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 
 

December 2008 

[This page intentionally left blank.] 
 

December 2008 

 EPA 452/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 

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 shortand 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 i

TABLE OF CONTENTS 

LIST OF ACRONYMS .......................................................................................................................iii

AUTHORS AND CONTRIBUTORS ...................................................................................................v

INTRODUCTION................................................................................................................................ 1

Background ....................................................................................................................................................2

Workshop Structure, Objectives, and Outline of Session Summaries......................................................2

Initial Successes and Next Steps ...................................................................................................................3

SESSION I: ELEMENTAL AND ORGANIC CARBON MEASUREMENTS ............................... 5

Background/Objectives.................................................................................................................................5

Session Overview ...........................................................................................................................................5

Major Points Raised by Participants ...........................................................................................................6

Recommendations/Actions for Consideration ............................................................................................9

SESSION II: ACCESSING AMBIENT AIR MONITORING DATA............................................ 11

Background/Objectives...............................................................................................................................11

Session Overview .........................................................................................................................................11

Major Points Raised by Participants .........................................................................................................12

Recommendations/Action Items for Consideration..................................................................................14

SESSION III: AMBIENT AIR MONITORING FOR HEALTH RESEARCH ............................ 17

Background/Objectives...............................................................................................................................17

Session Overview .........................................................................................................................................17

Major Points Raised by Participants .........................................................................................................17

Recommendations........................................................................................................................................19

Action Items for Consideration ..................................................................................................................20

SESSION IV: THORACIC COARSE PARTICLE COMPONENTS AND POTENTIAL HEALTH 

IMPACTS........................................................................................................................................... 22

Background/Objectives...............................................................................................................................22

Session Overview .........................................................................................................................................22

Major Points Raised by Participants .........................................................................................................24

Recommendations/Actions for Consideration ..........................................................................................26

SESSION V: AMBIENT AIR MONITORING REALITIES – EPA/STATE/LOCAL 

PERSPECTIVES – SUMMARY AND RECOMMENDATIONS.................................................... 30

Background/Objectives...............................................................................................................................30

Session Overview .........................................................................................................................................30

Major Points Raised by Participants .........................................................................................................30
 

December 2008 ii

Recommendations/Actions for Consideration ..........................................................................................35

BACKGROUND MATERIALS......................................................................................................... 39

Appendix A: Workshop Agenda and Participant List......................................................................... A-1

Appendix B: Session I: Elemental and Organic Carbon Measurements - Chemical Speciation 

Network (CSN) Carbon Issues .................................................................................................................B-1

Appendix C: Session II: Accessing Ambient Air Monitoring Data - Access to EPA’s Air Quality 

Data for Health Researchers ................................................................................................................... C-1

Appendix C.1 – 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-1

Appendix E: Session V: Ambient Air Monitoring Realities – EPA/State/Local Perspectives - 

Ambient Air Monitoring Network: Network Design and Site Selection Approval .............................E-1

Appendix F: Session V: Ambient Air Monitoring Realities – EPA/State/Local Perspectives - 

Ambient Air Monitoring Method Implementation ................................................................................F-1

Appendix G: Preliminary Survey of Ambient Air Monitoring Sites Currently Being Considered in 

EPA-funded Epidemiology Studies Feb 2008......................................................................................... G-1
 

December 2008 iii

LIST OF ACRONYMS 

The following acronyms have been used for the sake of brevity in this document: 

AMTIC Ambient Monitoring Technology Information Center 

AQS Air Quality System 

ARMs Approved Regional Methods 

AQI Air Quality Index 

BC Black carbon 

BOSC Board of Scientific Counselors 

CAA Clean Air Act 

CAAAC Clean Air Act Advisory Committee 

CASAC Clean Air Scientific Advisory Committee 

CMAQ Community Multiscale Air Quality Model 

CSN Chemical Speciation Network 

DASH Denver Aerosol Sources and Health 

DEARS Detroit Exposure and Aerosol Research Study 

DRI Desert Research Institute 

DRUM Davis Rotating Uniform size-cut Monitor 

EC Elemental carbon 

EPA Environmental Protection Agency 

FDMS Filter Dynamic Measurement System 

FEM Federal Equivalent Method 

FRM Federal Reference Method 

GEO Group on Earth Observations 

GIS Geographic information systems 

HEI Health Effects Institute 

IMPROVE Interagency Monitoring of Protected Visual Environment 

KML Keyhole markup language 

MESA Multi-Ethnic Study of Atherosclerosis 

MYP Multi-year Plan 

NAAQS National ambient air quality standards 

NACAA National Association of Clean Air Agencies 

NARSTO North American Research Strategy for Tropospheric Ozone 

NCER National Center for Environmental Research 

NCore National Core Monitoring Network 

OAQPS Office of Air Quality Planning and Standards 

OC Organic carbon 

ORD Office of Research and Development 

PM Particulate matter 

PM2.5 particles generally less than or equal to 2.5 μm in diameter 

PM10 particles generally less than or equal to 10 micrometers (μm) in diameter 

PM10-2.5 particles generally larger than 2.5 and up to 10 μm in diameter 

PQAO Primary Quality Assurance Organization 

QA Quality assurance 

SEM Scanning electron microscopy 

SLAMS State and local air monitoring stations 
 

December 2008 iv

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 v

AUTHORS AND CONTRIBUTORS 

AUTHORS 

Dr. Dan Costa, National Program Director for Air Research, Office of Research and Development, 

US Environmental Protection Agency, Research Triangle Park, NC 

Mr. Neil Frank, Office of Air Quality Planning and Standards, Office of Air and Radiation, US 

Environmental Protection Agency, Research Triangle Park, NC 

 

Dr. Barbara Glenn, National Center for Environmental Research, Office of Research and 

Development, US Environmental Protection Agency, Washington, DC 

Mr. Tim Hanley, Office of Air Quality Planning and Standards, Office of Air and Radiation, US 

Environmental Protection Agency, Research Triangle Park, NC 

Ms. Beth Hassett-Sipple, Office of Air Quality Planning and Standards, Office of Air and Radiation, 

US Environmental Protection Agency, Research Triangle Park, NC 

Dr. Bryan Hubbell, Office of Air Quality Planning and Standards, Office of Air and Radiation, US 

Environmental Protection Agency, Research Triangle Park, NC 

Mr. Phil Lorang, Office of Air Quality Planning and Standards, Office of Air and Radiation, US 

Environmental Protection Agency, Research Triangle Park, NC 

Mr. Nick Mangus, Office of Air Quality Planning and Standards, Office of Air and Radiation, US 

Environmental Protection Agency, Research Triangle Park, NC 

Dr. Lucas Neas, National Health and Environmental Effects Research Laboratory, Office of 

Research and Development, US Environmental Protection Agency, Chapel Hill, NC 

Dr. Venkatesh Rao, Office of Air Quality Planning and Standards, Office of Air and Radiation, US 

Environmental Protection Agency, Research Triangle Park, NC 

Ms. Joann Rice, Office of Air Quality Planning and Standards, Office of Air and Radiation, US 

Environmental Protection Agency, Research Triangle Park, NC 

Mr. Geoffrey Sunshine, Health Effects Institute, Boston , MA 

Mr. Tim Watkins, National Exposure Research Laboratory, Office of Research and Development, 

US Environmental Protection Agency, Research Triangle Park, NC 

Mr. Lewis Weinstock, Office of Air Quality Planning and Standards, Office of Air and Radiation, 

US Environmental Protection Agency, Research Triangle Park, NC 
 

December 2008 vi

CONTRIBUTORS 

Dr. Sherri Hunt, National Center for Environmental Research, Office of Research and Development, 

US Environmental Protection Agency, Washington, DC 

Ms. Sascha Lodge, National Center for Environmental Research, Office of Research and 

Development, US Environmental Protection Agency, Washington, DC 

Ms. Stacey Katz, National Center for Environmental Research, Office of Research and 

Development, US Environmental Protection Agency, Washington, DC 

Dr. Marc Pitchford, Office of Air Quality Planning and Standards, Office of Air and Radiation, US 

Environmental Protection Agency, Research Triangle Park, NC 

Ms. Gail Robarge. National Center for Environmental Research, Office of Research and 

Development, US Environmental Protection Agency, Washington, DC 

Mr. Rich Scheffe, Office of Air Quality Planning and Standards, Office of Air and Radiation, US 

Environmental Protection Agency, Research Triangle Park, NC 

Ms. Laurel Schultz, Office of Research and Development, US Environmental Protection Agency, 

Research Triangle Park, NC 

Dr. William Wilson, National Center for Environmental Assessment, US Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Darrell Winner, National Center for Environmental Research, Office of Research and 

Development, US Environmental Protection Agency, Washington, DC 
 

December 2008 1

 

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 Particulate Matter at 

http://books.nap.edu/catalog.php?record_id=10957; Board of Scientific Counselors ( BOSC) Report on the PM-Ozone 

Program Review: April 2005 at http://www.epa.gov/osp/bosc/pdf/pm0508rpt.pdf; and Clean Air Act Advisory 

Committee (CAAAC) Report on Air Quality Management in the United States at 

 http://www.nap.edu/catalog/10728.html. 
 

December 2008 2

 

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/local/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.
 

December 2008 3

 

 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 PM2.5 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. 
 

December 2008 4

 

 Science to Achieve Results (STAR) grantees voluntarily analyzed data sets to help 

determine whether changes to the measurement method for organic carbon would affect 

the findings of ongoing epidemiological studies. 

 Based on information they received from air quality experts at the workshop, STAR 

grantees have acknowledged making changes to the designs of their health studies that 

will improve validity of results as a result of better information on spatial variability of 

PM components. 

 OAQPS monitoring experts and ORD epidemiologists and exposure scientists are 

working together to examine the covariance of specific PM components (e.g. metals) 

across various cities to develop a network of sites for refined epidemiological study. 

 ORD’s National Center for Environmental Research has conducted a preliminary survey 

of EPA-funded health researchers to identify specific ambient monitoring sites that are 

being used in current epidemiological studies6

. This information will be useful to State 

and local air agencies as they consider any future changes to their monitoring networks. 

