Luna Pier
SITE, 200 FT WEST
MI 0005 Met One SASS Teflon Monroe
7 4,177,646 10.7 Houston. TX PMSA: Houston-Galveston-Brazoria. TX CMSA
SOUTH OF DETERMINED & Not in a city TX
ALDINE MAIL RD
INTERSECTION
NW OF W. LAMBUTH &
DURANT INTERSECTION
8 4,112,198 10.6 Atlanta. GA MSA
2390-B WILDCAT ROAD,
DECATUR, GA
Deer Park TX
Decatur
9 3,519,176 9.8 Dallas. TX PMSA: Dallas-Fort Worth. TX CMSA
0024 R & P Model 2025 PM- Harris
2.5 Sequential Air
Sampler w/VSCC
1039 URG MASS400 Teflon Harris
WINS
GA 0002 Met One SASS Teflon DeKalb
STN
STN
Extreme chemical
industry and ozone,
coastal sunny
Medium (if
interesting)
organics, sulfate?
Extreme biogenics, high High sulfate and EPRI,
traffic, sunny organics, low Supersite
nitrates
Traffic and cattle, sunny high ammonia?
WESTOFS. AKARD&
CANTON STREETS
INTERSECTION
Dallas TX 0050 R & P Model 2025 PM- Dallas
2.5 Sequential Air
Sampler w/VSCC
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#
2000 Pop
Wgt
MSA
Local Site Name
City Name
State
Site
ID
Sample Collection
County Name
STN?
Area characteristics
Components
Special
Studies
DALLAS HINTON Dallas TX
THIS SITE WILL MONITOR Midlothian TX
THE SAME AREA AS SITE
1390015
0069 URG MAS S400 Teflon Dallas
WINS
0016 R & P Model 2025 PM- Ellis
2.5 Sequential Air
Sampler w/VSCC
10 3,406,829 9.7 Boston. MA-NH PMSA: Boston-Worcester-Lawrence. MA-NH-ME-CT CMSA
DUDLEY SQUARE ROXBURY Boston MA 0042 Met One SASS Teflon Suffolk
11 3,254,821 9.4 Riverside-San Bernardino. CA PMSA: Los Angeles-Riverside-Orange County. CA CMSA
NULL
12 3,251,876 9.4 Phoenix-Mesa. AZ MSA
Rubidoux
(West
Riverside)
CA
8001 Met One SASS Teflon Riverside
PHOENIX SUPERSITE Phoenix AZ 9997 Met One SASS Teflon Mancopa
13 2,968,806 9.0 Minneapolis-St. Paul. MN-WI MSA
ANDERSON SCHOOL - Minneapolis MN 0963 Met One SASS Teflon Hennepm
PHILLIPS NEIGHBORHOOD
14 2,846,289 8.8 Orange County. CA PMSA: Los Angeles-Riverside-Orange County. CA CMSA
No speciation monitors?
STN
STN
STN
STN
STN
NE background plus
traffic
Complex coastal and
mountain topography,
extreme traffic, ozone.
and sun
Extreme traffic, ozone.
and sun
Continental urban
Complex coastal and
mountain topography,
traffic, ozone, and sun
High sulfate and
organics, low
nitrates
Extremely high
nitrate and
organics, low
sulfate
High nitrate and
organics, low
sulfate
Medium
High nitrate and
organics, low
sulfate
Harvard
MESA-Air
MESA-Air
15 2,813,833 8.8 San Diego. CA MSA
NULL
NULL
El Cajon CA 0003 Met One SASS Teflon San Diego
Escondido Ca 1002 Met One SASS Teflon San Diego
16 2,753,913 8.7 Nassau-Suffolk. NY PMSA: New York-Northern New Jersey-Long Island. NY-NJ-CT-PA CMSA
No speciation monitors?
17 2,603,607 8.4 St. Louis. MO-IL MSA
STN
Complex coastal and
mountain topography,
traffic, ozone, and sun
Medium nitrate and
organics, low
sulfate
Large urban increment
plus NE background
Industry and traffic
High sulfate and
organics, low
nitrates
High sulfate and
organics, low
nitrates
Super site
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# 2000 Pop Wgt MSA Local Site Name
SIU DENTAL CLINIC
NULL
City Name
Alton
Not in a city
State
IL
MO
Site
ID
2009
0012
Sample Collection
Met One SASS Teflon
Met One SASS Teflon
County Name STN?
Madison
Jefferson
Area
characteristics Components
Special
Studies
18
BLAIR STREET CATEGORY A St. Louis
CORESLAMPM2.5.
2,552,994 8.4 Baltimore. MD PMSA: Washington-Baltimore. DC-MD-VA-WV CMSA
STN
NE background plus High sulfate and MESA-Air,
traffic organics, low nitrates Supersite
ESSEX Essex MD 3001 Met One SASS Teflon
19 2,414,616 8.1 Seattle-Bellevue-Everett. WA PMSA: Seattle-Tacoma-Bremerton. WA CMSA
SEATTLE DUWAMISH
BEACON HILL
OLIVE STREET
Seattle
Seattle
Seattle
20
2,395,997 8.1 Tampa-St. Petersburg-Clearwater. FL MSA
Baltimore
WA 0057 Andersen RAAS Teflon King
WA 0080 Met One SASS Teflon King
WA 0048 Met One SASS Teflon King
STN
STN
Coastal urban, high
wood burning
Medium organics?,
nitrate?, low sulfate?
Coastal with some SE Medium sulfate and
and power plant organics
influence
SYDNEY
NULL
Plant City FL 3002 Met One SASS Teflon Hillsborough
Pmellas Park FL 0026 Met One SASS Teflon Pmellas
21 2,392,557 8.1 Oakland. CA PMSA: San Francisco-Oakland-San Jose. CA CMSA
No speciation monitors?
22 2,358,695 8.0 Pittsburgh. PA MSA
STN
Coastal urban, high
wood burning
Medium organics?,
nitrate?, low sulfate?
Industry & traffic plus High sulfate &
eastern background organics, low nitrates
NULL
NULL
S ALLEGHENY HS DOWN
WIND FROM USS CLAIRTON
COKE WORKS
LAT/LON POINT IS TRAILER
Pittsburgh PA 0008 Met One SASS Teflon Allegheny
Not in a city PA 5001 Met One SASS Teflon Washington
Liberty PA 0064 Met One SASS Teflon Allegheny
Greensburg PA 0008 Met One SASS Teflon Westmoreland
STN
Supersite
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Appendix E: Session V: Ambient Air Monitoring Realities - EPA/State/Local
Perspectives - Ambient Air Monitoring Network: Network Design and Site
Selection Approval
Questions on this draft white paper should be directed to Tim Hanley, EPA/OAQPS,
hanley.tim@epa.gov; (919) 541-4417.
Introduction
The purpose of this white paper is to briefly describe the process for designing major
ambient air monitoring networks including:
• the roles of EPA and State, local, and Tribal monitoring agencies in selecting and
approving monitoring stations;
• the ways that health and other researchers can provide input currently to
State/1 ocal/Tribal monitoring agencies and EPA on the usefulness and approval of
monitoring stations; and
• suggestions to improve facilitation of soliciting input on monitoring station
selection from health and other researchers.
Background
The measurement of ambient air pollution in the United States is provided through a
number of ambient air monitoring networks operated almost exclusively by State, local, and
Tribal air monitoring programs. The EPA identifies key parameters to measure such as criteria
pollutants35, pollutant precursors, chemical composition of particles, and air toxics. Ambient air
monitoring networks are implemented through a combination of Federal requirements and
voluntary programs.36 EPA provides required siting criteria and network deployment strategies
for measurement of pollutants as one of several key components to implementing air monitoring
networks. Monitors are categorized as State and local Air Monitoring Stations (SLAMS) when
they are approved as part of the long-term operating network or Special Purpose Monitors
(SPMs) when they are being used for short-term investigations (i.e., less than two years). Air
toxic monitoring stations are not required by regulation, and do not carry the SLAMS distinction.
Additional, Federally run networks provide monitoring coverage in primarily rural areas
to meet specialized objectives. The Interagency Monitoring of Protected Visual Environments
(IMPROVE) network, a cooperative measurement effort guided by a steering committee
composed of representatives from Federal and regional-state organizations, provides important
data for implementing both regional haze and PM2.5 attainment programs. The Clean Air Status
and Trends Network (CASTNET), managed by EPA's Clean Air Markets Division, provides
atmospheric data on the dry deposition component of total acid deposition, ground-level ozone
and other forms of atmospheric pollution. The National Atmospheric Deposition Program
(NADP), another cooperative program involving several governmental agencies, provides
measurements of pollutants in precipitation, including sulfate, nitrate and ammonium.
35 The Clean Air Act requires EPA to set National Ambient Air Quality Standards (NAAQS) for six common air
pollutants. They are particle pollution (often referred to as paniculate matter), ground-level ozone, carbon
monoxide, sulfur oxides, nitrogen oxides, and lead.
36 All Tribal monitoring programs are provided for cooperatively as compliance with Federal rules cannot be
required of Tribes.
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EPA designs and implements ambient air monitoring networks to meet several
monitoring objectives:
• Determining compliance with health- and welfare-based standards (i.e., the NAAQS);
• Providing air pollution data to the general public on a timely basis; and
• Supporting the development and tracking of emission control programs.
In addition to the monitoring objectives above, EPA recognizes important additional
objectives that need to be factored into designing ambient monitoring networks such as:
• Supporting health and welfare effects and exposure research studies
• Providing air pollution data for human health risk/exposure assessments and NAAQS
reviews;
• Providing air pollution data for welfare effects assessments; and
• Supporting atmospheric research studies.
EPA recently made changes to the NAAQS-related monitoring regulations. Specifically,
the general monitoring network design requirements for the minimum number of ambient air
monitors were modified to focus more on populated areas with air quality problems and to
significantly reduce the requirements for criteria pollutant monitors that have measured ambient
air concentrations well below the applicable NAAQS. A number of the changes related to the
monitoring of PM2 5 include revisions to the requirements for reference and equivalent method
determinations (including specifications and test procedures). These regulations also added a
requirement for a new multi-pollutant monitoring network called National Core (NCore) and
revised certain provisions regarding monitoring network descriptions and periodic assessments,
quality assurance, and data certifications (71 FR 61236, October 17, 2006)37.
Design criteria for required ambient air monitoring networks are provided in Appendix D
to 40 CFR Part 58. Network design criteria include monitoring objectives, scale of
representation, and specifications for locating monitors (e.g., a requirement to be in the area of
expected maximum concentration). In many cases, there are multiple monitoring objectives for a
site with the highest concentration of a pollutant. For instance, a neighborhood scale site in the
area of maximum fine particle exposure could be thought of as a central monitoring station.
These central monitoring stations might have several PM measurement samplers such as a PM2 5
FRM for comparison to the NAAQS, a PM2.5 continuous mass monitor for reporting the Air
Quality Index (AQI), and a fine particle speciation sampler to develop and track emission control
strategies. All of these data could be useful in health studies depending on the purpose and
availability of health endpoint data. Requirements for the minimum number of monitors to
operate are identified for PM, ozone, Photochemical Air Monitoring Stations (PAMS), and
NCore (which include several measurements); however, monitoring agencies are encouraged to
operate additional stations to adequately characterize pollutants. Siting criteria are provided in
Appendix E to Part 58. Siting criteria include the specifications for probe and inlet height,
distance from obstructions, and traffic.
37 See also http://www.epa.gov/ttn/amtic/ for more information on the Ambient Monitoring Technology Information
Center (AMTIC) operated by EPA's Ambient Air Monitoring Group (AAMG). AMTIC contains information and
files on ambient air quality monitoring programs, details on monitoring methods, relevant documents and articles,
information on air quality trends and nonattainment areas, and Federal regulations related to ambient air quality
monitoring.
