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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                                           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

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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
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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
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                                                                                      in

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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
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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
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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.

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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.

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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
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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

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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.


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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


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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.
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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

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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.
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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.


December 2008                                                                           20

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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

December 2008                                                                           34

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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


December 2008                                                                           35

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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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           DRAFT 4/02/08 - For Discussion Purposes Only - Do Not Quote or Cite
               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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          DRAFT 4/02/08 - For Discussion Purposes Only - Do Not Quote or Cite
   •   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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          DRAFT 4/02/08 - For Discussion Purposes Only - Do Not Quote or Cite


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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           DRAFT 4/02/08 - For Discussion Purposes Only - Do Not Quote or Cite


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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           DRAFT 4/02/08 - For Discussion Purposes Only - Do Not Quote or Cite
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
                                                                         A-10

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         DRAFT 4/02/08 - For Discussion Purposes Only - Do Not Quote or Cite
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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DRAFT 4/02/08 - For Discussion Purposes Only - Do Not Quote or Cite
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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           DRAFT 4/02/08 - For Discussion Purposes Only - Do Not Quote or Cite
    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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References

Chow, J. C., Watson, J.G., Chen, L-W., Chang, M.C.O., Robinson, N.F., Trimble, D., Kohl, S.,
(2007) The IMPROVE_A Temperature Protocol for Thermal/Optical Carbon Analysis:
Maintaining Consistency with a Long-Term Database, J. Air & Waste Management Association;
57: 1014-1023.

Currie, L. A., B.A. Benner, Jr., J.D. Kessler, D.B. Klinedinst, G.A. Klouda, J.V. Maroif, J.F.
Slater, S.A. Wise,  H. Cachier, R. Cary, J.C. Chow, J. Watson,  E.R.M. Druffel,  C.A.
Masiello, T.I. Eglinton,  A. Pearson, C.M. Reddy, O. Gustafsson,  J.G. Quinn,  P.C. Hartmann,
 J.I. Hedges, K.M. Prentice,  T.W. Kirchstetter,  T. Novakov,  H.  Puxbaum, H. Schmid. (2002).
A critical evaluation of interlaboratory data on total, elemental, and isotopic carbon in the
carbonaceous particle reference material, NIST SRM 1649a. J. Res. Natl. Inst. Stand. TechnoL,
107: 279-298.

Flanagan, James B., Max R. Peterson, R.K.M. Jayanty, and Ed E. Rickman. Analysis of PM2.5
Speciation Network Carbon Blank Data, Research Triangle Institute, Research Triangle Park,
North Carolina 27709 (2003). http://www.rti.org/pubs/OCEC_flanagan_2003.pdf

Frank, N. H., Retained Nitrate, Hydrated Sulfates, and Carbonaceous Mass in Federal Reference
Method Fine Particulate Matter for Six Eastern U.S. Cities, J. Air & Waste Manage. Assoc. 2006,
56,500-511.

Frank, N. H., Carbon Measurements and Adjustments. Presented at HEI/EPA Workshop on Air
Quality Data in Health Effects Research , Newton Marriott Hotel, Newton, MA, November 30-
December 1, 2006 http://www.healtheffects.org/AQDNov06/AQD_Frank.pdf

Kim, E. , P.K. Hopke and Y. Qin (2005). Estimation of Organic  Carbon Blank Values and Error
Structures of the Speciation Trends Network Data for Source Apportionment. J. Air & Waste
Manage. Assoc. 55:1190-1199.

Kim, E. and P.K. Hopke (2006). Characterization of fine particle sources in the Great Smoky
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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           DRAFT 4/02/08 - For Discussion Purposes Only - Do Not Quote or Cite
  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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          DRAFT 4/02/08 - For Discussion Purposes Only - Do Not Quote or Cite


       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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            DRAFT 4/02/08 - For Discussion Purposes Only - Do Not Quote or Cite
                   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".)

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                                                                                         C-10

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           DRAFT 4/02/08 - For Discussion Purposes Only - Do Not Quote or Cite
  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
                                                                                      D-8

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           DRAFT 4/02/08 - For Discussion Purposes Only - Do Not Quote or Cite


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
                                                                                    D-9

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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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           DRAFT 4/02/08 - For Discussion Purposes Only - Do Not Quote or Cite


       •  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
                                                                                   D-ll

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           DRAFT 4/02/08 - For Discussion Purposes Only - Do Not Quote or Cite


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.
                                                                                   D-13

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          DRAFT 4/02/08 - For Discussion Purposes Only - Do Not Quote or Cite


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
                                                                           D-14

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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)
                                                                               D-15

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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
                                                                                   D-16

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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
                                                                                     D-17

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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
                                                                                    D-18

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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.

                                                                                 E-l

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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.

                                                                                  E-2

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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.

                                                                                E-3

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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.

                                                                                E-4

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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.


                                                                                 E-5

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•  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

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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

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       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

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       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

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  Appendix G: Preliminary Survey of Ambient Air Monitoring Sites Currently
      Being Considered in EPA-funded Epidemiology Studies Feb 2008
                                                                    G-l

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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

-------