A draft of this document was distributed to the workshop participants for review and 

comment. This final summary document incorporates the comments that were received. In 

addition, EPA has identified additional next steps that will be taken, including: 

 Stratifying the recommendations in this document as to their feasibility and prioritizing 

actions to be taken. These include providing important input into the strategy for the 

revised monitoring network (NCore7

). EPA’s ORD and OAQPS will work together to 

facilitate the incorporation of these recommendations into both short- and long-term 

monitoring strategies and leveraged program plans. This will include EPA staff briefing 

and receiving direction from senior management during planning cycles for both 

monitoring network design and health research planning. 

 Implementing data access improvements and communication tools as soon as possible to 

prevent disruption of data streams, loss of important monitoring sites, and developing a 

clearinghouse for other datasets not readily formatted to the Air Quality System (AQS) 

data system. 

 Requesting periodic consultations on the enhanced monitoring program with the Clean 

Air Scientific Advisory Committee (CASAC) and its Ambient Air Monitoring and 

Methods (AAMM) Subcommittee. 

6

 See Appendix G.

7

 See http://www.epa.gov/ttn/amtic/ncore/index.html for more information on the NCore Multipollutant Monitoring 

Network. 
 

December 2008 5

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 longterm 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 

Colorado-Boulder) 

 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 
 

December 2008 6

 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 7

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 ongoing studies due to the many studies that are national in design and the need to understand 

regional variation in health effects. 

 One presenter indicated that the sub-fraction measurements of OC and EC made in the 

IMPROVE and new CSN protocols can also add valuable information to source attribution 

studies. However, low temperature (i.e., volatile) OC1 and OC2 fractions are most sensitive 

to temperature changes during the analysis and during sample handling (Chow et al, 2007, 

Dillner et al., 2008). 

 The type of measurement error that is most important to a specific epidemiological study will 

depend on the study design, and on the scientific questions it aims to address (e.g., acute 

versus chronic effects). In other words, there is no single factor or set of factors that is the 

critical need for epidemiological researchers. The impact of measurement changes will 
 

December 2008 8

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 timeseries 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 colocated 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 9

 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 ongoing 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 semicontinuous OC/EC data and its potential role in “harmonizing” old vs. new carbon 

measurements. 

 A limited number or participants also supported achieving better spatial resolution by 

implementing a main site and satellite sites for species with large spatial variability, to better 

characterize population exposures. 

Developed by EPA Staff Based on Workshop Discussions:

 Continue UC-Davis (January 2008) “Carbon Summit” process: 

 

o Selected archived samples from IMPROVE and collocated IMPROVE/CSN network 

sites will be analyzed by the new IMPROVE carbon analysis methodology to develop 
 

December 2008 10

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. 
 

December 2008 11

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. 
 

December 2008 12

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 PM2.5 data and will 

 

8

 See http://wiki.esipfed.org/index.php/Air_Quality_Data_Summit#Documents. 
 

December 2008 13

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 nonEPA 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. 
 

December 2008 14

User Friendliness and Documentation 

It was noted that some data users are novices, some are expert, and each needs different 

levels of data access and, therefore, would have different needs. Many participants voiced the 

opinion that epidemiologists tend to be “very opportunistic,” making use of whatever air quality data 

are provided. As such, epidemiologists will utilize available data, but would like more guidance as 

to what uses of the data are valid and/or appropriate (e.g. provision of data on the percent valid 

observations, monitoring objectives, monitor scale, use of flagged data). 

The need to increase user friendliness was discussed, such as with the ability to download 

specific States’ data rather than data for the whole US for some data sources. Within the discussion 

of user friendliness, there was a general consensus that documentation needs to be improved. In 

addition, documentation is perhaps a bit scattered throughout various EPA sites. The point was made 

that "user-friendliness" is dependent on the needs and skill level of the user, so that no standard form 

will be perfect for all users. 

The issue is that it takes more effort to find and interpret documentation of the data than it 

does to obtain the data; and missing descriptive information can be devastating to an analyst. There 

are many attributes of the data that are not well understood by users (metadata is not owned by EPA 

and therefore can be outdated or data can be updated by the owner at any time; EPA data storage 

labels are in flux with regulation and policy changes; etc.). Users having a single site for 

documentation will greatly reduce the likelihood of incorrect interpretation. 

Secondary Data 

The panel discussed whether non-air pollution data would be useful to include in the air 

pollution datasets. Examples were weather, land-use, and census data. Some people thought this 

would be useful. Others thought this was less useful, given that such data are available elsewhere. 

This seemed to be far less of an issue than the versioning. Thus, resources might better be spent on 

the versioning issue than on incorporating secondary data. 

Recommendations/Action Items for Consideration 

The recommendations listed below include items discussed at the workshop as well as 

additional recommendations developed by EPA staff based upon the workshop discussions. 

 Data Versioning 

 Explore options for adding the date (and time) of last modification to all data measurements 

in AQS (and the AQS Data Mart). 

Notes: Data in AQS can change at any time. Generally, EPA limits the ability of data 

submitters to change data for only the last few (3-5) years. However, we do open time windows 

where older data can be changed. There are implications to the submitter for changing data and the 

volume of older data changes is low, but they do happen. Researchers need to know if these data 

changes affect their analyses. Adding a date-of-change to each measurement would allow a data 

user to query the space and time domain of their initial query to see if any data had changed. EPA 

would not keep a record of the change, but new or changed values would be indicated with their date 

of creation/update. It would still be incumbent on the user to maintain the original data set and 

compare changed data to assess possible ramifications. Any change to a measured value or metadata 

(like the monitoring method or uncertainty) would trigger the date stamp to be updated. 
 

December 2008 15

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 valuechain they belong in and successfully connect with those up- and down-stream from them. The 

value-chain has roughly these divisions: base data provision, metadata provision, data integration, 

data processing (aggregation), data visualization, and communication. It is expected that the 

architecture standards board convened as a follow up to the Air Quality Data Summit will adopt 

specific recommendations on web services (machine queries) of data that all participating systems 

should support. If all systems agree to the web services, data integrators and interface builders 

would have a much easier time obtaining data from multiple systems. For example, the HEI Air 
 

December 2008 16

Quality Database could be updated more frequently, and include more information about the 

progeny of the data. 

 Explore opportunities to encourage discussions between health researchers and State/local 

monitoring experts. For example, encourage health researchers to share published results 

with State/local monitoring community, specifically studies that use monitoring data 

collected by the State/local air agencies and encourage participation of a variety of experts in 

regional and national meetings (e.g., health researcher participation in monitoring meetings). 

 User Friendliness and Documentation 

 Explore ways to highlight if a significant change in AQS has occurred. For example, EPA 

could provide an explanation on the AQS website so that regular AQS data users would be 

aware and know where to go to access the data. 

 Use the IMPROVE metadata as a model for developing AQS metadata, including the 

IMPROVE “Data Advisories” noting any changes or issues with the data. 

 Request that supplemental data be presented in the same format as primary data. 
 

December 2008 17

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 copollutants 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 shortterm exposure would develop more precise and valid exposure estimates and would be better able to 

generate hypotheses that address possible mechanisms for the observed health responses to PM. 

Although the panelists primarily focused on issues as they relate to epidemiology studies, the point 

was made that toxicological assessment of ambient particles must be generalizable to human 

exposure and be interpretable in relation to epidemiological results. 

Members of the discussion panel included:

 Barbara Glenn, co-chair, National Center for Environmental Research, EPA 

 Joel Schwartz, co-chair, Harvard School of Public Health 

 John Godleski, Harvard School of Public Health 

 Patrick Kinney, Columbia University 

 Lucas Neas, National Health and Environmental Effects Research Lab, EPA 

 Roger Peng, Johns Hopkins University 

 George Thurston, New York University 

 Jay Turner, Washington University in St. Louis 

Major Points Raised by Participants 

Uncertainties

 Epidemiology studies must deal with several sources of error in estimating personal exposure 

to ambient PM. These include: 

 Instrument measurement error. Is the monitoring technique adequately measuring pollutants 

at the site of the monitor? The observation was made that while exposure measurement error 
 

December 2008 18

at the monitor introduces uncertainty in estimates of ambient concentrations, this error is 

smaller than other measurement errors in epidemiology studies. 

 Spatial variability. How geographically homogenous are the particle concentrations in the 

cities where we are conducting epidemiology studies? Is the concentration of PM species 

measured at the monitor representative of the ambient concentrations experienced by human 

populations living and working in the city? 

 Temporal variability. Monitoring schedules of every third or sixth day results in data gaps 

that severely limit the ability to explore variations in the time lag of response for different 

PM components. Different PM components are believed to target different biologic pathways 

in generating health responses and the flexibility to explore a variety of time lags between 

exposure and outcome is necessary to reveal these effects. In addition, health events relevant 

to days with missing air quality data must be excluded, thus reducing sample size. 

Alternatively, air quality concentrations may be interpolated in some manner which increases 

uncertainty in exposure estimates. Interpolation results in a reduction of daily variability in 

PM concentration data with resulting loss of statistical power, and increases the correlation 

between concentration estimates for PM components in datasets. This inhibits efforts to 

differentiate species-specific toxicity. 

 Other errors in estimation of personal exposure based on ambient measurements. Infiltration 

of PM components indoors, time-location during the day etc. The comment was made that 

use of air conditioning during the summer months has a large effect on health models for 

certain geographic locations.

Time scale

 Research has shown that the health effects of air pollution exposure on one particular day are 

spread out over several subsequent days. Therefore, the effects (e.g., death) observed on one day are 

the result of air pollution that occurred during a period of days on and before the deaths were 

recorded. Epidemiologists need to use pollution concentrations on a defined number of days before 

the date of an effect to study hypotheses regarding the relevant time between exposure and effect. 

These time scales likely are different for different PM components and different health outcomes 

(e.g., heart attack, asthma etc). The relevant time lags for a particular outcome also may vary by 

season and location as well. The study of time lags informs mode of action. 

 In the absence of daily speciation data, PM components with a very acute or immediate effect 

(short-lags) on health would have less measurement error and thus an apparently stronger association 

with health outcomes than PM components with more delayed effects spread over several days 

(long-lags). This differential error could lead to a misattribution of PM effects to specific sources. 