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Roles of EPA and Monitoring Agencies in selecting and approving changes to a monitoring
network:
The EPA requires each State monitoring agency to develop and submit an Annual
Monitoring Network Plan to the applicable EPA Regional Office by July 1 of each year. States
may delegate portions of the Annual Monitoring Network Plan to applicable local agencies (e.g.,
in California there are several plans, while in New York there is one plan for the whole State).
The annual monitoring network plan must be made available for public inspection for at least 30
days prior to submission to EPA. Any annual monitoring network plan that proposes SLAMS
network modifications including new monitoring sites is subject to the approval of the EPA
Regional Administrator. Air Toxic monitoring stations are encouraged to be included in annual
monitoring network plans, but are not formally required. Modifications to PAMS, the Speciation
Trends Network (STN), and the NCore network are to be approved by EPA's Office of Air
Quality Planning and Standards. The EPA Regional Office will provide an opportunity for
public comment and approve or disapprove the plan and schedule within 120 days of submission.
If the State or local agency has already provided a public comment opportunity on its plan and
has made no changes subsequent to that comment opportunity, the Regional Administrator is not
required to provide a separate opportunity for comment.
The annual monitoring network plan must contain the following information for each
existing and proposed site:
1. The AQS site identification number.
2. The location, including street address and geographical coordinates.
3. The sampling and analysis method(s) for each measured parameter.
4. The operating schedules for each monitor.
5. Any proposals to remove or move a monitoring station within a period of 18 months
following plan submittal.
6. The monitoring objective and spatial scale of representation for each monitor as defined
in appendix D to this part.
7. The identification of any sites that are suitable and sites that are not suitable for
comparison against the annual PM2.5 NAAQS as described in §58.30.
8. The metropolitan area (e.g., MSA, CBSA, CSA) or other area represented by the monitor.
The annual monitoring network plan must document how State and local agencies
provide for the review of changes to a PM2.5 monitoring network that impact the location of a
violating PM2.5 monitor or the creation/change to a community monitoring zone. The affected
State or local agency must document the process for obtaining public comment and include any
comments received through the public notification process within their submitted plan.
What factors are critical in decisions to change the location of a monitoring station?
In most cases, monitoring stations are located for many years in the same location;
however, from time to time a monitoring station is moved or shut down due to either planned or
unforeseen reasons. The following list provides a summary of the most common reasons why
monitoring stations are moved or shut down:
Logistical reasons:
• The lease for the land or building where the monitoring station is located cannot be
extended due to redevelopment or other reasons.
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• Construction adjacent to a monitoring station renders the site inappropriate to use during
construction and in some cases unable to meet siting criteria after construction is
complete.
Failure to meet probe and siting criteria:
• Growth of trees around a monitoring station renders the site no longer able to meet siting
criteria and the owner of the trees is unwilling to have them cut or trimmed.
• Increases in motor vehicles traffic, including the addition of new traffic lanes lead to re-
categorizing the scale of representation or failure to meet set-back requirements; which
no longer meets the network design criteria.
• Site inspections reveal that some aspect of the siting criteria is no longer acceptable (e.g.,
a new HVAC system or emission source is located too close to an inlet).
Changes in responses to emission and/or ambient monitoring trends:
• Changes to existing point, area, or mobile source emission inventories (e.g.,
establishment of new beltways, shut-down of manufacturing facilities) that alter the
original premise for site placement.
• Long-term trends analysis demonstrates monitor's objective has been fulfilled (taking
into account future alterations of the NAAQS). For example, steep declines in ambient
carbon monoxide levels at micro-scale compliance sites led to discontinuation of a large
number of CO monitors.
• Network assessment indicates that monitoring resources should be devoted to other issues
(e.g., air toxics measurement) or conserved to preserve high priority objectives (e.g.,
ozone, PM2 5)
What feeds into the decision-making process for moving or shutting down a monitoring
station?
With any number of reasons why a monitoring station may need to be moved or shut
down, monitoring agencies must plan for network changes. For situations where it may be
possible to stay at the existing site, if barriers can be overcome, an agency would likely make the
necessary efforts to maintain the site if the monitoring objective were critical and no other
suitable location were available. Knowing that data from a monitoring station were being used
in an important health or epidemiological study would provide a persuasive argument to keep the
station in the same place if the agency knew the data were being used. For example, an agency
may be willing to petition a land owner to trim a tree or move an obstruction given the more
compelling use of the data in a health research study.
If a monitoring station has to be moved, how is a new location picked and approved?
Although usually not possible, the best way to handle moving a monitoring station is to
identify a new site location within the same general area such that:
• the scale of representation and impacts from emission sources is the same as the
original site (so long as this is what is intended to be measured at the monitor);
• the old and new monitoring stations can be both operated simultaneously for one
year or, at minimum, during the season(s) of maximum expected concentrations;
and
• the statistical analyses of the data from the old and new monitoring stations are
deemed to be sufficiently comparable.
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In many cases, the decision to move a monitoring station does not allow enough time to
provide for simultaneous operation, so careful selection of the new station and analysis of its data
can only be compared to historical levels and other operating monitors. Use of emission
inventories, traffic counts, pictures, and satellite imagery can help document site characteristics
for comparability of old and new sites.
Monitoring station removals or relocations that are anticipated for the next 18 months
must be identified in the annual monitoring network plan that is required to be made available for
public inspection and is due to the EPA Regional Office by July 1 of each year. Although not
required, ideally the applicable EPA Regional Office will visit and perform a site inspection to
assure the new station meets siting criteria and is acceptable. In recognition of uncontrollable
circumstances (e.g., a natural disaster such as Hurricane Katrina), the EPA provides for moving
an air monitoring station outside the window of an annual monitoring plan by review and
approval of the applicable EPA Regional Office.
In what ways can communication with the health research community be improved
concerning possible changes in the ambient air monitoring networks?
Improved outreach concerning currently available tools:
• EPA has developed a web site that provides a link to each available State and local agency
annual monitoring network plan (see: http://www.epa.gov/ttn/amtic/plans.html).
• EPA will continue to update the website as plans are revised. EPA is seeking input regarding
recommendations for how often a reminder should goes out when plans are updated.
o State/1 ocal/Tribal monitoring agencies can be encouraged to summarize their
anticipated monitoring network changes in one place within the annual monitoring
network plans, or in a companion summary document, that could be easily scanned
by interested parties without wading through an extensive plan.
o Although not currently available, there is recognition that a mechanism allowing for
quick review of all anticipated network changes across the nation in one place would
be beneficial to the health research community. Note: this is not available as all
network plans are currently summarized by the appropriate State, local, and/or Tribal
agency.
• EPA has developed a web site dedicated to documenting the site characteristics, including
photos, and links to satellite imagery of candidate NCore monitoring stations
(http://www.epa.gov/ttn/amtic/ncore/). At the bottom of each individual NCore station web
page, there is an opportunity to provide comments on the candidate station. EPA encourages
health researchers to offer comments on the usefulness of candidate NCore stations.
• EPA maintains a relatively easy to use public web site that can be used to generate maps and
lists of active ambient air monitors (http://www.epa.gov/air/data/).
o An agency contact list is maintained as part of this web site so that data users can
reach State and local contacts concerning monitors of interest
(http ://www. epa. gov/air/data/contsl. html).
o A contact list for EPA Regional Office monitoring staff is available at:
http ://www. epa. gov/ttn/amtic/namscon.html.
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DRAFT 3/27/08 - FOR DISCUSSION PURPOSES ONLY - DO NOT QUOTE OR CITE
• EPA's Office of Air Quality Planning and Standards (OAQPS) has recently developed a list
serve that is used as a communication tool for ambient air monitoring and health researchers
can be added to our distribution list. Sign-up instructions are available at:
http://www.epa.gov/ttn/amtic/airlist.html.
Increased participation in health-focused gatherings:
• Monitoring experts from OAQPS can participate in key, annual national health research
conferences to present information on ambient air monitoring networks and plans for method
improvements or changes. This would also improve monitoring experts' knowledge of health
research needs, improve communication, and build a bridge between these two communities.
o OAQPS can also work with key State and local agency monitoring and network leads
by inviting them to participate in annual national health research conferences to
present information on ambient air monitoring networks for which they are
responsible.
• EPA will continue to engage CASAC's Ambient Air Monitoring and Methods
Subcommittee, and in doing so can specifically engage or address health research interests.
Communication initiatives:
• Health researchers are encouraged to communicate with the ambient air monitoring
community on the key monitoring sites that provide data to their research. Communications
should be at multiple levels to ensure an understanding of the importance of the work;
however, the most important communication needs to be directly with the State and/or local
air monitoring agency responsible for operating ambient air monitoring stations.
• EPA will facilitate and encourage the participation of health researchers at national air
monitoring conferences to provide presentations on how their research is using ambient data.
This will serve to educate and sensitize monitoring staff to the importance of the ambient air
monitoring program to health researchers especially if the issue of relocation or termination
of long-term monitoring sites is being considered.
• Health researchers and EPA should work collectively to establish the requirements for a
website or other publicly available forum to serve as an inventory of all on-going and
planned health studies utilizing ambient air monitoring data, the monitoring sites and key
ambient monitoring data being used, and the time period of the study. This would be
extremely beneficial so that monitoring agencies can make contacts with researchers who are
using the information from their networks.
E-6
-------
DRAFT 3/27/08 - FOR DISCUSSION PURPOSES ONLY - DO NOT QUOTE OR CITE
Appendix F: Session V: Ambient Air Monitoring Realities - EPA/State/Local
Perspectives - Ambient Air Monitoring Method Implementation
Questions on this draft white paper should be directed to Joann Rice, EPA/OAQPS,
rice.joann@epa.gov, (919) 541-3372.
Introduction
The purpose of this draft white paper is to describe the current process and
communication strategy used by the EPA to implement monitoring methods and method
improvements in support of National Ambient Air Quality Standards (NAAQS) criteria
pollutants, criteria pollutant precursors, and air toxics and to encourage discussion on how to
improve communications with the health research community.
Background
The Office of Air Quality Planning and Standards (OAQPS) is responsible for identifying
ambient monitoring needs based on the NAAQS review process and other air quality data
requirements. OAQPS implements the nation's ambient air monitoring networks to ensure that
they meet critical air program needs by leading and collaborating on the development of data
quality objectives (DQOs), monitoring methods, and a quality assurance (QA) program for
achievement of monitoring objectives. The best approach is utilized to optimize the value of the
monitoring networks to meet multiple program objectives and regularly assesses the network's
effectiveness in continuing to meet those objectives. This is done in collaboration with other key
partners, including EPA Headquarters and Regional Offices, the Office of Research and
Development (ORD), other Federal agencies, the Ambient Air Monitoring Steering Committee
(AAMSC), State/1 ocal/Tribal agencies, the National Association of Clean Air Agencies
(NACAA), Multi-State Organizations, the National Academy of Sciences (NAS), the Clean Air
Scientific Advisory Committee (CASAC), and private entities such as instrument manufacturers.
The EPA requires approved methods for measuring criteria pollutants. The monitoring
staff participates in the NAAQS review process to help identify monitoring network issues and
new monitoring technology needs. Once these needs are identified and articulated, the staff
works with ORD to develop new monitoring technologies and Federal Reference Methods
(FRMs) to support these needs. EPA also engages the CASAC, and their subcommittee on
ambient air monitoring and methods, in review of the methods developed. Once EPA develops
and specifies the FRM requirements, the instrument manufacturers are involved to develop
candidate FRM and Federal Equivalent Methods (FEMs). ORD is responsible for testing and
approval of equivalent and reference methods. A method has several components: sample
collection, analysis, handling, archival, and data processing and reporting, etc. Once FRM/FEMs
are approved, they are implemented in the national ambient air monitoring network to support
the NAAQS. As the NAAQS review cycle repeats, EPA reviews the monitoring networks and
monitoring method needs in consultation with monitoring agencies at the State, Local and Tribal
level. If adjustments to the FRM/FEMs are needed, the AAMG works with ORD to develop new
methods, or make adjustments or improvements to methods to meet the data needs in support of
the NAAQS. Then the method development, review, consultation, approval, and implementation
cycles repeat as described above.