Monitoring on a daily basis is needed in a reasonable number of cities to evaluate this variation. 

Statistical Power to differentiate between components 

 The panel listed several key factors that influence the statistical power of an epidemiology 

study. These include: 

 Number of daily health events – need large population size. 

 Variability in estimated exposure (indicated by variation in pollutant concentration across 

space for long-term cohort studies or change in daily concentration for time-series studies). 

We want to maximize variance and minimize covariance. 
 

December 2008 19

 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 longterm 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: 
 

December 2008 20

 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 21

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, J.; Gauderman, W.J.; Rappaport, E.; Millstein, J.; Bennion, J.; 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 22

 

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., PM10-2.5), even 

though a NAAQS for PM10-2.5 was not adopted. This was done to facilitate consistent research on 

PM10-2.5 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 PM10-2.5 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 PM10 and PM2.5, there exists no national network with the specific 

intent to consistently and accurately measure PM10-2.5.

10 As a result, the amount of PM10-2.5 air 

quality data available and associated analyses are available at fewer locations than PM10 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 PM10

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 PM10 and PM2.5 monitors exist, but there are uncertainties in the consistency of 

these data because the protocol for the PM10 and PM2.5 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 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. 

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 23

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 PM10 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 24

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 PM2.5 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 25

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 PM10 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 PM10, but in attainment for PM2.5

(see Table 1 below) or at least areas with higher PM10 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 26

Why should State/Local/Tribal agencies be interested in thoracic coarse particle monitoring? 

Thoracic coarse particle monitoring in PM10 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). Particulate 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.; and Etyemezian, V. (2000). 

Annual report for the Robbins Particulate Study - October 1997 through September 1998. Prepared for Versar Inc., 

Lombard, IL, by Desert Research Institute, Reno, NV. 
 

December 2008 27

 

 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 hypothesisdriven 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 PM10. 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/display.rfa/rfa_id/450 for more information 
 

December 2008 28

 

 Sources, composition, variability and toxicological characteristics of coarse 

(PM10-2.5) 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 particulate 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 (PM10-2.5) 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 

 Pinal County PMc Characterization Study (Region 9 RARE Project) 

17 add a link to descriptions of these studies 
 

December 2008 29

Table 1. PM10 Non-Attainment Areas with PM2.5 Designations 

(Source: EPA TTN Website) 
 

December 2008 30

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 31

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 semicontinuous 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 32

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. 

PM2.5 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 33

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 PM2.5 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 Parameter 

Code Purpose ~Active Samplers/ Monitors 

PM2.5 LOCAL 

CONDITIONS 88101 

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. 

940 FRM’s 

(~150 operate daily) 

PM2.5 TOTAL 

ATMOSPHERIC 88500 

Valid data from methods 

measuring total PM2.5 aerosols 

in the atmosphere. FDMS is 

the method currently stored 

here 

100 

PM2.5 RAW DATA 88501 

Valid uncorrected data that does 

not meet DQO’s for reporting at 

least the AQI 

300 

ACCEPTABLE PM2.5

AQI & SPECIATION 

MASS 

88502 

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. 

400 PM2.5 

continuous 

mass; 

200 CSN 

PM2.5 VOLATILE 

CHANNEL 88503 

Store important related data 

such as the FDMS reference 

channel. 

5020

In total, 

there are 

~ 600 

operating 

PM2.5

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 

PM2.5 FRM. The positive bias with PM2.5 continuous methods are most likely associated with semivolatile 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 filterbased 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 34

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 35

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 ultrafines, 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 36

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 PM10-2.5 chemical speciation 

Whitepaper (ORD) 

 Analyze data from co-located hourly PM10-2.5 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). 

PM2.5 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 PM2.5 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 HEI). 

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 PM2.5 continuous data on-line, including developing 

statistical adjustments to have their data more closely resemble the PM2.5 FRM data. 
 

December 2008 37

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. 

 

22 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. 
 

December 2008 38

 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? 
 

December 2008 39

BACKGROUND MATERIALS
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 particulate 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/AQDNov06/AQDWorkshop.html for 

more information.

A-1
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 nearand 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 Wayland, 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 

A-2
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. Ed Avol, 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. Dirk Felton, NY Department of Environmental Conservation 

 

25 See http://vista.cira.colostate.edu/improve/Publications/Workshops/Carbon_Jan2008/CarbonMeeting2008.htm for 

more information. 

A-3
DRAFT 4/02/08 – For Discussion Purposes Only – Do Not Quote or Cite 

12:30 – 1:30 LUNCH 

Session I: Elemental and Organic Carbon Measurements (cont.)

1:30 – 2:45 Panel Discussion 

Dr. Ed Avol, 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 of CA-Davis 

Dr. Allen Robinson, Carnegie Mellon Univ. 

Dr. Warren White, University of CA-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 HEI’s 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 

A-4
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. Nick Mangus, 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. Nick Mangus, 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? 

A-5
DRAFT 4/02/08 – For Discussion Purposes Only – Do Not Quote or Cite 

 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? 

A-6
DRAFT 4/02/08 – For Discussion Purposes Only – Do Not Quote or Cite 

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 

mix of PM? 

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? 

A-7
DRAFT 4/02/08 – For Discussion Purposes Only – Do Not Quote or Cite 

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. PM10-2.5) 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 PM10-2.5 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. Philip Fine, SCAQMD Dr. Thomas Peters, University of Iowa 

Dr. Richard Flagan, 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. Dirk Felton, NY DEC 

Mr. Timothy Hanley, EPA/OAQPS 

1:15 – 2:30 Panel Discussion 

Dr. Philip Fine, SCAQMD 

Mr. Michael Gilroy, Puget Sound CAA 

Mr. Richard Payton, 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
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 First Name Affiliation 

Arnold Jeff EPA National Center for Environmental Assessment 

Avol Ed University of Southern California 

Baldauf Rich EPA NRMRL 

Baxter Lisa EPA National Exposure Research Laboratory 

Bell Michelle Yale University 

Brook Jeffrey Environment Canada 

Bucky Barbra EPA National Center for Environmental Assessment 

Chow Judy Desert Research Institute 

Costa Dan EPA Office of Research & Development 

Devlin Robert 

EPA National Health & Environmental Effects Research 

Laboratory 

Diaz-Sanchez David 

EPA National Health & Environmental Effects Research 

Laboratory 

Dubowsky Adar Sara University of Washington 

Felton Dirk New York State Department of Environmental Conservation 

Fine Phillip South Coast Air Quality Management District 

Flagan Richard California Institute of Technology 

Foley Kristen EPA National Exposure Research Laboratory 

Frank Neil EPA Office of Air Quality Planning & Standards 

Garbe Paul Centers for Disease Control & Prevention 

Garcia Val EPA National Exposure Research Laboratory 

Gilliland Alice EPA National Exposure Research Laboratory 

Gilroy Mike Puget Sound Clean Air Agency 

Glenn Barbara EPA National Center for Environmental Research 

Godleski John Harvard School of Public Health 

Gordon Terry New York University 

Hall Eric S EPA National Exposure Research Laboratory 

Hanley Tim EPA Office of Air Quality Planning & Standards 

Hannigan Michael University of Colorado at Boulder 

Hansen Craig EPA National Center for Environmental Assessment 

Hassett-Sipple Beth EPA Office of Air Quality Planning & Standards 

Holland David EPA National Exposure Research Laboratory 

Hopke Phil Clarkson University 

Hubbell Bryan EPA Office of Air Quality Planning & Standards 

Ito Kaz New York University 

Jenkins Scott EPA Office of Air Quality Planning & Standards 

Katz Stacey EPA National Center for Environmental Research 

Kim Jee-Young EPA National Center for Environmental Assessment 

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Last Name First Name Affiliation 

Kinney Patrick Columbia University 

Kirrane Ellen EPA National Center for Environmental Assessment 

Kleeman Mike University of California-Davis 

Kryak David EPA National Exposure Research Laboratory 

Lamason Bill EPA Office of Air Quality Planning & Standards 

Langstaff John EPA Office of Air Quality Planning & Standards 

Larson Tim University of Washington 

Lippman Morton New York University 

Long Tom EPA National Center for Environmental Assessment 

Lorang Phil EPA Office of Air Quality Planning & Standards 

Luben Tom EPA National Center for Environmental Assessment 

Mangus Nick EPA Office of Air Quality Planning & Standards 

Martin Karen EPA Office of Air Quality Planning & Standards 

Mikel Dennis EPA Office of Air Quality Planning & Standards 

Mintz David EPA Office of Air Quality Planning & Standards 

Mukerjee Shailbal EPA National Exposure Research Laboratory 

Neas Lucas 

EPA National Health & Environmental Effects Research 

Laboratory 

Ozkaynak Haluk EPA National Exposure Research Laboratory 

Payton Richard EPA Region 8 

Peltier Richard New York University 

Peng Roger Johns Hopkins University 

Peters Thomas University of Iowa 

Pierce Tom EPA National Center for Environmental Assessment 

Pinto Joe EPA National Center for Environmental Assessment 

Poirot Rich Vermont Department of Environmental Conservation 

Pun Betty Atmospheric and Environmental Research, Inc. 

Rao Venkatesh EPA Office of Air Quality Planning & Standards 

Reynolds Scott 

South Carolina Department of Health and Environmental 

Control 

Rice Joann EPA Office of Air Quality Planning & Standards 

Richmond Harvey EPA Office of Air Quality Planning & Standards 

Robarge Gail EPA National Center for Environmental Research 

Robinson Allen Carnegie Mellon University 

Ross Zev ZevRoss Spatial Analysis 

Sacks Jason EPA National Center for Environmental Assessment 

Schauer Jamie University of Wisconsin 

Scheffe Rich EPA Office of Air Quality Planning & Standards 

Schultz Laurel EPA Office of Research & Development 

Schwartz Joel Harvard University 

Sheldon Linda EPA National Exposure Research Laboratory 

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Last Name First Name Affiliation 

Stanek Lindsay EPA National Center for Environmental Assessment 

Stevenson Eric Bay Area Air Quality Management District 

Stewart Michael EPA Office of Air Quality Planning & Standards 

Stone Susan EPA Office of Air Quality Planning & Standards 

Sunshine Geoffrey Health Effects Institute 

Thurston George New York University 

Tikvart Joe EPA Office of Air Quality Planning & Standards 

Turner Jay Washington University in St. Louis 

Turpin Barbara Rutgers University 

Vandenberg John EPA National Center for Environmental Assessment 

Vanderpool Robert EPA National Exposure Research Laboratory 

Vette Alan EPA National Exposure Research Laboratory 

Watkins Tim EPA National Exposure Research Laboratory 

Watson John Desert Research Institute 

Wayland 

Richard 

(Chet) EPA Office of Air Quality Planning & Standards 

Wegman Lydia EPA Office of Air Quality Planning & Standards 

Weinstock Lewis EPA Office of Air Quality Planning & Standards 

White Warren University of California-Davis 

Williams Ron EPA National Exposure Research Laboratory 

Willis Robert EPA National Exposure Research Laboratory 

Wilson William EPA National Center for Environmental Assessment 

Winner Darrell EPA National Center for Environmental Research 

Wyzga Ronald Electric Power Research Institute 

ZimmerDauphinee Susan Georgia Department of Natural Resources 
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Appendix B: Session I: Elemental and Organic Carbon Measurements - 

Chemical Speciation Network (CSN) Carbon Issues 

Questions on this draft white paper should be directed to Neil Frank, EPA/OAQPS, 

frank.neil@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 

NPS 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, EC3). 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 subfraction) 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 transitioned 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, Kim). 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.html). 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 oldprotocol-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 PM2.5 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.pdf for 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 doublyanalyzed 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 IMPROVE_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 AQS Site ID Urban/

Rural 

CSN Sampler Start 

Date 

End Date 

Atlanta 

(Decatur), GA 

13-089-0002 Urban Andersen RAAS 

until 1/2006 then 

MetOne SASS 

4/2004 Ongoing * 

Birmingham, 

AL 

01-073-0023 Urban MetOne SASS 

(URG3000N for 

carbon May 2007) 

4/2004 Ongoing 

Detroit (Allen 

Park), MI 

26-163-0001 Urban MetOne SASS 11/2003 Ongoing * 

Fresno, CA 06-019-0008 Urban MetOne SASS 9/2004 Ongoing 

New York 

(IS52), NY 

36-005-0110 Urban R&P2300 until 

1/2006 then 

MetOne SASS 

(URG3000N for 

carbon May 2007) 

8/2004 Ongoing 

Pittsburgh, PA 42-003-0008 Urban MetOne SASS 4/2004 Ongoing * 

Houston, TX 48-201-1039 Urban URG MASS 5/2004 9/2005 

Chicago, IL 17-01-0076 Urban URG MASS 11/2003 9/2005 

Rubidoux 

(Riverside), CA 

06-065-8001 Urban MetOne SASS 9/2004 9/2005 

Phoenix, AZ 04-013-9997 Urban MetOne SASS 10/2001 12/2003 

Tonto NP, AZ 04-007-0010 Rural MetOne SASS 10/2001 12/2003 

Seattle, WA 53-033-0080 Urban URG MASS 10/2001 12/2003 

Mt. Ranier, WA 53-053-0014 Rural URG MASS 10/2001 12/2002 

Washington DC 11-001-0042 Urban Andersen RAAS 10/2001 12/2003 

Dolly Sods, WV 54-093-9000 Rural Andersen RAAS 10/2001 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 Jürgen 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_OC_Artifacts_6-29-

04_Conf_Call.ppt
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Appendix C: Session II: Accessing Ambient Air Monitoring Data - Access to 

EPA’s Air Quality Data for Health Researchers 

Questions on this draft white paper should be directed to Nick Mangus, EPA/OAQPS, 

mangus.nick@epa.gov, (919) 541-5549. 

Introduction 

A common refrain from policymakers, analysts, and scientists is that obtaining the air 

quality data which they need is a challenge. This paper outlines the current collection and 

dissemination framework for air quality data and poses “charge questions” to the health-research/ 

epidemiology community. The answers to these questions will help us at the EPA improve our 

offerings. 

To frame the charge questions, this document describes a relatively new EPA system, the 

AQS Data Mart, and contrasts it with the HEI Air Quality Database, which was put in place to 

provide access to PM components and other data for health researchers. Finally, the charge 

questions are presented. 

Background 

The collection, storage, and dissemination of air quality data is a complex process 

achieved by a series of separate groups of hardware, software, and people. As technology has 

advanced and the number of distinct sets of user groups (those with different data or analytical 

needs) have proliferated, the problem for any individual finding precisely what they need has 

only gotten more complex. Adding to this complexity are intermediate “value-added” providers 

who may integrate, visualize, or otherwise post-process data from various sources. Thus, users 

can invest in their own data gathering and processing or they can rely on an array of intermediary 

providers. We also have data from special studies. The quality is (probably) high, but the data 

may not be readily available to others. So, EPA will always be the provider of certain base data, 

but we may not have it in the desired form, integrated with other desirable data (emissions or 

population), or presented in the desired manner. There will always be the possibility for a valueadded 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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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 transitioning 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 transitioned 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 = AQSdatamart1 (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 Description 

Recommended, returns any single raw, daily, or annual variable with metadata and is very 

efficient Values 

Monitor Returns descriptions of the monitoring site and equipment 

Annual 

Summary Returns all annual summary aggregate statistics for the monitors selected 

Raw Data Returns raw data in the AQS transaction format - recommended only for AQS users 

Sites by 

Threshold 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 

(SO2, O3, 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 HEI 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 HEI Air Quality Database AQS Data Mart 

Site browser Yes, with maps to help No 

Site finder Yes, with multiple-variable filter Yes, via a single-variable “sites by 

threshold” query 

Query from saved list Yes. Station lists may be saved and 

re-used 

No. Query based on geography and 

parameter or single site 

Query by any geography Yes Yes 

Air quality data for PM2.5, O3, 

CO, NOx, NO2, & SO2

Yes Yes 

Air quality data for all other 

parameters No Yes 

AQS met data Yes Yes 

Integrated non-EPA met data Yes No 

Emissions data Yes No 

Census data Yes No 

On-line help Yes No 

Off-line help Yes Yes 

File format CSV XML (CSV planned) 

Data returned in one file No Yes 

Update frequency (versions) Quarterly Daily 

Build your own query Yes 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 HEI’s, 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 ultrafine particle projects. If these data 

should be included, how should it be done? For example, to be loaded into the AQS Data 

Mart the data must match the monitor paradigm (no remote sensing or mobile monitors), 

it must meet format and quality requirements, and it must have associated descriptive 

data (e.g., method used, sampling schedule). Getting new data to match EPA’s data 

standards are often labor-intensive activities – are they worth it? Would EPA have to 

correct and load these data into the AQS database (or a “research” copy of the AQS 

database)? Would EPA be able to commit the resources to doing this? As an alternative, 

EPA can provide information to managers of new studies about the data format and 

content standards we have so that the data can be collected in a way that could be more 

easily shared and compared with AQS data or other new data collected using the 

standards. If this special study data remains outside of EPA systems, is there a role for a 

clearinghouse? The clearinghouse could keep an up-to-date list of monitoring efforts, 

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databases, contents, and appropriate uses. Issues to be considered include: how resource 

intensive would this effort be and who would develop and maintain this clearinghouse? 

Other Charge Questions 

The remaining charge questions are more straight-forward than the standout charge 

questions. They are related to how individuals gather and use data rather than community-wide 

concerns. 

4. What are the key data that you need? Is any of this currently collected but not 

available? 

5. Is there a particular way that you need data organized, grouped, or formatted? 

6. What data elements other than measurements do you need? 

7. What is the typical domain of the data you need (time, space, and parameter

selections; for example, 3 years, several cities, and 4 parameters; or 1 year, national, 

44 parameters)? 

8. Are there “profiling” reports – descriptions of which sites collect which data, how 

complete the data are, etc. – that you need? 

9. Would you rather query a database or have a large list of files that you can select 

from to download (like 

http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm but with more 

geographic resolution)? 

10. What would your ideal query builder/interface look like? 

11. Are there pieces of data that we provide or questions that we ask that confuse you?

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Appendix C.1 – Other Data Access Mechanisms

EPA has many places to access air quality data. Each of these websites or applications was 

designed for a specific target audience, for example, the general public concerned with acute health 

issues, the general public concerned with long-term air quality where they live, the general public 

interested in air quality comparisons between multiple locations (for living, vacationing, etc.), data 

analysts concerned with regulatory compliances, data analysts contributing to policy decisions, and health 

researchers. We consider a researcher to be someone who is looking to download raw data; either in large 

volume or in small, discrete sets that are difficult to tease out of large published datasets. Each of these 

websites or applications presents a unique front end for queries, charts, or maps that are geared toward 

their target audience. 

EPA is developing a “portal” to list all of the sources of air quality (and emissions) data that are 

available and link directly to their access pages. This portal is at the following web address: 

http://www.epa.gov/oar/airpolldata.html. Below is a table comparing key information about each of the 

available EPA-maintained systems for air quality data (including AirNow and CASTNET which contain 

data not in AQS). The systems are described at the link above. (Key: a filled circle means “yes” and an 

empty circle means “some”.) 

System 

Level of 

Detail Time Substances Outputs Audience 

Raw (samples & NAAQS) Daily sums & avgs Annual sums & avgs QA Time range of most detailed data Ozone + PM2.5 Other Criteria Toxics PM2.5 Adj. Spec PM2.5 Unadj. Spec Other Canned queries: tabular Canned queries: maps Ad hoc Public SLT Analysts Researchers 

AQS ● ● ● ● 1994 – Present ● ● ● ● ● ● ● ●

AirNow (Tech) ● ● 1999 – Present ● ○ ● ● ●

AirData ● 1996 - 2006 ● ● ● ● ●

AirExplorer ● ● 1996 – 2006 ● ● ○ ● ● ● ● ●

AirCompare ● ● 2000 – Present ● ● ● ● ●

AQS Data Page ● 1994 – 2006 ● ● ● ● ●

NATA (modeled) ● 1996 & 1999 ● ● ● ●

Air Trends ● 1990 – 2005 ● ● ● ● ●

AQS Data Mart ● ● ● 1980 – Present ● ● ● ● ● ● ●

CASTNET ● ● 1987 - 2005 ○ ● ● ●
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Appendix D: Session III: Ambient Air Monitoring for Health Research - Air 

Quality Sampling: Benefits and Costs of Daily Health Targeted Monitors for 

Fine Particle Components 

Questions on this draft white paper should be directed to Dr. Barbara Glenn, EPA/NCER, 

glenn.barbara@epa.gov; (202) 343-9721. 