G-l
-------
DRAFT 3/27/08 - FOR DISCUSSION PURPOSES ONLY - DO NOT QUOTE OR CITE
In the case of criteria pollutant precursors, like PM2.5 chemical species or air toxics, there
are no requirements for FRM development and approval. EPA rules or method plans may specify
the species or components and methods needed. In this case, OAQPS works with ORD to
identify the best methods and technologies available to meet the data use objectives. Once these
methods/technologies are identified, OAQPS/ORD consults with Regional Offices,
State/1 ocal/Tribal agencies, Multi-State Organizations, and CASAC to obtain feedback on the
appropriateness of the methods chosen. Once recommendations are provided on the
method/technological approach, the monitoring methods are implemented with the help of the
Regions and State/local agencies. Method plans are documented in the monitoring agency's
quality assurance project plan (QAPP). States and local agencies often adopt the methods
employed in the national monitoring programs for additional monitoring in their networks. As
EPA regularly reviews and assesses the monitoring networks to confirm that they are meeting the
data quality objectives and data use needs, revisions to the monitoring methods may be
recommended or warranted.
What factors are critical in decisions to change - why make changes or improvements?
Changes to the FRM/FEM are done in support of the NAAQS review process and any
resulting changes in the form or level of the standards, as well as to address needed operational
efficiencies. Changes for non-criteria pollutants or precursor species are largely made to improve
consistency and data usability across our monitoring networks, and to support multiple
monitoring objectives such as:
• Supporting the development of modeling tools and the application of source
apportionment modeling for control strategy development in support of the NAAQS;
• Assessing the effectiveness of emission reductions strategies through the characterization
of air quality trends;
• Supporting health effects and exposure research studies; and
• Supporting programs aimed at improving environmental welfare (e.g., the regional haze
program).
What feeds into the decision-making process?
Some changes are intentionally made and others inadvertently or unknowingly happen as
a result of changes at the sample collection or analysis stages (e.g., changes in field or laboratory
instrument operation). In the case of intentional plans for change, EPA may invoke special field
or monitoring studies and data analysis efforts to assess the need for, and the impact of change.
Plans for change are then vetted within EPA, and the monitoring, expert, and academic
community (disciplines covered include monitoring, modeling and data analysis researchers, as
well as health scientists) in a variety of ways and forums to obtain feedback from key partners.
These forums include participation in and presentation or communication of plans for change at
conferences, meetings, workshops, and Regional/State/Local and NACAA conference calls. In
addition, OAQPS holds a tri-annual monitoring conference specific to monitoring issues (the last
one was held November 2006). OAQPS may also issue letters, memorandums, program
Newsletters, and other forms of written communication through our list serve (link to sign up
instructions provided below). The list serve sends an email notification to all parties on the
distribution about posting of information on our Ambient Monitoring Technology Information
Center (AMTIC) website. In addition, special consultation with the AAMSC and CASAC is held
if appropriate.
How do we communicate plans for change?
G-2
-------
DRAFT 3/27/08 - FOR DISCUSSION PURPOSES ONLY - DO NOT QUOTE OR CITE
Several opportunities exist along the way for interested parties and key partners to
provide feedback. EPA begins to consider possible changes well in advance of implementation.
It takes several months if not years to perform any special studies, analyze the data, consult with
ORD, the academic and expert community and other groups before change can occur. The EPA
has (and continually develops) a variety of mechanisms to communicate plans for method
improvements and changes. These mechanisms have already been mentioned above (e.g.,
participation in conferences, meetings and conference calls, newsletters, consultations, etc.).
In what new ways can we engage health researchers and improve communication?
Communications between OAQPS and the health research community can be improved
by the following:
• OAQPS can participate in key, annual national health research conferences to present
information on ambient air monitoring networks and plans for method improvements or
changes. This would also improve OAQPS's knowledge of health research needs,
improve communication, and build a bridge between these two communities.
o Important conferences and dates need to be identified.
• EPA will continue to engage CASAC, and in doing so can specifically engage or address
health research interests.
o If the monitoring subcommittee is restored, make sure "right" health person(s)
involved
• OAQPS has recently developed a list serve that is used as a communication tool and
health researchers can be added to our distribution list. Sign-up instructions are available
at: http://www.epa.gov/ttn/amtic/airlist.html.
o NCER can help OAQPS focus what health researchers need to pay attention to or
focus the distribution versus "mass mailing" (see below).
• EPA can improve internal communications by instituting regular forms of
communication between ORD and OAQPS' divisions.
o Need regular process of communication across EPA on changes/plans, etc.
o Need to "institutional" process to formalize communications between OAQPS
and health researchers through NCER.
o Build additional relationships and channels for communication.
• OAQPS is involved in ORD's air research implementation planning process where
OAQPS research needs are identified and conveyed across ORD laboratories. This forum
can also be used to communicate plans for change across ORD.
o OAQPS can communicate plans to ORD and ORD can help to convey messages
and information across ORD labs and centers.
• ORD can participate in the AAMSC to improve communication with health researchers
regarding monitoring method issues and to monitoring agencies regarding ongoing and
planned research efforts.
G-3
-------
DRAFT 3/27/08 - FOR DISCUSSION PURPOSES ONLY - DO NOT QUOTE OR CITE
Appendix G: Preliminary Survey of Ambient Air Monitoring Sites Currently
Being Considered in EPA-funded Epidemiology Studies Feb 2008
G-l
-------
Preliminary Survey of Ambient Air Monitoring Sites Currently Being Considered in EPA-funded Epidemiology Studies Feb 2008
for more information, contact Sascha Lodge
PLEASE NOTE'
There is no distir
the Bakersfield ((
only one researc
Colors signify that a given monitor is being used by multi
ction between blue and orange. For example, four resea
DA) monitor, three researchers are using the El Cajon (C
her is using the Phoenix (AZ) monitor.
State
STN Sites
Alabama
Arizona
California
California
California
California
City
Birmingham
Phoenix
Bakersfield
Bakersfield
Bakersfield
Bakersfield
County
Jefferson
Maricopa
Kern
Kern
Kern
Kern
Site Name
NULL
PHOENIX
SUPERSITE
FLAT TERRAIN.OIL
REFINERY 1.3 Ml
NNW.TRAIN 1.4 Ml
N.FREEWAY 1.3 Ml
E
FLAT TERRAIN.OIL
REFINERY 1.3 Ml
NNW.TRAIN 1.4 Ml
N.FREEWAY 1.3 Ml
E
FLAT TERRAIN.OIL
REFINERY 1.3 Ml
NNW.TRAIN 1.4 Ml
N.FREEWAY 1.3 Ml
E
FLAT TERRAIN.OIL
REFINERY 1.3 Ml
NNW.TRAIN 1.4 Ml
N.FREEWAY 1.3 Ml
E
State
Code
01
04
06
06
06
06
County
Code
73
013
029
029
029
029
Site
ID
0023
9997
0014
0014
0014
0014
lodge.sascha@epa.gov; (202) 343-9769
pie researchers.
rchers are using
A) monitor, and
Priority
Medium
High
Medium
High
High
High
Address
5558
California
Ave;
Bakersfield
Mon
Objectivel
Population
Exposure
Mon
Object ive2
Latitude
33.553056
33.503643
35.356111
35.356111
35.356111
35.356111
Longitude
-86.815000
-112.095001
-119.040278
-119.040278
-119.040278
-119.040278
Parameters
Measured
if available
N020ZPM10
PM25
PM25species
Researcher
Name
Kaz Ito
Kaz Ito
Bart Ostro
Antonella
Zanobetti
Kaz Ito
Kaz Ito
Organizatio
n/Affiliation
NYU
NYU
CAOEHHA
Harvard
University
NYU
NYU
Duration of Study
For time-series and
case-crossover, the
longer into the future.
the better the analyses.
2000-2003
-------
California
California
California
California
California
California
California
California
California
California
California
California
El Cajon
El Cajon
El Cajon
Riverside
Rubidoux
(West
Riverside)
Rubidoux
(West
Riverside)
Rubidoux
(West
Riverside)
Rubidoux
(West
Riverside)
Rubidoux
(West
Riverside)
Sacramento
Sacramento
Sacramento
San Diego
San Diego
San Diego
Riverside
Riverside
Riverside
Riverside
Riverside
Riverside
Sacrament
0
Sacrament
0
Sacrament
0
NULL
NULL
NULL
Mira Loma
NULL
Riverside-Rubidoux
NULL
NULL
NULL
NULL
NULL
NULL
06
06
06
06
06
06
06
06
06
06
06
06
073
073
073
065
065
065
065
065
065
067
067
067
0003
0003
0003
8005
8001
8001
8001
8001
8001
0006
0006
0006
Medium
High
High
High
Medium
High
High
High
High
Medium
High
High
1155
Redwood
Ave.; El
Cajon
5130
Poinsettia
Place
5888 Mission
Blvd.;
Rubidoux
5888 Mission
Blvd.,
Rubidoux
Del Paso-
2701 Avalon
Dr;
Sacramento
Population
Exposure
Other
Population
Exposure
Population
Exposure
(Riverside-
San
Bernardino,
CA)
Population
Exposure
32.791389
32.791389
32.791389
33.995638
33.999580
33.99958
33.999580
33.999580
33.999580
38.614167
38.614167
38.614167
-116.941667
-116.941667
-116.941667
-117.493304
-117.416010
-117.41601
-117.416010
-117.416010
-117.416010
-121.366944
-121.366944
-121.366944
if available
N020ZPM10
PM25
PM25species
PM2.5, PM10
if available
N020ZPM10
PM25
PM25species
PM2.5, PM10,
S02, N02, Oz
CO
if available
N020ZPM10
PM25
PM25species
Bart Ostro
Antonella
Zanobetti
Kazlto
Joel
Kaufman
Bart Ostro
Joel
Kaufman
Antonella
Zanobetti
Kaz Ito
Kaz Ito
Bart Ostro
Antonella
Zanobetti
Kazlto
CAOEHHA
Harvard
University
NYU
Univ. of
Wash.
MESA Air
project
CAOEHHA
Univ. of
Wash.
MESA Air
project
Harvard
University
NYU
NYU
CAOEHHA
Harvard
University
NYU
For time-series and
case-crossover, the
longer into the future,
the better the analyses.
2000-2003
8/1/2004-7/31/2014
For time-series and
case-crossover, the
longer into the future,
the better the analyses.
8/1/2004-7/31/2014
2000-2003
For time-series and
case-crossover, the
longer into the future,
the better the analyses.
2000-2003
-------
California
California
California
Colorado
Connecticu
t
District Of
Columbia
Florida
Florida
Georgia
Idaho
Illinois
Illinois
Illinois
Indiana
San Jose
San Jose
Simi Valley
Commerce
City
New Haven
Washington
Davie
Plant City
Decatur
Meridian
Chicago
Chicago
Chicago
Indianapolis
Santa Clara
Santa Clara
Ventura
Adams
New Haven
District of
Columbia
Broward
Hillsboroug
h
DeKalb
Ada
Cook
Cook
Cook
Marion
SAN JOSE
JACKSON ST
SAN JOSE
JACKSON ST
NULL
ALSUP
ELEMENTARY
SCHOOL-
COMMERCE CITY
NULL
MCMILLAN PAMS
NULL
SYDNEY
2390-B WILDCAT
ROAD, DECATUR,
GA
NULL
Lawndale Comm-Ed
COM ED
MAINTENANCE
BLDG
COM ED
MAINTENANCE
BLDG
IN PARKING LOT
NEXT TO POLICE
STATION
06
06
06
08
09
11
12
12
13
16
17
17
17
18
085
085
111
001
009
001
011
057
089
001
031
031
031
097
0005
0005
2002
0006
0027
0043
1002
3002
0002
0010
0076
0076
0076
0078
Medium
High
High
High
High
High
High
High
High
Medium
High
High
High
High
156B
Jackson
Street; San
Jose
7801
Lawndale
Population
Exposure
Population
Exposure
(Chicago, IL
Northwester
n Indiana)
37.348500
37.348500
34.277500
39.825739
41.301111
38.918889
26.082778
27.965650
33.688007
43.607568
41.751369
41.751369
41.751369
39.811097
-121.895000
-121.895000
-118.684722
-104.936987
-72.902778
-77.012500
-80.237778
-82.230400
-84.290325
-116.348434
-87.713745
-87.713745
-87.713745
-86.114469
if available
N020ZPM10
PM25
PM25species
PM2.5, S02,
N02, Oz
Bart Ostro
Kaz Ito
Kaz Ito
Kaz Ito
Kaz Ito
Kaz Ito
Kaz Ito
Kaz Ito
Kaz Ito
Kaz Ito
Joel
Kaufman
Kaz Ito
Antonella
Zanobetti
Kaz Ito
CAOEHHA
NYU
NYU
NYU
NYU
NYU
NYU
NYU
NYU
NYU
Univ. ol
Wash.