INTRODUCTION 

EPA’s air quality datasets are generally recognized and valued primarily for their use in 

ascertaining compliance with the National Ambient Air Quality Standards (NAAQS), developing 

State Implementation Plans for the improvement in air quality, and providing timely air quality 

data to the public. EPA’s air quality datasets also are essential to extramural and intramural 

health research addressing scientific uncertainty related to the current NAAQS and to the 

assessment of the possible health benefits of any new air quality standard. These health-related 

uses of EPA’s air quality datasets are an important consideration in the design and conduct of the 

national air quality monitoring network. As specialized monitoring networks have begun 

providing information on the composition of particulate matter, epidemiologic researchers are 

striving to address a major research priority defined by the National Academy of Sciences 

National Research Council (NRC)—assessing the health effects of PM components and sources. 

The NRC reports on “Research Priorities for Airborne Particulate Matter” repeatedly 

emphasize the importance of research to assess the relationships between particle composition 

and health responses. According to the fourth report, “Progress on assessment of hazardous PM 

components is central to the national research portfolio and to any refinement of the current 

mass-based NAAQS for PM....A better understanding of characteristics that modulate toxicity 

could lead to targeted control strategies specifically addressing those sources having the most 

significant adverse effects on public health.” (NRC 2004) 

On November 30, 2006, the Health Effects Institute (HEI) and EPA, in conjunction with 

the annual EPA PM Centers meeting, convened a meeting of the research and air quality 

management communities to discuss the use of EPA’s air quality datasets for health research on 

particulate matter (PM). Participants raised several issues that complicate the design and 

interpretation of epidemiologic research on PM2.5 mass, components, and sources. Participants 

emphasized that the lack of daily concentration measurements for fine particle mass and 

components in key locations was severely affecting their ability to design and conduct 

epidemiologic studies that would address issues of scientific uncertainties highlighted in air 

quality standard setting at Federal and state levels. This paper summarizes these issues, 

proposing a range of options to address the need for daily data based on the November 2006 

meeting and subsequent information exchange with EPA grantees, state/local air quality 

monitoring representatives, HEI and EPA staff. These challenges would not exist if resources to 

collect daily, speciated monitoring data were readily available. The pressing needs for these data 

are increasing at a time when resources are decreasing and monitoring costs are generally 

increasing. This draft white paper provides background information to facilitate a broad 

discussion on the benefits of obtaining daily fine particle speciation measurements and to 

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encourage health researchers and air quality experts to work together to creatively identify 

solutions that address the need for daily data while understanding the resource constraints and 

competing needs for monitoring data. 

The goal of this discussion paper is to draw attention to the importance of specific 

monitoring data needs in planning health research studies, specifically related to evaluating 

potential public health impacts of fine particles. Epidemiologic studies relating daily variation in 

ambient air pollutant concentrations with disease-specific mortality or morbidity have been very 

important for providing the scientific basis for recent standard-setting for PM. Some of the key 

policy relevant issues considered in evaluating the PM NAAQS include: 

 What are the potential public health impacts associated with exposures to specific size 

fractions, chemical components, sources and/or environments (e.g., urban and non-urban 

areas) of PM? 

 What is the relationship between various health endpoints and different lag periods (e.g., 

less than one day, single day, and multi-day distributed lags)? 

 How does spatial and/or temporal heterogeneity of PM exposures vary with different 

size fractions and/or components? 

Providing daily ambient air monitoring data for fine particle components from several 

cities to health researchers would reduce exposure misclassification, allow the use of all health 

events in statistical analyses, and thereby increase the precision of risk estimates. In addition, the 

availability of these data would significantly decrease the length of time necessary to produce 

study results. 

BACKGROUND 

Epidemiologic studies of the adverse human health effects of short-term exposures to air 

pollutants have generally relied upon air quality monitoring systems established to ensure 

compliance with ambient air quality standards. These epidemiologic studies contributed to 

decisions in 1987 to change the indicator for the PM NAAQS from total suspended particles 

(TSP) to PM10 and to decisions in 1997 to add new standards to consider fine and coarse 

fractions of PM10 separately, using PM2.5 as the indicator for fine particles and using PM10 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 dataavailability 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 PM10 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 PM10 (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 1-in-6 day sampling schedules. The 8-year mortality dataset was 

necessarily restricted to only those days where PM10 data were available between 1987 and 1994 

in each county. County-specific mean PM10 concentrations were calculated for each day with 

PM10 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 PM10 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 of ten cities with PM10 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 μg/m3 increase in daily PM10 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 PM10. In 

addition, consistent with previous reports, the NMMAPS results for morbidity in 14 cities 

demonstrated that use of PM10 concentrations on a single day (a one day lag) resulted in an 

underestimation of the cumulative PM10 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 1-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 μg/m3

increase in PM2.5. The hypothesized increase in mortality rate was derived from city-specific 

relative risks reported in the PM literature. With 1-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 1⁄3 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 1⁄3 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-tocity 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-today 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 colinearity 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 1-in-6 day ambient concentration data. In this 

study, a significant association was reported between daily change in PM10 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 1-in-6 day 

monitoring frequency, the effect estimates for the PM10-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 1-in-3 or 1-in-6 day monitors, the relationship with the health measurements 

is disordered; the lagged effects of air quality on a single day must be compared with mortality 

on different days. For example, today’s air quality is compared to today’s mortality at lag 0, 

with tomorrow’s mortality at lag 1, and with the day after tomorrow’s mortality at lag 2. 

Distributed lag models using daily monitors are advantageous because a specific lag structure for 

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modeling the association of a pollutant with health does not have to be selected in advance. This 

modeling flexibility will be especially important for the study of PM components, which may 

have differing lags between exposure and health outcome. 

SUMMARY 

The purpose of the preceding discussion has been to highlight the importance of timeseries 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 HEI/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 1-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 

PM2.5 and some components that are key source indicators. XRF analysis of archived daily PM2.5

mass (Teflon) filters collected from an adequate number of locations of daily FRM monitors 

(primarily ones co-located with some of the 1-in-3 day CSN monitors) would provide critical 

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information about daily concentrations of metals and elements. There are alternative methods to 

measure particle mass, EC, and XRF-elements that may address the data needs for epidemiology 

studies and be cost-effective. For example, Joel Schwartz and George Thurston recommended 

using reflectance, a measure of black carbon, at the CSN sites to obtain estimates of daily EC 

(where daily samples for PM2.5 mass are available). Reflectance or other optical analyses of the 

same filters may provide an indicator of EC. It must be noted that the existence of all historical, 

Teflon filters of interest remains to be determined and gaining access to those filters that have 

been archived will require a collaborative effort with State and local agencies. 

2. Evaluate continuous measurement data to obtain daily organic carbon (OC)/elemental 

carbon (EC) at TRENDS sites. 

Continuous (hourly) data for sulfate, nitrate, EC, and OC are available in some cities 

including, Chicago, IL, Indianapolis, IN, Davenport, IA, Bar Harbor, ME, Cedar Rapids, IA, 

Raleigh, NC, New York City, Seven Oaks, SC, Greenville, SC, Rockwell, NC, Seattle, WA, and 

Detroit, MI. However, all components are not measured at all cities. Daily measures for sulfate, 

nitrate, EC, and OC could be obtained over multiple years for Chicago (2002-2007), Bar Harbor 

(2004-2007 for sulfate, 2004 –? for OC, EC, & TC), New York City (2001 – 2007 for sulfate & 

nitrate, 2005 - 2007 for OC & EC), and Raleigh (2003 – 2006 for OC, 2003 - ? for sulfate, 2003 

– 2007 for nitrate, and 2003 – 2006 for total carbon). These data could be used to construct a 

data set containing daily concentrations. Of these sites, STN monitors are located in the vicinity 

of the continuous monitors at Chicago, New York City, and Raleigh. OAQPS notes that 

continuous speciation monitors have their own measurement uncertainties, which may include 

systematic biases that are not well characterized; data from them cannot simply be merged with 

CSN data to fill in missing days. However, the continuous data could be used on a site-by-site 

basis if a relationship between the continuous analyzer and the filter-based monitor was 

established. 

B: Expand current monitoring schedules at selected locations in order to conduct daily 

speciation measures in selected metropolitan areas. 

1. Locations 

Population size and the number of health events that occur each day are location 

attributes that contribute the greatest amount to the power of a time-series study to detect an 

association with exposure to an air pollutant if one exists. The top 22 PMSAs or MSAs with the 

highest weight were selected from Table 1 and are listed in Table 2 along with any CSN 

monitors currently operating in that location. It should be noted that there are three PMSAs 

(highlighted in red) in this group where there is no CSN monitor located. All of the other 

locations, except for Los Angeles, have an STN monitoring site. In addition to population size 

and mortality and the opportunity to take advantage of an existing CSN monitor, there are a 

number of factors that are important to consider for site selection for daily speciation monitoring. 

Important information might include: 

 What are the major sources in an area? – Are components of interest present in 

measureable concentrations? 

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 Regional representation of study locations under consideration 

 Area characteristics – topography, which influences spatial variability 

 Existing data collection at a location - 

 Are data available on a daily timescale for components of interest retrospectively? 

 Are multiple speciation monitors operating in the location? 

 Are special studies (government or grant-supported) being conducted in the location? 

Participants at the April 2008 workshop will be asked to provide feedback about the 

importance of daily monitoring for fine particle components for advancing our understanding of 

the impacts of air pollutant exposures on public health and criteria to consider for prioritizing 

locations for daily PM2.5 speciation monitoring. For discussion purposes, three columns have 

been added to Table 2 indicating significant area characteristics, notes on predominant PM 

components, and any special studies known to be conducted in the location that may contribute 

information on temporal or spatial variability. 

2. Components 

For what PM species would it be beneficial to have daily ambient measurements? For 

discussion purposes, Ito (HEI/EPA workshop, 2006) suggested the following components: OC, 

EC, nitrate, sulfate, Se, As, Si, Fe, Mn, Cr, Zn, Pb, V, Ni based upon information from 

toxicology and source apportionment studies. Some of these components may have more 

homogenous distributions in certain regions but others are likely to have a high degree of spatial 

variability. CSN and IMPROVE currently analyze for these components and EPA plans 

are to continue to do so. 

3. Costs 

The estimated annual cost for shipping and lab analysis to add daily PM2.5 mass and 

speciation monitoring at one CSN site that is currently operating on a 1-in-3 day sampling 

schedule would be $100,000 based on current EPA contracts. The State/local monitoring agency 

would incur additional labor and equipment costs to operate the monitors as well. 

OTHER MONITORING ISSUES 

Two additional issues should be mentioned in this discussion of the use of air quality 

monitoring data in time-series studies. 

A: Spatial Variability: Set up additional monitoring sites within certain cities to 

increase understanding of spatial variability of specific components. 

Several components of research interest will be associated with a high degree of spatial 

variability across a location. A small number of monitors (4 – 6) distributed to capture 

concentrations throughout an area could give enough information to conclude whether or not a 

specified component has a uniform distribution in that area. If the distribution appeared uniform 

for a certain component, multi-site time-series analyses could be conducted using data from one 

or more centrally located monitors per location. If not, a more detailed exposure analysis would 

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be necessary in certain locations to obtain a finer spatial resolution and develop exposure 

models. Such a detailed, nonroutine study could potentially involve a research grant. Based on 

current EPA contracts, the estimated annual cost to establish a CSN-like site operating on a daily 

schedule in a new location but probably where other monitors are located is $150,000 for 

shipping and lab analysis. 

B. Alternative Measurement Methodologies 

There are alternative methods to measure particle mass, EC, and XRF-elements that may 

address the data needs for epidemiology studies and be cost-effective. For example, Joel 

Schwartz and George Thurston recommended using reflectance, a measure of black carbon, at 

the CSN sites to obtain estimates of daily EC. In addition, the use of a rotating drum sampler 

which measures various size classes of PM mass and PM components with a finer time 

resolution (six hours) could be considered. At a meeting with State/local monitoring managers, 

reservations were expressed about whether this sampler has demonstrated adequate 

repeatabililty. 

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 colinearity 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 particulate mass and daily mortality in 

eight Canadian cities. In: Revised Analyses of Time-Series Studies of Air Pollution and Health. 

Special Report. Health Effects Institute, Boston, MA. 

Dominici F, McDermott A, Daniels M, Zeger SL, Samet JM. 2003. Mortality among residents of 

90 cities. In: Revised Analyses of Time-Series Studies of Air Pollution and Health. Special 

Report. Health Effects Institute, Boston, MA. 

Ito K, Kinney PL, and Thurston GD. 1995. Variations in PM-10 concentrations within two 

metropolitan areas and their implications for health effects analyses. Inhalation Toxicol 7:735-

745. 

National Research Council (NRC). 2004. Research Priorities for Airbourne Particulate Matter 

IV: Continuing Research Progress. The National Academies Press, Washington D.C. 

Samet JM, Zeger SL, Dominici F, Curriero F, Coursac I, Dockery DW, Schwartz J, Zanobetti A. 

2000. The National Morbidity, Mortality, and Air Pollution Study, Part II: Morbidity and 

Mortality from Air Pollution in the United States. Research Report 94. Health Effects Institute, 

Cambridge MA. 

Schwartz J, Zanobetti A, Bateson T. 2003. Morbidity and Mortality Among Elderly Residents of 

Cities with Daily PM Measurements. In: Revised Analyses of Time-Series Studies of Air 

Pollution and Health. Special Report. Health Effects Institute, Boston, MA. 

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Figure 1. Implication of every-6th-day and every-3rd-day data: Statistical power for timeseries studies. 

Slide provided by Dr. Kazuhito Ito, presented at HEI/EPA Workshop on Air Quality Data, 

Newton, MA, November 2006. Adapted slightly for this discussion paper. 

number of monitoring days

A 

B

C 

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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 STN? Area charact

1 9,314,235 16.0 New York, NY PMSA; New York--Northern New Jersey--Long Island, NY--NJ--CT--PA CMSA Large urban incr

plus NE backgro

IS 52 New York NY 0110 Met One SASS Teflon Bronx STN 

DIVISION STREET New York NY 0134 Met One SASS Teflon New York 

QUEENS COLLEGE 2 New York NY 0124 R&P MDL2300 PM2.5 

SEQ SPEC 

Queens

2 9,519,338 16.1 Los Angeles--Long Beach, CA PMSA; Los Angeles--Riverside--Orange County, CA CMSA Complex coastal 

mountain topograextreme traffic, oand sun 

NULL Los Angeles CA 1103 Met One SASS Teflon Los Angeles 

3 8,272,768 15.1 Chicago, IL PMSA; Chicago--Gary--Kenosha, IL--IN--WI CMSA Industrial center 

influences, windy 

SPRINGFIELD PUMP STATION Chicago IL 0057 Met One SASS Teflon Cook 

COM ED MAINTENANCE BLDG 

Chicago IL 0076 Met One SASS Teflon Cook STN 

NORTHBROOK WATER PLANT Northbrook IL 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 Industry plus NE 

background 

CAMDEN LAB Camden NJ 0003 Met One SASS Teflon Camden 

AMS Laboratory Philadelphia PA 0004 Met One SASS Teflon Philadelphia STN ON AMTRAK RIGHT OF WAY - NEAR AIRPORT HI SPEED LINE (ELECTRIFIED) 

Philadelphia PA 0136 Met One SASS Teflon Philadelphia 

A420450002LAT/LON POINT IS OF CORNER OF TRAILER 

Chester PA 0002 Met One SASS Teflon Delaware 

34 Weights (Wgt) are proportional to expected inverse-variance regression weights: sqrt((population * 0.01) / 365)

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# 2000 Pop Wgt MSA Local Site Name City Name State 

Site 

ID Sample Collection County Name STN? Area charact

CHESTER COUNTY TRANSPORT SITE INTO PHILADELPHIA 

Not in a city PA 0100 Met One SASS Teflon Chester 

CORNER OF MLK BLVD AND JUSTISON ST, NO TRAFFIC DATA AVAILABLE 

Wilmington DE 2004 Met One SASS Teflon New Castle 

5 4,923,153 11.6 Washington, DC--MD--VA--WV PMSA; Washington--Baltimore, DC--MD--VA--WV CMSA NE background p

traffic 

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 Industry and high 

NULL Allen Park MI 0001 Met One SASS Teflon Wayne STN 

PROPERTY OWNED BY DEARBORN PUBLIC SCHOOLS 

Dearborn MI 0033 Met One SASS Teflon Wayne 

DEAN ROAD DEAD-ENDS AT SITE, 200 FT WEST 

Luna Pier MI 0005 Met One SASS Teflon Monroe 

7 4,177,646 10.7 Houston, TX PMSA; Houston--Galveston--Brazoria, TX CMSA Extreme chemica

industry and ozoncoastal sunny 

SOUTH OF DETERMINED & ALDINE MAIL RD INTERSECTION 

Not in a city TX 0024 R & P Model 2025 PM2.5 Sequential Air 

Sampler w/VSCC 

Harris 

NW OF W. LAMBUTH & DURANT INTERSECTION 

Deer Park TX 1039 URG MASS400 Teflon 

WINS 

Harris STN 

8 4,112,198 10.6 Atlanta, GA MSA Extreme biogenic

traffic, sunny 

2390-B WILDCAT ROAD, DECATUR, GA 

Decatur GA 0002 Met One SASS Teflon DeKalb STN 

9 3,519,176 9.8 Dallas, TX PMSA; Dallas--Fort Worth, TX CMSA Traffic and cattle

WEST OF S. AKARD & CANTON STREETS INTERSECTION 

Dallas TX 0050 R & P Model 2025 PM2.5 Sequential Air 

Sampler w/VSCC 

Dallas 

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# 2000 Pop Wgt MSA Local Site Name City Name State 

Site 

ID Sample Collection County Name STN? Area charact

DALLAS HINTON Dallas TX 0069 URG MASS400 Teflon 

WINS 

Dallas STN 

THIS SITE WILL MONITOR THE SAME AREA AS SITE 1390015 

Midlothian TX 0016 R & P Model 2025 PM2.5 Sequential Air 

Sampler w/VSCC 

Ellis 

10 3,406,829 9.7 Boston, MA--NH PMSA; Boston--Worcester--Lawrence, MA--NH--ME--CT CMSA NE background p

traffic 

DUDLEY SQUARE ROXBURY Boston MA 0042 Met One SASS Teflon Suffolk STN 

11 3,254,821 9.4 Riverside--San Bernardino, CA PMSA; Los Angeles--Riverside--Orange County, CA CMSA Complex coastal 

mountain topograextreme traffic, oand sun 

NULL Rubidoux 

(West Riverside) 

CA 8001 Met One SASS Teflon Riverside STN 

12 3,251,876 9.4 Phoenix--Mesa, AZ MSA Extreme traffic, o

and sun 

PHOENIX SUPERSITE Phoenix AZ 9997 Met One SASS Teflon Maricopa STN 

13 2,968,806 9.0 Minneapolis--St. Paul, MN--WI MSA Continental urba

ANDERSON SCHOOL - PHILLIPS NEIGHBORHOOD 

Minneapolis MN 0963 Met One SASS Teflon Hennepin STN 

14 2,846,289 8.8 Orange County, CA PMSA; Los Angeles--Riverside--Orange County, CA CMSA

 No speciation monitors? 

Complex coastal mountain topogratraffic, ozone. an

15 2,813,833 8.8 San Diego, CA MSA Complex coastal 

mountain topogratraffic, ozone. an

NULL El Cajon CA 0003 Met One SASS Teflon San Diego STN NULL 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? 