MESA Air
project
NYU
Harvard
University
NYU
For time-series and
case-crossover, the
longer into the future,
the better the analyses.
8/1/2004-7/31/2014
2000-2003
-------
Kansas
Kansas
Louisiana
Maryland
Maryland
Massachus
etts
Massachus
etts
Massachus
etts
Michigan
Michigan
Minnesota
Minnesota
Minnesota
Kansas City
Kansas City
Baton
Rouge
Essex
Essex
Boston
Boston
Chicopee
Allen Park
Detroit
Minneapolis
Minneapolis
Minneapolis
Wyandotte
Wyandotte
East Baton
Rouge
Baltimore
Baltimore
Suffolk
Suffolk
Hampden
Wayne
Wayne
Hennepin
Hennepin
Hennepin
JFK
JFK
NULL
Essex
ESSEX
DUDLEY SQUARE
ROXBURY
DUDLEY SQUARE
ROXBURY
NULL
NULL
Phillips
ANDERSON
SCHOOL -PHILLIPS
NEIGHBORHOOD
ANDERSON
SCHOOL -PHILLIPS
NEIGHBORHOOD
20
20
22
24
24
25
25
25
26
26
27
27
27
209
209
033
005
005
025
025
013
163
163
053
053
053
0021
0021
0009
3001
3001
0042
0042
0008
0001
0001
0963
0963
0963
Medium
High
Medium
High
High
High
High
High
High
High
High
High
High
Woodward
And Franklin
Roads
Essex
2727 10th St.
Minneapolis
Population
Exposure
(Baltimore,
MD)
Population
Exposure
(Minneapoli
s-St. Paul,
MN)
39.117500
39.117500
30.461111
39.310833
39.310833
42.329444
42.329444
42.194460
42.228611
42.228333
44.955396
44.955396
44.955396
-94.635556
-94.635556
-91.176944
-76.474444
-76.474444
-71.082778
-71.082778
-72.555711
-83.208333
-83.209167
-93.25827
-93.258270
-93.258270
PM2.5, S02,
N02, Oz, CO
PM2.5
Kaz Ito
Antonella
Zanobetti
Kaz Ito
Joel
Kaufman
Kaz Ito
Kaz Ito
Antonella
Zanobetti
Kaz Ito
Kaz Ito
Antonella
Zanobetti
Joel
Kaufman
Kaz Ito
Antonella
Zanobetti
NYU
Harvard
University
NYU
Univ. ol
Wash.
MESA Air
project
NYU
NYU
Harvard
University
NYU
NYU
Harvard
University
Univ. of
Wash.
MESA Air
project
NYU
Harvard
University
2000-2003
8/1/2004-7/31/2014
2000-2003
2000-2003
8/1/2004-7/31/2014
2000-2003
-------
Mississippi
Missouri
Missouri
Montana
Nebraska
Nevada
New
Jersey
New
Jersey
New York
Gulfport
St. Louis
St. Louis
Missoula
Omaha
Reno
Elizabeth
North
Brunswick
(Township
of)
New York
Harrison
St Louis
(City)
St. Louis
City
Missoula
Douglas
Washoe
Union
Middlesex
Bronx
BEHIND HARRISON
COUNTY YOUTH
COURT
BLAIR STREET
CATEGORY A
CORE SLAM PM2.5.
BLAIR STREET
CATEGORY A
CORE SLAM PM2.5.
NULL
NULL
NULL
ELIZABETH LAB
NEW BRUNSWICK
I.S.52
New York
North
Carolina
New York
Charlotte
Bronx
Mecklenbur
9
IS 52
Garinger High School
North [Charlotte |Mecklenbur|Garinger High School
Carolina
North
Dakota
Ohio
Fargo
Cleveland
g
Cass
Cuyahoga
FARGO NW
GT CRAIG
28
29
29
30
31
32
34
34
36
36
37
37
38
39
047
510
510
063
055
031
039
023
005
005
119
119
017
035
0008
0085
0085
0031
0019
0016
0004
0006
0110
0110
0041
0041
1004
0060
High
High
High
Medium
High
Medium
High
High
High
High
High
High
High
High
E 156th St
Bet Dawson
And Kelly
Population
Exposure
(New York,
NY-
Northeaster
n New
Jersey)
30.390139
38.656300
38.656300
46.874912
41.247222
39.525083
40.641440
40.472790
40.81616
-89.049722
-90.198100
-90.198100
-113.995253
-95.975556
-119.807717
-74.208360
-74.422510
-73.90207
PM2.5, S02,
Kaz Ito
Antonella
Zanobetti
Kaz Ito
Kaz Ito
Kaz Ito
Kaz Ito
Kaz Ito
Kaz Ito
Joel
NYU
Harvard
University
NYU
NYU
NYU
NYU
NYU
NYU
Univ. ol
2000-2003
8/1/2004-7/31/2014
|N02,Oz JKaufman |Wash. |
40.816160
35.240278
-73.902070
-80.785556
35.240278 -80.785556
46.933754
41 .493955
-96.855350
-81.678542
N02 OZ PM10
CO
Kaz Ito
Adel Hanna
Kaz Ito
Kaz Ito
Kaz Ito
MESA Air
project
NYU
University
Of Noth
Carolina
NYU
NYU
NYU
01/01/06-12/21/2008
-------
Ohio
Oklahoma
Oregon
Pennsylva
nia
Pennsylva
nia
Pennsylva
nia
Pennsylva
nia
Rhode
Island
South
Carolina
Tennessee
Texas
Texas
Texas
Texas
Texas
Texas
Cleveland
Tulsa
Portland
Philadelphia
Philadelphia
Pittsburgh
Pittsburgh
Providence
Charleston
Knoxville
Dallas
Dallas
Deer Park
Deer Park
El Paso
El Paso
Cuyahoga
Tulsa
Multnomah
Philadelphi
a
Philadelphi
a
Allegheny
Allegheny
Providence
Charleston
Knox
Dallas
Dallas
Harris
Harris
El Paso
El Paso
GT CRAIG
NORTH TULSA -
FIRE STATION#24
AT 36TH AND
PEORIANR
NULL
AMS Laboratory
AMS Laboratory
NULL
NULL
BUILDING
ROOFTOP
CHARLESTON
PUBLIC WORKS
NULL
DALLAS HINTON
DALLAS HINTON
NW OF W.
LAMBUTH &
DURANT
INTERSECTION
NW OF W.
LAMBUTH &
DURANT
INTERSECTION
CHAMIZAL
CHAMIZAL
39
40
41
42
42
42
42
44
45
47
48
48
48
48
48
48
035
143
051
101
101
003
003
007
019
093
113
113
201
201
141
141
0060
1127
0080
0004
0004
0008
0008
0022
0049
1020
0069
0069
1039
1039
0044
0044
High
High
High
High
High
High
High
High
High
Medium
High
High
High
High
High
High
41 .493955
36.204902
45.496667
40.008889
40.008889
40.465556
40.465556
41.807949
32.790984
36.019440
32.819952
32.819952
29.670046
29.670046
31 .765673
31 .765673
-81 .678542
-95.976537
-122.602222
-75.097778
-75.097778
-79.961111
-79.961111
-71.415000
-79.958694
-83.873610
-96.860082
-96.860082
-95.128485
-95.128485
-106.455225
-106.455225
Antonella
Zanobetti
Kaz Ito
Kazlto
Kaz Ito
Antonella
Zanobetti
Kazlto
Antonella
Zanobetti
Kazlto
Kazlto
Kazlto
Kazlto
Antonella
Zanobetti
Kaz Ito
Antonella
Zanobetti
Kaz Ito
Antonella
Zanobetti
Harvard
University
NYU
NYU
NYU
Harvard
University
NYU
Harvard
University
NYU
NYU
NYU
NYU
Harvard
University
NYU
Harvard
University
NYU
Harvard
University
2000-2003
2000-2003
2000-2003
2000-2003
2000-2003
2000-2003
-------
Utah
Vermont
Virginia
Washingto
n
Washingto
n
Washingto
Salt Lake
City
Burlington
Not in a city
Seattle
Seattle
Seattle
n
West
Virginia
Wisconsin
SLAMS
Not in a city
Milwaukee
Salt Lake
Chittenden
Henrico
King
King
King
Kanawha
Milwaukee
UTM
COORDINATES =
PROBE LOCATION
ZAMPIERI STATE
OFFICE BUILDING,
CORNER OF
CHERRY STREET
NULL
BEACON HILL
BEACON HILL
Beacon Hill
NULL
DNR SER HQRS
SITE
California Escondido San Diego NULL
California
California
Fresno
Fresno
Fresno
Fresno
NULL
49
50
51
53
53
53
54
55
06
06
06
035
007
087
033
033
033
039
079
073
019
019
3006
0012
0014
0080
0080
0080
0011
0026
1002
0008
0008
High
Medium
High
High
High
High
Medium
High
Medium
Medium
High
4103 Beacon
Ave. S.
600 E. Valley
Pkwy.;
Escondido
3425 N First
St; Fresno
Population
Exposure
(Seattle-
Tacoma-
Bellevue,
WA)
Population
Exposure
Population
Exposure
40.736389
44.480278
37.558333
47.570273
47.570273
47.570273
-111.872222
-73.214444
-77.400278
-122.308596
-122.308596
-122.308596
PM2.5, S02,
N02, Oz, CO
38.448611
43.061111
-81.683889
-87.912500
Kaz Ito
Kaz Ito
Kaz Ito
Kaz Ito
Antonella
Zanobetti
Tim Larson
Kaz Ito
Kaz Ito
NYU
NYU
NYU
NYU
Harvard
University
Univ. d
Wash.
NYU
NYU
2000-2003
7/1/2008-6/30/2009
33.127778 -117.074167 if available Bart Ostro CAOEHHA For time-series and
36.781389
36.781389
-119.772222
-119.772222
N020ZPM10
PM25
PM25species
if available
N020ZPM10
PM25
PM25species
Bart Ostro
Antonella
Zanobetti
CAOEHHA
Harvard
University
case-crossover, the
longer into the future,
the better the analyses.
For time-series and
case-crossover, the
longer into the future,
the better the analyses.