Large urban incrplus NE backgro

17 2,603,607 8.4 St. Louis, MO--IL MSA Industry and traf

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D-18 

# 2000 Pop Wgt MSA Local Site Name City Name State 

Site 

ID Sample Collection County Name STN? 

Area 

characterist

SIU DENTAL CLINIC Alton IL 2009 Met One SASS Teflon Madison NULL Not in a city MO 0012 Met One SASS Teflon Jefferson 

BLAIR STREET CATEGORY A CORE SLAM PM2.5. 

St. Louis MO 0085 Met One SASS Teflon St. Louis City STN 

18 2,552,994 8.4 Baltimore, MD PMSA; Washington--Baltimore, DC--MD--VA--WV CMSA NE background p

traffic 

ESSEX Essex MD 3001 Met One SASS Teflon Baltimore STN 

19 2,414,616 8.1 Seattle--Bellevue--Everett, WA PMSA; Seattle--Tacoma--Bremerton, WA CMSA Coastal urban, hi

wood burning 

SEATTLE DUWAMISH Seattle WA 0057 Andersen RAAS Teflon King BEACON HILL Seattle WA 0080 Met One SASS Teflon King STN OLIVE STREET Seattle WA 0048 Met One SASS Teflon King 

20 2,395,997 8.1 Tampa--St. Petersburg--Clearwater, FL MSA Coastal with som

and power plant influence 

SYDNEY Plant City FL 3002 Met One SASS Teflon Hillsborough STN NULL Pinellas Park FL 0026 Met One SASS Teflon Pinellas

21 2,392,557 8.1 Oakland, CA PMSA; San Francisco--Oakland--San Jose, CA CMSA

 No speciation monitors?

Coastal urban, hiwood burning 

22 2,358,695 8.0 Pittsburgh, PA MSA Industry & traffi

eastern backgrou

NULL Pittsburgh PA 0008 Met One SASS Teflon Allegheny STN 

NULL Not in a city PA 5001 Met One SASS Teflon Washington 

S ALLEGHENY HS DOWN WIND FROM USS CLAIRTON COKE WORKS 

Liberty PA 0064 Met One SASS Teflon Allegheny 

LAT/LON POINT IS TRAILER Greensburg PA 0008 Met One SASS Teflon Westmoreland 
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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/local/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 particulate matter), ground-level ozone, carbon 

monoxide, sulfur oxides, nitrogen oxides, and lead. 

36 All Tribal monitoring programs are provided for cooperatively as compliance with Federal rules cannot be 

required of Tribes. 

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EPA designs and implements ambient air monitoring networks to meet several 

monitoring objectives: 

 Determining compliance with health- and welfare-based standards (i.e., the NAAQS); 

 Providing air pollution data to the general public on a timely basis; and 

 Supporting the development and tracking of emission control programs. 

In addition to the monitoring objectives above, EPA recognizes important additional 

objectives that need to be factored into designing ambient monitoring networks such as: 

 Supporting health and welfare effects and exposure research studies 

 Providing air pollution data for human health risk/exposure assessments and NAAQS 

reviews; 

 Providing air pollution data for welfare effects assessments; and 

 Supporting atmospheric research studies. 

EPA recently made changes to the NAAQS-related monitoring regulations. Specifically, 

the general monitoring network design requirements for the minimum number of ambient air 

monitors were modified to focus more on populated areas with air quality problems and to 

significantly reduce the requirements for criteria pollutant monitors that have measured ambient 

air concentrations well below the applicable NAAQS. A number of the changes related to the 

monitoring of PM2.5 include revisions to the requirements for reference and equivalent method 

determinations (including specifications and test procedures). These regulations also added a 

requirement for a new multi-pollutant monitoring network called National Core (NCore) and 

revised certain provisions regarding monitoring network descriptions and periodic assessments, 

quality assurance, and data certifications (71 FR 61236, October 17, 2006)37. 

Design criteria for required ambient air monitoring networks are provided in Appendix D 

to 40 CFR Part 58. Network design criteria include monitoring objectives, scale of 

representation, and specifications for locating monitors (e.g., a requirement to be in the area of 

expected maximum concentration). In many cases, there are multiple monitoring objectives for a 

site with the highest concentration of a pollutant. For instance, a neighborhood scale site in the 

area of maximum fine particle exposure could be thought of as a central monitoring station. 

These central monitoring stations might have several PM measurement samplers such as a PM2.5

FRM for comparison to the NAAQS, a PM2.5 continuous mass monitor for reporting the Air 

Quality Index (AQI), and a fine particle speciation sampler to develop and track emission control 

strategies. All of these data could be useful in health studies depending on the purpose and 

availability of health endpoint data. Requirements for the minimum number of monitors to 

operate are identified for PM, ozone, Photochemical Air Monitoring Stations (PAMS), and 

NCore (which include several measurements); however, monitoring agencies are encouraged to 

operate additional stations to adequately characterize pollutants. Siting criteria are provided in 

Appendix E to Part 58. Siting criteria include the specifications for probe and inlet height, 

distance from obstructions, and traffic. 

 

37 See also http://www.epa.gov/ttn/amtic/ for more information on the Ambient Monitoring Technology Information 

Center (AMTIC) operated by EPA's Ambient Air Monitoring Group (AAMG). AMTIC contains information and 

files on ambient air quality monitoring programs, details on monitoring methods, relevant documents and articles, 

information on air quality trends and nonattainment areas, and Federal regulations related to ambient air quality 

monitoring. 

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Roles of EPA and Monitoring Agencies in selecting and approving changes to a monitoring 

network: 

The EPA requires each State monitoring agency to develop and submit an Annual 

Monitoring Network Plan to the applicable EPA Regional Office by July 1 of each year. States 

may delegate portions of the Annual Monitoring Network Plan to applicable local agencies (e.g., 

in California there are several plans, while in New York there is one plan for the whole State). 

The annual monitoring network plan must be made available for public inspection for at least 30 

days prior to submission to EPA. Any annual monitoring network plan that proposes SLAMS 

network modifications including new monitoring sites is subject to the approval of the EPA 

Regional Administrator. Air Toxic monitoring stations are encouraged to be included in annual 

monitoring network plans, but are not formally required. Modifications to PAMS, the Speciation 

Trends Network (STN), and the NCore network are to be approved by EPA’s Office of Air 

Quality Planning and Standards. The EPA Regional Office will provide an opportunity for 

public comment and approve or disapprove the plan and schedule within 120 days of submission. 

If the State or local agency has already provided a public comment opportunity on its plan and 

has made no changes subsequent to that comment opportunity, the Regional Administrator is not 

required to provide a separate opportunity for comment. 

The annual monitoring network plan must contain the following information for each 

existing and proposed site: 

1. The AQS site identification number. 

2. The location, including street address and geographical coordinates. 

3. The sampling and analysis method(s) for each measured parameter. 

4. The operating schedules for each monitor. 

5. Any proposals to remove or move a monitoring station within a period of 18 months 

following plan submittal. 

6. The monitoring objective and spatial scale of representation for each monitor as defined 

in appendix D to this part. 

7. The identification of any sites that are suitable and sites that are not suitable for 

comparison against the annual PM2.5 NAAQS as described in §58.30. 

8. The metropolitan area (e.g., MSA, CBSA, CSA) or other area represented by the monitor. 

The annual monitoring network plan must document how State and local agencies 

provide for the review of changes to a PM2.5 monitoring network that impact the location of a 

violating PM2.5 monitor or the creation/change to a community monitoring zone. The affected 

State or local agency must document the process for obtaining public comment and include any 

comments received through the public notification process within their submitted plan. 

What factors are critical in decisions to change the location of a monitoring station? 

In most cases, monitoring stations are located for many years in the same location; 

however, from time to time a monitoring station is moved or shut down due to either planned or 

unforeseen reasons. The following list provides a summary of the most common reasons why 

monitoring stations are moved or shut down: 

Logistical reasons:

 The lease for the land or building where the monitoring station is located cannot be 

extended due to redevelopment or other reasons. 

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 Construction adjacent to a monitoring station renders the site inappropriate to use during 

construction and in some cases unable to meet siting criteria after construction is 

complete. 

Failure to meet probe and siting criteria: 

 Growth of trees around a monitoring station renders the site no longer able to meet siting 

criteria and the owner of the trees is unwilling to have them cut or trimmed. 

 Increases in motor vehicles traffic, including the addition of new traffic lanes lead to recategorizing the scale of representation or failure to meet set-back requirements; which 

no longer meets the network design criteria. 

 Site inspections reveal that some aspect of the siting criteria is no longer acceptable (e.g., 

a new HVAC system or emission source is located too close to an inlet). 

Changes in responses to emission and/or ambient monitoring trends: 

 Changes to existing point, area, or mobile source emission inventories (e.g., 

establishment of new beltways, shut-down of manufacturing facilities) that alter the 

original premise for site placement. 

 Long-term trends analysis demonstrates monitor’s objective has been fulfilled (taking 

into account future alterations of the NAAQS). For example, steep declines in ambient 

carbon monoxide levels at micro-scale compliance sites led to discontinuation of a large 

number of CO monitors. 

 Network assessment indicates that monitoring resources should be devoted to other issues 

(e.g., air toxics measurement) or conserved to preserve high priority objectives (e.g., 

ozone, PM2.5) 

What feeds into the decision-making process for moving or shutting down a monitoring 

station? 

With any number of reasons why a monitoring station may need to be moved or shut 

down, monitoring agencies must plan for network changes. For situations where it may be 

possible to stay at the existing site, if barriers can be overcome, an agency would likely make the 

necessary efforts to maintain the site if the monitoring objective were critical and no other 

suitable location were available. Knowing that data from a monitoring station were being used 

in an important health or epidemiological study would provide a persuasive argument to keep the 

station in the same place if the agency knew the data were being used. For example, an agency 

may be willing to petition a land owner to trim a tree or move an obstruction given the more 

compelling use of the data in a health research study. 

If a monitoring station has to be moved, how is a new location picked and approved? 