2000-2003
-------
California
California
California
California
Illinois
Illinois
Missouri
New York
Los Angeles
Los Angeles
Los Angeles
Sacramento
Chicago
Northbrook
Not in a city
Rochester
Los
Angeles
Los
Angeles
Los
Angeles
Sacrament
0
Cook
Cook
Clay
Monroe
NULL
Los Angeles-North
Main Street
NULL
Springfield Pump
Station
Northbrook Water
Plant
Rochester 2
06
06
06
06
17
17
29
36
037
037
037
067
031
031
047
055
1103
1103
1103
0010
0057
4201
0005
1007
Medium
High
High
Medium
Medium
High
High
High
1630 N Mainl Population
St; Los
Angeles
1630 N Main
St, Los
Angeles
1309 T St.;
Sacramento
1745 N.
Springfield
750 Dundee
Road
Yarmouth Re
(RG&E
Substation)
Exposure
Population
Exposure
(Los
Angeles-
Long
Beach, CA
MSA)
Population
Exposure
Population
Exposure
(Chicago, IL
Northwester
n Indiana)
Population
Exposure
(Chicago, IL
Northwester
n Indiana)
Population
exposure
34.066590
34.06659
34.06659
38.558333
41.914733
42.14
39.303056
43.146198
-118.226880
-118.22688
-118.22688
-121.491944
-87.722725
-87.799167
-94.376389
-77.54813
if available
N020ZPM10
PM25
PM25species
PM2.5, PM10,
S02, N02, Oz
CO
if available
N020ZPM10
PM25
PM25species
PM2.5
PM2.5, PM10,
S02, N02, Oz
CO
PM2.5, S02,
Bart Ostro
Antonella
Zanobetti
Joel
Kaufman
Bart Ostro
Joel
Kaufman
Joel
Kaufman
Antonella
Zanobetti
Philip Hopke
CAOEHHA
Harvard
University
Univ. of
Wash.
MESA Air
project
CAOEHHA
Univ. d
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Harvard
University
Clarkson
For time-series and
case-crossover, the
longer into the future,
the better the analyses.
2000-2003
8/1/2004-7/31/2014
For time-series and
case-crossover, the
longer into the future,
the better the analyses.
8/1/2004-7/31/2014
8/1/2004-7/31/2014
2000-2003
6/2006to12/2009
|CO, 03 [University
-------
North Asheville Buncombe BOARD OF ED 37 021
Carolina BLDG NW CORNER
PARKING LOT
North Raleigh Wake NULL 37 183
Carolina
North Winston- Forsyth Hattie Avenue 37 067
Carolina Salem
Ohio Akron Summit 39 153
Ohio Columbus Franklin 39 049
Ohio Toledo Lucas 39 095
Pennsylva Erie Erie 42 049
nia
Pennsylva Harrisburg Dauphin 42 043
nia
Washingto Seattle King 53 033
n
Other
network
sites
CA Azusa Los Azusa 06 037
Angeles
CA Los 06 037
Angeles
0034 Medium 35.609722 -82.350833 N02 OZ PM10 Adel Hanna
CO
0014 High 35.856111 -78.574167 N02 OZ PM10 Adel Hanna
CO
0022 High 1300 Blk. Population 36.110556 -80.226667 PM2.5, PM 10, Joel
Hattie Exposure S02, N02, Oz Kaufman
Avenue (Winston-
Salem, NC)
0023 High 41.088056 -81.541667 Antonella
Zanobetti
0081 High 40.087778 -82.959722 Antonella
Zanobetti
0026 High 41.620556 -83.641389 Antonella
Zanobetti
0003 High 42.14175 -80.038611 Antonella
Zanobetti
0401 High 40.245 -76.844722 Antonella
Zanobetti
0057 High 47.563333 -122.338333 Antonella
Zanobetti
0002 Low 803 N. Loren HIGHEST OTHER 34.1365 -117.923 PM, NOx Joel
Ave., Azusa CO Kaufman
0016 Low 34.1443 -117.85 NOx Joel
Kaufman
University of 01/01/06 - 12/21/2008
North
Caroliana
University of 01/01/06 - 12/21/2008
North
Caroliana
Univ. of 8/1/2004 -7/31/2014
Wash.
MESA Air
project
Harvard 2000-2003
University
Harvard 2000-2003
University
Harvard 2000-2003
University
Harvard 2000-2003
University
Harvard 2000-2003
University
Harvard 2000-2003
University
Univ. of 8/1/2004 -7/31/201 4
Wash.
MESA Air
projGct
Univ. of 8/1/2004 -7/31/2014
Wash.
MESA Air
project
-------
CA
CA
CA
CA
CA
CA
CA
CA
CA
Burbank
Los Angeles
Reseda
Lynwood
Pico Rivera
Pico Rivera
Los
Angeles
Los
Angeles
Los
Angeles
Los
Angeles
Los
Angeles
Los
Angeles
Los
Angeles
Los
Angeles
Los
Angeles
Burbank
Reseda
Lynwood
Pico Rivera #1
Pico Rivera #2
06
06
06
06
06
06
06
06
06
037
037
037
037
037
037
037
037
037
0031
0113
1002
1103
1201
1301
1601
1602
1701
Low
Low
Low
Low
Low
High
Low
High
Low
228 W. Palm
Ave.,
Burbank
18330 Gault
St., Reseda
11220 Long
Beach Blvd.,
Lynwood
4144 San
Gabriel River
Pkwy, Pico
Rivera
OTHER
GENERAL7
BA
HIGHEST
CO
OTHER
Population
Exposure
(Los
Angeles,
CA)
HIGHEST
CO
Population
Exposure
(Los
Angeles,
CA)
POPULATIO
N
OTHER
OTHER
POPULATIO
N
MAX
PRECUR
33.7861
34.0511
34.176
34.0665
34.1992
33.92899
34.014
34.01407
34.067
-118.246
-118.456
-118.317
-118.226
-118.532
-118.21071
-118.06
-118.06995
-117.751
NOx
NOx
PM, NOx
PM, NOx
PM, NOx
PM2.5, N02,
Oz, CO
PM, NOx
PM2.5, N02,
Oz, CO
NOx
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
CA
CA
CA
CA
CA
CA
CA
CA
CA
Pasadena
Long Beach
Long Beach
Lancaster
Los
Angeles
Los
Angeles
Los
Angeles
Los
Angeles
Los
Angeles
Los
Angeles
Los
Angeles
Los
Angeles
Orange
Pasadena
North Long Beach
(Long Beach)
South Long Beach
06
06
06
06
06
06
06
06
06
037
037
037
037
037
037
037
037
059
2005
4002
4004
5001
5005
6012
9002
9033
0001
High
Low
Low
Low
Low
Low
Low
Low
Low
752 S.
Wilson Ave.,
Pasadena
3648 N.
Long Beach
Blvd., Long
Beach
1305 E.
Pacific Coast
Hwy., Long
Beach
43301
Division St.,
Lancaster,
Ca
Population
Exposure
(Los
Angeles,
CA)
HIGHEST
CO
OTHER
MAX
OZONE
UPWIND
BAG
OTHER
POPULATI
ON
POPULATI
ON
OTHER
OTHER
POPULATIO
N
POPULATIO
N
POPULATIO
N
34.1326
33.8237
33.7923
33.9228
33.9508
34.3834
34.69
34.6713
33.8306
-118.1272
-118.189
-118.175
-118.37
-118.43
-118.528
-118.131
-118.13
-117.938
PM2.5, N02,
Oz, CO
PM, NOx
PM
NOx
NOx
NOx
NOx
PM, NOx
PM, NOx
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
Anaheim
Riverside
Mission
Viejo
Palm
Springs
Riverside
Indio
Palm
Springs
Rubidoux
(West
Riverside)
Orange
Orange
Orange
Orange
Riverside
Riverside
Riverside
Riverside
Riverside
Riverside
Anaheim-Loara
School
Riverside-Magnolia
Mission Viejo
Riverside-Magnolia
Big Bear
06
06
06
06
06
06
06
06
06
06
059
059
059
059
065
065
065
065
065
065
0007
1003
2022
5001
0012
1003
2002
5001
8001
9001
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
1630 W.
Pampas
Lane
7002
Magnolia
Ave.,
Riverside
26081 Via
Pera,
Mission
Viejo, Ca
92691
Fs-590
Racquet
Club Ave,
Palm Springs
7002
Magnolia
Ave.,
Riverside
46-990
Jackson St.,
Indio
Fs-590
Racquet
Club Ave,
Palm Springs
501 W.
Valley Blvd.,
Big Bear City
POPULATI
ON
OTHER
POPULATI
ON
POPULATI
ON
HIGHEST
CO
OTHER
OTHER
HIGHEST
CO
POPULATI
ON
POPULATIO
N
UPWIND
BAG
OTHER
POPULATIO
N
POPULATIO
N
OTHER
33.8306
33.6746
33.63
33.9251
33.9208
33.946
33.7085
33.8527
33.9995
33.6764
-117.938
-117.925
-117.675
-117.952
-116.858
-117.4
-116.215
-116.541
-117.416
-117.33
PM, NOx
NOx
PM
NOx
NOx
PM
PM
PM, NOx
PM, NOx
NOx
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
Ontario
Victorville
Fontana
San
Bernardino
Thousand
Oaks
Piru
Simi Valley
San
Bernardino
San
Bernardino
San
Bernardino
San
Bernardino
San
Bernardino
San
Bernardino
Ventura
Ventura
Ventura
Ventura
Ontario-Fire Station
Fontana- Arrow
Highway
San Bernardino
06
06
06
06
06
06
06
06
06
06
071
071
071
071
071
071
111
111
111
111
0001
0025
0306
1004
2002
9004
0007
0009
1004
2002
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
1408 Francis
St.
14306 Park
Ave.,
Victorville,
Ca
14360 Arrow
Blvd.,
Fontana
24302 4th
St., San
Bernardino,
Ca
2323
Moorpark
Road,
Thousand
Oaks
3301 Pacific
Avenue, Piru,
Ca 93040
5400
Cochran
Street, Simi
Valley
POPULATI
ON
OTHER
POPULATI
ON
GENERAL/
BA
HIGHEST
CO
HIGHEST
CO
OTHER
OTHER
OTHER
HIGHEST
CO
REGIONAL
T
POPULATIO
N
REGIONAL
T
HIGHEST
CO
OTHER
OTHER
POPULATIO
N
POPULATIO
N
POPULATIO
N
OTHER
34.895
34.0372
34.51
34.1037
34.1
34.1068
34.21
34.4046
34.4483
34.2775
-117.023
-117.69
-117.33
-117.629
-117.492
-117.274
-118.869
-118.81
-119.23
-118.684
NOx
PM
PM, NOx
NOx
PM, NOx
PM, NOx
PM, NOx
PM
NOx
PM, NOx
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
CA
CA
CT
CT
CT
CT
CT
CT
CT
CT
CT
El Rio
Ventura
Ventura
Fairfield
Fairfield
Fairfield
Fairfield
Fairfield
Fairfield
Hartford
New Haven
New Haven
06
06
09
09
09
09
09
09
09
09
09
111
111
001
001
001
001
001
001
003
009
009
2003
3001
0010
0113
1123
2124
3005
9003
1003
0018
0026
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Rio Mesa
School, El
Rio
GENERAL/
BA
GENERAL/
BA
GENERAL/
BA
HIGHEST
CO
POPULATI
ON
HIGHEST
CO
HIGHEST
CO
GENERAL/
BA
GENERAL/
BA
HIGHEST
CO
POPULATI
ON
POPULATIO
N
HIGHEST
CO
HIGHEST
CO
OTHER
POPULATIO
N
POPULATIO
N
POPULATIO
N
HIGHEST
CO
OTHER
34.2804
34.255
41.1708
41.1836
41 .3991
41 .063
41.1125
41.1183
41 .7847
41 .2938
41.2911
-119.313
-119.142
-73.1947
-73.1902
-73.443
-73.5288
-73.4072
-73.3366
-72.6316
-72.9013
-72.8941
NOx
PM, NOx
PM
PM
PM
PM
PM
PM, NOx
NOx
PM
PM
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
CT
CT
CT
CT
CT
DE
DE
DE
DE
DE
DE
Dover
Bellefonte
Newark
Wilmington
New Haven
New Haven
New Haven
New Haven
New Haven
Kent
Kent
New Castle
New Castle
New Castle
New Castle
09
09
09
09
09
10
10
10
10
10
10
009
009
009
009
009
001
001
003
003
003
003
0027
1123
2008
2123
9005
0002
0003
1003
1007
1012
2004
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
State Roac
384, Killens
Pond Rd
Water St.
Dover
River Roac
Park
Lums Pone
State Park
Univ. Del.
North
Campus
Mlk Blvd Anc
GENERAL/
BA
HIGHEST
CO
POPULATI
ON
HIGHEST
CO
HIGHEST
CO
GENERAL/
BA
POPULATI
ON
HIGHEST
CO
OTHER
OTHER
HIGHEST
HIGHEST
CO
OTHER
OTHER
MAX
PRECUR
OTHER
POPULATIO
N
POPULATIO
N
POPULATIO
N
OTHER
41.3011
41.3108
41.3313
41 .5505
41.3411
38.9847
39.155
39.7611
39.5511
39.6919
39.7394
-72.9027
-72.9169
-72.9197
-73.0436
-72.9213
-75.5555
-75.518
-75.4919
-75.7308
-75.7616
-75.558
PM, NOx
PM, NOx
PM
PM
PM, NOx
PM
PM
PM
PM
PM
PM, NOx
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
DE
DC
DC
DC
DC
IL
IL
Seaford
Chicago
Chicago
Sussex
District of
Columbia
District of
Columbia
District of
Columbia
District of
Columbia
Cook
Cook
Farr Dormitory
Washington
10
11
11
11
11
17
17
005
001
001
001
001
031
031
1002
0025
0041
0042
0043
0014
0022
Low
Low
Low
Low
Low
Low
Medium
350 Virginia
Ave Seaford
3300 S
Michigan Ave
3535 E.
114th St.
OTHER
POPULATI
ON
HIGHEST
CO
GENERAL/
BA
HIGHEST
CO
POPULATI
ON
Population
Exposure
(Chicago, IL
Northwester
n Indiana)
POPULATIO
N
POPULATIO
N
HIGHEST
CO
MAX
OZONE
38.6444
38.9752
38.8972
38.8808
38.9188
41 .8342
41.689195
-75.613
-77.0227
-76.9527
-77.0325
-77.0125
-87.6238
-87.539318
PM
NOx
PM, NOx
PM
PM, NOx
PM
PM2.5
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
IL
IL
IL
IL
IL
Chicago
Chicago
Chicago
Cook
Cook
Cook
Cook
Cook
S.E. Chicago
Mayfair Pumping Stn.
Springfield Pump
Station
17
17
17
17
17
031
031
031
031
031
0050
0052
0057
0063
0075
Medium
Medium
Low
Low
Low
103rd Anc
Luella
4850 Wilson
Ave.
1745 N.
Springfield
Source
Oriented
(Chicago, IL
Northwester
n Indiana),
Population
Exposure
(Chicago, IL
Northwester
n Indiana)
Population
Exposure
(Chicago, IL
Northwester
n Indiana),
Highest
Concentrati
on
(Chicago, IL
Northwester
n Indiana)
POPULATI
ON
HIGHEST
CO
POPULATI
POPULATIO
N
41 .709561
41 .967429
41.9147
41 .8772
41 .9641
-87.568576
-87.749819
-87.7227
-87.6344
-87.6586
PM2.5, S02
PM2.5, PM10
PM
NOx
NOx
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
IL
IL
IL
IL
IL
IL
IL
IL
IL
Chicago
Mccook
Blue Island
Schiller Park
Summit
Des Plaines
Northbrook
Cicero
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Lawndale Comm-Ed
Northbrook Water
Plant
17
17
17
17
17
17
17
17
17
031
031
031
031
031
031
031
031
031
0076
1016
2001
3103
3301
4002
4007
4201
6005
Low
Low
Medium
Low
Low
Low
High
Low
Low
7801
Lawndale
50th St. Anc
Glencoe
12700
Sacramento
4743
Mannheim
Rd.
60th St. &
74th Ave.
9511 W.
Harrison St
750 Dundee
Road
13th St. &
GENERAL7
BA
HIGHEST
CO
Population
Exposure
(Chicago, IL
Northwester
n Indiana)
HIGHEST
CO
POPULATI
ON
HIGHEST
CO
Population
Exposure
(Chicago, IL
Northwester
n Indiana)
MAX
OZONE
POPULATI
HIGHEST
CO
POPULATIO
N
POPULATIO
N
POPULATIO
N
POPULATIO
N
41.7513
41.8011
41 .663997
41 .9652
41 .7827
41 .8552
42.060278
42.14
41 .8642
-87.7137
-87.8319
-87.696468
-87.8763
-87.8052
-87.7524
-87.863333
-87.7991
-87.7488
PM, NOx
PM
PM2.5, PM10
PM, NOx
PM
NOx
PM2.5, Oz
PM, NOx
PM
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
IL
IL
IL
IL
IL
IL
IL
IN
IN
IN
IN
Naperville
Elgin
Pittsboro
Cook
DuPage
Kane
Lake
McHenry
Will
Will
Gibson
Hendricks
Hendricks
Hendricks
City Hall
17
17
17
17
17
17
17
18
18
18
18
031
043
089
097
111
197
197
051
063
063
063
8003
4002
0003
1007
0001
1002
1011
0010
0001
0002
0003
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
400 S. Eagle
St.
258 Lovell
St.
Illinois Beach
State Park
Cr 800 N
And Cr 275
E
206 N.
Meridian St.
High School,
POPULATI
ON
POPULATI
ON
POPULATI
ON
EXTREME
DO
POPULATI
ON
HIGHEST
CO
GENERAL/
BA
HIGHEST
CO
HIGHEST
CO
HIGHEST
HIGHEST
CO
POPULATIO
N
POPULATIO
N
OTHER
OTHER
OTHER
41.6313
41.7711
42.0502
42.4675
42.2214
41 .5266
41.2215
38.2762
39.8769
39.8633
39.8808
-87.568
-88.1525
-88.2802
-87.81
-88.242
-88.1163
-88.1909
-87.5529
-86.4738
-86.4707
-86.5421
NOx
PM
PM
PM
PM
PM
NOx
NOx
NOx
NOx
NOx
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
IN
IN
IN
IN
IN
IN
IN
IN
IN
East
Chicago
Gary
Gary
Griffith
Gary
Hammond
Hammond
Indianapolis
Lake
Lake
Lake
Lake
Lake
Lake
Lake
Lake
Marion
18
18
18
18
18
18
18
18
18
089
089
089
089
089
089
089
089
097
0006
0022
0026
0027
1003
1016
2004
2010
0073
High
High
High
High
High
High
High
High
Low
Franklin
School Alder
&142ndSt
201
Mississippi
St., litr
Bunker
25th Anc
Burr Street
Ready Eldon
School, 1345
N. Broad St.
Ivanhoe
School 15th
& Gerry Sts
Purdue Univ
Calumet-
Powers
Building
6937
1921 Davis
St.,
Robertsdale,
Clark H.S.
Naval
Avionics
Center, 6125
E. 16th St.
HIGHEST
CO
HIGHEST
CO
HIGHEST
CO
POPULATI
ON
POPULATI
ON
HIGHEST
CO
POPULATI
ON
POPULATI
ON
HIGHEST
CO
POPULATIO
N
POPULATIO
N
POPULATIO
N
POPULATIO
N
OTHER
41 .6361
41 .6066
41 .573
41 .5466
41 .5888
41 .6002
41 .5852
41 .6783
39.7891
-87.4408
-87.3047
-87.4058
-87.4263
-87.4077
-87.3347
-87.4744
-87.5083
-86.0608
PM
PM, NOx
PM
PM
PM
PM
PM
PM
NOx
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
IN
IN
IN
IN
IA
IA
IA
IA
IA
IA
Ogden
Dunes
(Wickliffe)
Evansville
Waterloo
Clinton
Iowa City
Cedar
Rapids
Porter
St. Joseph
Spencer
Vanderburg
h
Black Hawk
Cerro
Gordo
Clinton
Emmet
Johnson
Linn
18
18
18
18
19
19
19
19
19
19
127
141
147
163
013
033
045
063
103
113
0024
1008
0008
0012
0008
0019
0021
0003
2001
0033
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
84 Diana Rd/
Water
Treatment
Plant
425 West Mill
Road/ Fire
Station #17
Roosevelt St.
2200 East
Court
408 E. Linn
St. Coggon,
Iowa
HIGHEST
CO
OTHER
HIGHEST
CO
HIGHEST
CO
OTHER
OTHER
GENERAL/
BA
POPULATI
ON
POPULATI
ON
POPULATIO
N
POPULATIO
N
OTHER
POPULATIO
N
POPULATIO
N
POPULATIO
N
POPULATIO
N
41.6175
41 .6936
37.9811
38.0216
42.493
43.1616
41 .8749
43.3975
41 .6573
42.2805
-87.1991
-86.2366
-87.0325
-87.5694
-92.3438
-93.2083
-90.1774
-94.8172
-91 .5034
-91 .5269
PM
NOx
NOx
NOx
PM
PM
PM
PM
PM
NOx
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
IA
IA
IA
IA
IA
IA
IA
IA
IA
Cedar
Rapids
Muscatine
Emmetsburg
Des Moines
Des Moines
Clive
Davenport
Linn
Muscatine
Palo Alto
Polk
Polk
Polk
Polk
Polk
Scott
19
19
19
19
19
19
19
19
19
113
139
147
153
153
153
153
153
163
0037
0015
1002
0030
0058
0059
2510
2520
0014
Low
Low
Low
Low
Low
Low
Low
Low
Medium
1599 Wenig
RdNe
1409
Wisconsin
Iowa Lakes
Community
College
1907
Carpenter,
Des Moines
Iowa
Se 18th Anc
Scott,
National By-
products
9401 Indian
Hills Drive,
Clive 50325
Scott County
Park
POPULATI
ON
POPULATI
ON
GENERAL/
BA
OTHER
POPULATI
ON
HIGHEST
CO
OTHER
OTHER
GENERAL/
BA
POPULATIO
N
POPULATIO
N
OTHER
POPULATIO
N
POPULATIO
N
POPULATIO
N
42.0083
41 .4008
43.1233
41 .603
41 .6077
41 .5833
41 .6027
41 .6647
41 .6991
-91 .6786
-91 .0677
-94.6933
-93.643
-93.5719
-93.5838
-93.7477
-93.6141
-90.5219
PM
PM
PM
PM
NOx
PM
PM
PM
NOx
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
IA
IA
IA
IA
MD
MD
MD
MD
MD
MD
Davenport
Davenport
Clarion
Fort Meade
(U.S. Army)
Glen Burnie
Riviera
Beach
Cockeysville
Essex
Scott
Scott
Story
Wright
Anne
Arundel
Anne
Arundel
Anne
Arundel
Anne
Arundel
Baltimore
Baltimore
Davidsonville Family
Recreation Center
19
19
19
19
24
24
24
24
24
24
163
163
169
197
003
003
003
003
005
005
0018
0019
2530
0004
0014
0019
1003
2002
1007
3001
Medium
Medium
Low
Low
High
Low
High
Low
High
High
3029 N
Division St.
Davenport
300 Wellman
St.
Davenport
2446 Quincy
Ave. Clarion
Queen Anne
And Wayson
Roads
9001
YStreet.Ft.M
eade
7409 Balto
And
Annapolis
Blvd
8515 Jenkins
Rd Riviera
Beach
Padonia E.S.
9834
Greenside
Dr. Cockeysv
600 Dorsey
Avenue,
POPULATI
ON
POPULATI
ON
OTHER
GENERAL/
BA
POPULATI
ON
GENERAL/
BA
POPULATI
ON
GENERAL/
BA
GENERAL/
BA
HIGHEST
CO
SOURCE
ORI
POPULATIO
N
POPULATIO
N
POPULATIO
N
POPULATIO
N
HIGHEST
CO
MAX
PRECUR
41.55
41.5177
42.0413
42.6953
38.9025
39.1011
39.1695
39.1597
39.4608
39.3108
-90.6
-90.6186
-93.6138
-93.6559
-76.653
-76.7294
-76.6279
-76.5116
-76.6311
-76.4744
PM
PM
PM
PM
PM
PM, NOx
PM
PM
PM
PM, NOx
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
Air
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
MD
MD
MD
MD
MD
MD
MD
MD
MD
Edgewood
Rockville
Beltsville
Greater
Upper
Marlboro
Hagerstown
Baltimore
Baltimore
Baltimore
Cecil
Harford
Montgomer
y
Prince
George's
Prince
George's
Washington
Baltimore
Baltimore
Baltimore
NEPS
NWPS
SEPS
24
24
24
24
24
24
24
24
24
015
025
031
033
033
043
510
510
510
0003
1001
3001
0030
8003
0009
0006
0007
0008
Low
High
Medium
Medium
Medium
Low
Medium
Medium
Medium
4600
Telegraph
Road,
Fairhill, Cecil
Co.
Edgewood
Army Chem
Center,
Waehli Road
Lathrop E.
Smith
Env.Ed
Center
51 10 Me
Howard
University's
Beltsville
Laborator
P.G. Co.
Equestrian
Cntr, 14900
Pennsylv
18530
Roxbury
Road,
Hagerstown
N E Police
Sta, 1900
Argonne Dr,
Balto
N W Police
Station 5700
Reistertown
Rd.
S E Police
POPULATI
ON
HIGHEST
CO
POPULATI
ON
GENERAL/
BA
POPULATI
ON
POPULATI
ON
Population
Exposure
(Baltimore,
MD)
Population
Exposure
(Baltimore,
MD)
Population
POPULATIO
N
HIGHEST
CO
39.7011
39.41
39.1144
39.0552
38.8119
39.5655
39.340556
39.344444
39.28768
-75.86
-76.2966
-77.1069
-76.8783
-76.7441
-77.7219
-76.582222
-76.685278
-76.547616
PM
PM
PM
NOx
PM
PM
PM2.5
PM2.5
PM2.5
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Air
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
MD
MD
MD
MN
MN
MN
MN
MN
MN
Baltimore
Baltimore
Baltimore
Blaine
Cloquet
Rosemount
Apple Valley
Baltimore
(City)
Baltimore
Baltimore
(City)
Anoka
Carlton
Dakota
Dakota
Dakota
Dakota
Oldtown
24
24
24
27
27
27
27
27
27
510
510
510
003
017
037
037
037
037
0035
0040
0049
1002
7416
0020
0423
0470
6018
High
Medium
High
High
Low
High
High
High
Low
Fmc Corp.
1701 E
Patapsco
Avenue
Old Town
Fire Station
1100 Hillen
Street
Anoka
County
Airport 2289
Co. Rd. J
175
University Rd
12821 Pine
Bend Trail
2142 120th
Street East
225 Garden
View Drive
GENERAL/
BA
Population
Exposure
(Baltimore,
MD)
POPULATI
ON
GENERAL/
BA
GENERAL/
BA
POPULATI
ON
POPULATI
ON
HIGHEST
CO
POPULATI
ON
HIGHEST
CO
POPULATIO
N
POPULATIO
N
SOURCE
ORI
SOURCE
ORI
OTHER
REGIONAL
T
39.2327
39.298056
39.2616
45.1397
46.7052
44.7653
44.775
44.7407
44.75
-76.5797
-76.604722
-76.6375
-93.2076
-92.5236
-93.0324
-93.0627
-93.2372
-92.8877
PM
PM2.5
PM
NOx
NOx
NOx
NOx
PM
PM
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
MN
MN
MN
MN
MN
MN
MN
MN
MN
Minneapolis
Minneapolis
Minneapolis
St. Louis
Park
Rochester
St. Paul
St. Paul
Hennepin
Hennepin
Hennepin
Hennepin
Mille Lacs
Olmsted
Ramsey
Ramsey
Ramsey
Richfield
Phillips
St. Louis Park
Red Rock Road
St Paul Health
Centre
27
27
27
27
27
27
27
27
27
053
053
053
053
095
109
123
123
123
0961
0963
1007
2006
3051
5008
0864
0866
0868
High
High
High
High
Low
Medium
Low
Medium
Medium
7020 12th
Ave S,
Minneapolis,
Mn
27271 Qh St.
Mpls
4646
Humboldt
Ave. N.
5005
Minnetonka
Blvd.
Her 67 Box
194
1801 9th Ave
S. E.
Rochester,
Mn 55904
1450 Rec
Rock Road,
St. Paul, Mn
555 Cedar
Street
POPULATI
ON
POPULATI
ON
POPULATI
ON
POPULATI
ON
POPULATI
ON
GENERAL/
BA
POPULATI
ON
Highest
Concentrati
on
(Minneapoli
s-St. Paul,
MN)
Population
Exposure
WELFARE
RE
SOURCE
ORI
REGIONAL
T
HIGHEST
CO
44.8775
44.9553
45.0418
44.95
46.207
43.9969
44.9919
44.899379
44.952442
-93.2588
-93.2582
-93.2987
-93.3428
-93.7594
-92.4503
-93.183
-93.017155
-93.098475
PM
PM
PM
PM
PM
PM
NOx
PM2.5, PM10
PM2.5, PM10
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
Air
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
MN
MN
MN
MN
MN
MN
MN
MO
NJ
St. Paul
Virginia
Duluth
Duluth
Shakopee
St. Cloud
Fort Lee
Ramsey
Ramsey
Saint Louis
Saint Louis
Saint Louis
Scott
Stearns
Mercer
Bergen
Harding High School
Fort Lee Library
27
27
27
27
27
27
27
29
34
123
123
137
137
137
139
145
129
003
0871
0872
7001
7550
7551
0505
3052
0001
0003
Medium
Low
Low
Low
Low
High
Low
Low
High
1540 East
6th Street
City Hall
Roof
1202 East
University
Circle
2424 W 5th
St
917 Dakota
St.,
Shakopee,
Mn 55379
1321
Michigan
Ave, St.
Cloud Mn
56304
Fort Lee
Library.Cente
r Avenue
Population
Exposure
(Minneapoli
s-St. Paul,
MN)
POPULATI
ON
POPULATI
ON
POPULATI
ON
HIGHEST
CO
POPULATI
ON
POPULATI
ON
OTHER
POPULATI
ON
POPULATIO
N
SOURCE
ORI
44.961451
44.9311
47.5233
46.8201
46.7666
44.7914
45.5498
40.56
40.8516
-93.035894
-93.156
-92.5363
-92.0894
-92.133
-93.5125
-94.1334
-93.4183
-73.9733
PM2.5
PM
PM
PM
PM
PM
PM
NOx
PM
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
Pennsauken
(Pensauken)
Newark
Jersey City
Lawrence
(Township
Of)
Trenton
North
Brunswick
(Township
Of)
East
Brunswick
(Township
Of)
Morristown
Camden
Essex
Hudson
Mercer
Mercer
Mercer
Middlesex
Middlesex
Morris
34
34
34
34
34
34
34
34
34
007
013
017
021
021
021
023
023
027
1007
0015
1003
0005
0008
8001
0006
0011
0004
Low
High
High
Low
Low
Low
Medium
Medium
High
Pennsauken
Twp; Morris-
Delair Wtp
Mary Willis
Cultural Ctr,
18th
Av, Newark
355 Newark
Ave.Consolid
ated Fire
House
Rider
College;Lawr
ence
Township
120
Academy
Street,
Trenton
Public Libr.
Washington
Crossing
State Park
Cook
College, Log
Cabin Road
R.U. Veg
Research
Farm
3,Ryders Ln,
Newb
16 Early St,
Morristown
HIGHEST
CO
POPULATI
ON
HIGHEST
CO
HIGHEST
CO
HIGHEST
CO
POPULATI
ON
HIGHEST
CO
GENERAL/
BA
POPULATI
ON
POPULATIO
N
POPULATIO
N
MAX
OZONE
POPULATIO
N
REGIONAL
T
POPULATIO
N
POPULATIO
N
39.9888
40.7319
40.7254
40.283
40.2222
40.3124
40.4727
40.4621
40.803
-75.0491
-74.2052
-74.0522
-74.7426
-74.7636
-74.8726
-74.4225
-74.4294
-74.4833
PM
PM
PM
NOx
PM
PM
PM
NOx
PM
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
NJ
NJ
NJ
NJ
NJ
NY
NY
Paterson
Elizabeth
Elizabeth
Rahway
Phillipsburg
New York
New York
Passaic
Union
Union
Union
Warren
Bronx
Bronx
Morrisania
NY Botanical
Gardens
34
34
34
34
34
36
36
031
039
039
039
041
005
005
0005
0004
0006
2003
0006
0080
0083
High
High
High
High
High
Medium
Medium
Health
Department
176
Broadway
New Jersey
Turnpike
Interchange
13
Mitchell
Building,600
North Broac
Street
Rahway Fire
Dept, 1300
Main Street
Pburg
Municipal
Bldg, 675
Corliss Ave
Morrisania
Center, 1225
57 Gerarc
Ave.
200th Street
And
Southern
Blvd
POPULATI
ON
HIGHEST
CO
HIGHEST
CO
POPULATI
ON
POPULATI
ON
Population
Exposure
(New York,
NY-
Northeaster
n New
Jersey)
Population
Exposure
(New York,
NY-
Northeaster
n New
Jersey)
POPULATIO
N
POPULATIO
N
40.9186
40.6414
40.673
40.606
40.6872
40.83608
40.86586
-74.1677
-74.2083
-74.2136
-74.2749
-75.1813
-73.92021
-73.88075
PM
PM
PM
PM
PM
PM2.5
PM2.5, S02,
N02, CO
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
NY
NY
NY
NY
NY
NY
NY
NY
NY
New York
East
Meadow
Cedarhurst
Dutchess
Kings
Kings
Kings
Nassau
Nassau
Nassau
Nassau
New York
36
36
36
36
36
36
36
36
36
027
047
047
047
059
059
059
059
061
1004
0052
0076
0122
0005
0008
0012
0013
0010
Low
Low
Low
High
Low
Low
Low
Low
High
Jhs 126 424
Leonard St
Eisenhower
Park.Merrick
Av&Old
Country R
Lawrence
High
School ,Arling
ton Place
OTHER
HIGHEST
CO
OTHER
POPULATI
ON
GENERAL/
BA
POPULATI
ON
OTHER
OTHER
HIGHEST
CO
POPULATIO
N
OTHER
POPULATIO
N
HIGHEST
CO
POPULATIO
N
POPULATIO
N
OTHER
41 .6948
40.6415
40.6718
40.7198
40.7432
40.631
40.789
40.7607
40.7394
-73.9144
-74.0183
-73.9782
-73.9478
-73.5854
-73.7347
-73.6364
-73.4906
-73.9861
PM
PM
PM
PM
NOx
PM
PM
PM
PM
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
NY
NY
NY
NY
NY
NY
NY
NY
NY
New York
New York
New York
Newburgh
New York
New York
New York
New York
Orange
Queens
Queens
Queens
Queens
Queens
JHS45
Queens College 2
36
36
36
36
36
36
36
36
36
061
061
061
071
081
081
081
081
081
0062
0079
0128
0002
0094
0096
0097
0098
0124
High
Medium
High
Low
Low
Low
Low
Low
High
Post
Office,350
Canal Street
School Is 45,
2351 1st
Avenue
Ps 19 185
1st Avenue
55 Broadway
14439
Gravett Road
GENERAL/
BA
Population
Exposure
(New York,
NY-
Northeaster
n New
Jersey)
POPULATI
ON
GENERAL/
BA
OTHER
OTHER
GENERAL/
BA
GENERAL/
BA
GENERAL/
BA
HIGHEST
CO
POPULATIO
N
POPULATIO
N
POPULATIO
N
OTHER
SOURCE
ORI
OTHER
40.7205
40.79937
40.73
41 .4994
40.7779
40.7703
40.7552
40.7842
40.7362
-74.004
-73.93334
-73.9844
-74.0097
-73.8431
-73.8284
-73.7586
-73.8475
-73.8231
PM
PM2.5
PM
PM
PM
PM
NOx
NOx
PM, NOx
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
Air
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
NY
NY
NY
NC
NC
NC
NC
NC
NC
New York
Mamaronec
k
Burlington
Black
Mountain
Fayetteville
Durham
Richmond
Suffolk
Westcheste
r
Alamance
Buncombe
Caswell
Chatham
Cumberlan
d
Durham
36
36
36
37
37
37
37
37
37
085
103
119
001
021
033
037
051
063
0067
0001
1002
0002
0034
0001
0004
0009
0001
High
Low
High
High
Medium
Low
Medium
Medium
Medium
Susan
Wagner Hs,
Brielle Ave.S
Manor Rd,
5th Avenue
& Madison,
Thruway Exit
9
827 S
Graham &
Hopedale Rd
175 Bingham
Road
Asheville Nc
7074 Cherry
Grove Rd,
Reidsville
Rt 4 Box 62
4533
Raeford Rd
Health Dept,
300 E Main
GENERAL/
BA
POPULATI
ON
POPULATI
ON
EXTREME
DO
POPULATI
ON
GENERAL/
BA
GENERAL/
BA
POPULATI
ON
HIGHEST
CO
POPULATIO
N
POPULATIO
N
POPULATIO
N
POPULATIO
N
POPULATIO
N
40.5973
40.7458
40.93
36.089
35.6097
36.307
35.7572
35.0414
35.9919
-74.1261
-73.4202
-73.7692
-79.4078
-82.3508
-79.4674
-79.1597
-78.9531
-78.8963
PM
PM
PM
PM
PM
PM
PM
PM
PM
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
NC
NC
NC
NC
NC
NC
NC
NC
Rocky
Mount
Winston-
Salem
Winston-
Salem
Greensboro
Charlotte
Spruce Pine
Candor
Edgecomb
e
Forsyth
Forsyth
Guilford
Guilford
Mecklenbur
9
Mitchell
Montgomer
y
North Forsyth H.S.
Clemmons
Mendenhall
37
37
37
37
37
37
37
37
065
067
067
081
081
119
121
123
0004
0024
0030
0009
0013
0041
0001
0001
Medium
Low
High
Low
Low
Medium
Low
Low
900
Springfield
Road
North
Forsyth High
School
Fraternity
Church Road
205
Wiloughby
Blvd
1130
Eastway
Drive
City Hall
Summit St
112 Perry
Drive,
GENERAL/
BA
POPULATI
ON
Population
Exposure
(Winston-
Salem, NC)
POPULATI
ON
Population
Exposure
(Greensbor
o, NC),
General/Ba
ckground
(Greensbor
o, NC)
OTHER
GENERAL/
BA
GENERAL/
BA
POPULATIO
N
POPULATIO
N
POPULATIO
N
POPULATIO
N
35.9335
36.1713
36.026
36.0758
36.109167
35.2402
35.9152
35.26
-77.75
-80.2819
-80.342
-79.7944
-79.801111
-80.7855
-82.0733
-79.84
PM
{shut down
Jan. '06}
PM2.5, Oz
PM
PM2.5, PM10,
Oz
NOx
PM
PM
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
NC
NC
NC
NC
NC
Ohio
PA
PA
PA
PA
Lumberton
Raleigh
Boone
Goldsboro
Dayton
Robeson
Wake
Wake
Watauga
Wayne
Montgomer
y
Adams
Berks
Bucks
Chester
37
37
37
37
37
39
42
42
42
42
155
183
183
189
191
113
001
011
017
029
0005
0014
0015
0003
0005
0031
0001
0009
0012
0100
Low
Medium
Low
Low
Medium
High
Low
Low
Low
Low
1170
Linkhaw
Road
3801 Spring
Forest Rd.
361
Jefferson
Road, Boone
Dillard
Middle
School,
Devereau St
GENERAL/
BA
GENERAL/
BA
POPULATI
ON
EXTREME
DO
POPULATI
ON
EXTREME
DO
HIGHEST
CO
OTHER
POPULATI
ON
POPULATIO
N
MAX
OZONE
GENERAL/B
A
POPULATIO
N
OTHER
POPULATIO
N
REGIONAL
T
34.6425
35.8561
35.79
36.2219
35.3692
39.759444
39.92
40.3202
40.1072
39.8344
-78.9902
-78.5741
-78.6197
-81 .663
-77.9938
-84.144444
-77.31
-75.9266
-74.8822
-75.7686
PM
PM
PM
PM
PM
PM, NOx
NOx
PM, NOx
PM
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Antonella
Zanobetti
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Harvard
University
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
2000-2003
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
PA
PA
PA
PA
PA
PA
PA
PA
PA
PA
Dauphin
Delaware
Lackawann
a
Lancaster
Lehigh
Luzerne
Montgomer
y
Northampto
n
Perry
Philadelphi
a
42
42
42
42
42
42
42
42
42
42
043
045
069
071
077
079
091
095
099
101
0401
0002
2006
0007
0004
1101
0013
0025
0301
0004
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
HIGHEST
CO
HIGHEST
CO
HIGHEST
CO
HIGHEST
CO
OTHER
OTHER
OTHER
OTHER
GENERAL/
BA
HIGHEST
CO
OTHER
OTHER
OTHER
OTHER
POPULATIO
N
POPULATIO
N
POPULATIO
N
POPULATIO
N
POPULATIO
N
POPULATIO
N
40.245
39.8355
41 .4427
40.0466
40.6119
41 .2655
40.1122
40.628
40.4569
40.0088
-76.8447
-75.3725
-75.623
-76.2833
-75.4325
-75.8463
-75.3091
-75.3411
-77.1655
-75.0977
PM, NOx
NOx
NOx
PM, NOx
PM, NOx
NOx
PM, NOx
PM, NOx
PM, NOx
PM
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
PA
PA
PA
PA
SC
SC
SC
SC
SC
SC
Philadelphi
a
Philadelphi
a
Philadelphi
a
York
Chesterfiel
d
Florence
Greenville
Greenville
Greenwood
Lexington
42
42
42
42
45
45
45
45
45
45
101
101
101
133
025
041
045
045
047
063
0024
0047
0136
0008
0001
0002
0008
0009
0003
0008
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
POPULATI
ON
HIGHEST
CO
HIGHEST
CO
HIGHEST
CO
GENERAL/
BA
OTHER
OTHER
GENERAL/
BA
OTHER
GENERAL/
POPULATIO
N
POPULATIO
N
OTHER
POPULATIO
N
POPULATIO
N
POPULATIO
N
OTHER
POPULATIO
N
OTHER
40.0763
39.9447
39.9275
39.9652
34.6171
34.1676
34.8404
34.901
34.2145
34.0528
-75.0119
-75.1661
-75.2227
-76.6994
-80.1987
-79.8504
-82.4029
-82.313
-82.1731
-81.1549
PM
PM
PM
PM, NOx
PM
PM
PM
PM
PM
PM
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
sc
sc
Texas
VA
VA
VA
VA
VA
Wl
Wl
Port Arthur
Madison
Richland
Spartanbur
9
Jefferson
Arlington
Fairfax
Fairfax
Fairfax
Loudoun
Dane
Dane
45
45
48
51
51
51
51
51
55
55
079
083
245
013
059
059
059
107
025
025
0019
0010
0022
0020
0030
1005
5001
1005
0025
0047
Low
Low
High
Low
Low
Low
Low
Low
Low
Low
City Well #6,
2557
OTHER
OTHER
POPULATI
ON
GENERAL/
BA
POPULATI
ON
POPULATI
ON
POPULATI
ON
HIGHEST
CO
HIGHEST
CO
POPULATIO
N
POPULATIO
N
HIGHEST
CO
POPULATIO
N
POPULATIO
N
33.9932
34.9268
29.863889
38.8575
38.7727
38.8375
38.9319
39.0244
43.0819
43.0733
-81 .0241
-82.0052
-94.317778
-77.0591
-77.1055
-77.1632
-77.1988
-77.49
-89.3766
-89.4358
PM
PM
PM
PM
PM
PM
PM
PM
PM
Joel
Kaufman
Joel
Kaufman
Antonella
Zanobetti
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Harvard
University
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
Air
8/1/2004-7/31/2014
8/1/2004-7/31/2014
2000-2003
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Pleasant
Prairie
Milwaukee
Milwaukee
Milwaukee
Milwaukee
Douglas
Grant
Jefferson
Kenosha
Milwaukee
Milwaukee
Milwaukee
Milwaukee
Milwaukee
55
55
55
55
55
55
55
55
55
031
043
055
059
079
079
079
079
079
0025
0009
0008
0019
0010
0026
0041
0043
0050
Low
Low
Low
High
Medium
Medium
Medium
Medium
Low
128 Hwy 61,
Potosi
Township
Chiwaukee
Prairie,
11838 First
Court
Health
Center, 1337
So16thSt
Dnr Ser
Hdqrts, 2300
N M. L. King
JrDr
Uwm North
Campus,
2114 E
Kenwood
Blvd
Virginia Fire
Station, 100
W Virginia St
HIGHEST
CO
POPULATI
ON
HIGHEST
CO
GENERAL/
BA
OTHER
HIGHEST
CO
HIGHEST
CO
POPULATI
ON
POPULATI
REGIONAL
T
HIGHEST
CO
POPULATIO
N
MAX
PRECUR
MAX
PRECUR
SOURCE
ORI
46.7302
42.6921
43.1838
42.5047
43.0166
43.0611
43.0752
43.0264
43.0977
-92.0797
-90.6863
-88.9941
-87.8093
-87.9333
-87.9125
-87.8844
-87.9111
-88.0077
PM
PM
PM
PM
PM
PM, NOx
NOx
PM
PM
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. ol
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Milwaukee
Milwaukee
Grafton
Somerset
Waukesha
Milwaukee
Milwaukee
Ozaukee
Ozaukee
St. Croix
Sauk
Taylor
Waukesha
Waukesha
55
55
55
55
55
55
55
55
55
079
079
089
089
109
111
119
133
133
0059
0099
0008
0009
1002
0007
8001
0027
0034
Medium
Medium
Medium
Medium
High
Low
Low
Medium
Low
Federal
Aviation
Adm, 4942 S
16thSt
Milw Fire
DeptHq, 711
W Wells St
Grafton,
Hwy32 And
I43
Harrington
Beach State
Park, 531
HwyD
Hwy 64,
Somerset
Town Hall
Devils Lake
State Park,
E12886
Tower Rd
1 Mi E.
Perkinstown
OnSr.M
1310
Cleveland
Ave
GENERAL/
BA
HIGHEST
CO
HIGHEST
CO
GENERAL/
BA
POPULATI
ON
GENERAL/
BA
GENERAL/
BA
HIGHEST
CO
POPULATI
ON
HIGHEST
CO
POPULATIO
N
POPULATIO
N
HIGHEST
CO
REGIONAL
T
OTHER
POPULATIO
N
OTHER
42.955
43.0397
43.343
43.498
45.1244
43.4355
45.2038
43.0202
43.0072
-87.9341
-87.9205
-87.9208
-87.81
-92.6625
-89.6802
-90.6
-88.215
-88.2297
PM
PM
PM
PM
PM
PM
PM
PM
PM
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Joel
Kaufman
Univ. of
Wash.
MESA Air
E°M___
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
E2Ei___
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
MESA Air
E°M___
Univ. of
Wash.
MESA Air
project
Univ. of
Wash.
project
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
8/1/2004-7/31/2014
-------
United States Office of Air Quality Planning and Standards Publication No. EPA 452/S-08-001
Environmental Protection Health and Environmental Impacts Division December 2008
Agency Research Triangle Park, NC
------- |