Although usually not possible, the best way to handle moving a monitoring station is to 

identify a new site location within the same general area such that: 

 the scale of representation and impacts from emission sources is the same as the 

original site (so long as this is what is intended to be measured at the monitor); 

 the old and new monitoring stations can be both operated simultaneously for one 

year or, at minimum, during the season(s) of maximum expected concentrations; 

and 

 the statistical analyses of the data from the old and new monitoring stations are 

deemed to be sufficiently comparable. 

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In many cases, the decision to move a monitoring station does not allow enough time to 

provide for simultaneous operation, so careful selection of the new station and analysis of its data 

can only be compared to historical levels and other operating monitors. Use of emission 

inventories, traffic counts, pictures, and satellite imagery can help document site characteristics 

for comparability of old and new sites. 

Monitoring station removals or relocations that are anticipated for the next 18 months 

must be identified in the annual monitoring network plan that is required to be made available for 

public inspection and is due to the EPA Regional Office by July 1 of each year. Although not 

required, ideally the applicable EPA Regional Office will visit and perform a site inspection to 

assure the new station meets siting criteria and is acceptable. In recognition of uncontrollable 

circumstances (e.g., a natural disaster such as Hurricane Katrina), the EPA provides for moving 

an air monitoring station outside the window of an annual monitoring plan by review and 

approval of the applicable EPA Regional Office. 

In what ways can communication with the health research community be improved 

concerning possible changes in the ambient air monitoring networks? 

Improved outreach concerning currently available tools: 

 EPA has developed a web site that provides a link to each available State and local agency 

annual monitoring network plan (see: http://www.epa.gov/ttn/amtic/plans.html). 

 EPA will continue to update the website as plans are revised. EPA is seeking input regarding 

recommendations for how often a reminder should goes out when plans are updated. 

o State/local/Tribal monitoring agencies can be encouraged to summarize their 

anticipated monitoring network changes in one place within the annual monitoring 

network plans, or in a companion summary document, that could be easily scanned 

by interested parties without wading through an extensive plan. 

o Although not currently available, there is recognition that a mechanism allowing for 

quick review of all anticipated network changes across the nation in one place would 

be beneficial to the health research community. Note: this is not available as all 

network plans are currently summarized by the appropriate State, local, and/or Tribal 

agency. 

 EPA has developed a web site dedicated to documenting the site characteristics, including 

photos, and links to satellite imagery of candidate NCore monitoring stations 

(http://www.epa.gov/ttn/amtic/ncore/). At the bottom of each individual NCore station web 

page, there is an opportunity to provide comments on the candidate station. EPA encourages 

health researchers to offer comments on the usefulness of candidate NCore stations. 

 EPA maintains a relatively easy to use public web site that can be used to generate maps and 

lists of active ambient air monitors (http://www.epa.gov/air/data/). 

o An agency contact list is maintained as part of this web site so that data users can 

reach State and local contacts concerning monitors of interest 

(http://www.epa.gov/air/data/contsl.html). 

o A contact list for EPA Regional Office monitoring staff is available at: 

http://www.epa.gov/ttn/amtic/namscon.html. 

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 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.
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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/local/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. 

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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/local/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? 

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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. 
DRAFT 3/27/08 - FOR DISCUSSION PURPOSES ONLY - DO NOT QUOTE OR CITE 

Appendix G: Preliminary Survey of Ambient Air Monitoring Sites Currently 

Being Considered in EPA-funded Epidemiology Studies Feb 2008 

G-1
Preliminary Survey of Ambient Air Monitoring Sites Currently Being Considered in EPA-funded Epidemiology S

for more information, contact Sascha Lodge, lodge.sascha@epa.gov; (202) 343-9769

PLEASE NOTE: Colors signify that a given monitor is being used by multiple researchers. There is no distinction between blue and orange. For example, four researchers are using the Bakersfield (CA) monitor, three researchers are using the El Cajon (CA) monitor, and only one researcher is using the Phoenix (AZ) monitor.

State City County Site Name State 

Code

County Code

Site ID

Priority Address Mon 

Objective1

Mon Objective2

Latitude Longitude Parameters 

Measured

ReseaName

STN SitesAlabama Birmingham Jefferson NULL 01 73 0023 Medium 33.553056 -86.815000 Kaz Ito 

Arizona Phoenix Maricopa PHOENIX 

SUPERSITE

04 013 9997 High 33.503643 -112.095001 Kaz Ito 

California Bakersfield Kern FLAT TERRAIN,OIL

REFINERY 1.3 MINNW,TRAIN 1.4 MIN,FREEWAY 1.3 MIE

06 029 0014 Medium 5558 

California Ave; Bakersfield

Population Exposure

35.356111 -119.040278 if available

NO2 OZ PM10PM25 PM25species

Bart O

California Bakersfield Kern FLAT TERRAIN,OIL

REFINERY 1.3 MINNW,TRAIN 1.4 MIN,FREEWAY 1.3 MIE

06 029 0014 High 35.356111 -119.040278 Antone

Zanob

California Bakersfield Kern FLAT TERRAIN,OIL

REFINERY 1.3 MINNW,TRAIN 1.4 MIN,FREEWAY 1.3 MIE

06 029 0014 High 35.356111 -119.040278 Kaz Ito 

California Bakersfield Kern FLAT TERRAIN,OIL

REFINERY 1.3 MINNW,TRAIN 1.4 MIN,FREEWAY 1.3 MIE

06 029 0014 High 35.356111 -119.040278 Kaz Ito 
California El Cajon San Diego NULL 06 073 0003 Medium 1155 

Redwood Ave.; ElCajon

Population Exposure

32.791389 -116.941667 if available

NO2 OZ PM10PM25 PM25species

Bart O

California El Cajon San Diego NULL 06 073 0003 High 32.791389 -116.941667 Antone

Zanob

California El Cajon San Diego NULL 06 073 0003 High 32.791389 -116.941667 Kaz Ito 

California Riverside Riverside Mira Loma 06 065 8005 High 5130 

Poinsettia Place

Other 33.995638 -117.493304 PM2.5, PM10 Joel 

Kaufm

California Rubidoux 

(West Riverside)

Riverside NULL 06 065 8001 Medium 5888 Mission

Blvd.; Rubidoux

Population Exposure

33.999580 -117.416010 if available

NO2 OZ PM10PM25 PM25species

Bart O

California Rubidoux 

(West Riverside)

Riverside Riverside-Rubidoux 06 065 8001 High 5888 Mission

Blvd., Rubidoux

Population Exposure (RiversideSan Bernardino, CA)

33.99958 -117.41601 PM2.5, PM10,

SO2, NO2, Oz,CO

Joel Kaufm

California Rubidoux 

(West Riverside)

Riverside NULL 06 065 8001 High 33.999580 -117.416010 Antone

Zanob

California Rubidoux 

(West Riverside)

Riverside NULL 06 065 8001 High 33.999580 -117.416010 Kaz Ito 

California Rubidoux 

(West Riverside)

Riverside NULL 06 065 8001 High 33.999580 -117.416010 Kaz Ito 

California Sacramento Sacrament

o

NULL 06 067 0006 Medium Del Paso2701 Avalon

Dr; Sacramento

Population Exposure

38.614167 -121.366944 if available

NO2 OZ PM10PM25 PM25species

Bart O

California Sacramento Sacrament

o

NULL 06 067 0006 High 38.614167 -121.366944 Antone

Zanob

California Sacramento Sacrament

o

NULL 06 067 0006 High 38.614167 -121.366944 Kaz Ito 
f f

STATION

California San Jose Santa Clara SAN JOSE

JACKSON ST

06 085 0005 Medium 156B 

Jackson Street; SanJose

Population Exposure

37.348500 -121.895000 if available

NO2 OZ PM10PM25 PM25species

Bart O

California San Jose Santa Clara SAN JOSE

JACKSON ST

06 085 0005 High 37.348500 -121.895000 Kaz Ito 

California Simi Valley Ventura NULL 06 111 2002 High 34.277500 -118.684722 Kaz Ito 

Colorado Commerce 

City

Adams ALSUP 

ELEMENTARY SCHOOLCOMMERCE CITY

08 001 0006 High 39.825739 -104.936987 Kaz Ito 

Connecticu New Haven New Haven NULL 09 009 0027 High 41.301111 -72.902778 Kaz Ito 

tDistrict OColumbia

Washington District o

Columbia

MCMILLAN PAMS 11 001 0043 High 38.918889 -77.012500 Kaz Ito 

Florida Davie Broward NULL 12 011 1002 High 26.082778 -80.237778 Kaz Ito 

Florida Plant City Hillsboroug SYDNEY 12 057 3002 High 27.965650 -82.230400 Kaz Ito 

Georgia Decatur

h

DeKalb 2390-B WILDCAT

ROAD, DECATUR,

13 089 0002 High 33.688007 -84.290325 Kaz Ito 

Idaho Meridian Ada

GA

NULL 16 001 0010 Medium 43.607568 -116.348434 Kaz Ito 

Illinois Chicago Cook Lawndale Comm-Ed 17 031 0076 High 7801 

Lawndale

Population Exposure (Chicago, ILNorthwestern Indiana) 

41.751369 -87.713745 PM2.5, SO2,

NO2, Oz

Joel Kaufm

Illinois Chicago Cook COM ED

MAINTENANCE 

17 031 0076 High 41.751369 -87.713745 Kaz Ito 

Illinois Chicago Cook

BLDG

COM ED

MAINTENANCE 

17 031 0076 High 41.751369 -87.713745 Antone

Zanob

Indiana Indianapolis Marion

BLDG

IN PARKING LOT

NEXT TO POLICE

18 097 0078 High 39.811097 -86.114469 Kaz Ito 
Kansas Kansas City Wyandotte JFK 20 209 0021 Medium 39.117500 -94.635556 Kaz Ito 

Kansas Kansas City Wyandotte JFK 20 209 0021 High 39.117500 -94.635556 Antone

Zanob

Louisiana Baton 

Rouge

East BatonRouge

NULL 22 033 0009 Medium 30.461111 -91.176944 Kaz Ito 

Maryland Essex Baltimore Essex 24 005 3001 High Woodward 

And FranklinRoads Essex

Population Exposure (Baltimore, MD)

39.310833 -76.474444 PM2.5, SO2,

NO2, Oz, CO

Joel Kaufm

Maryland Essex Baltimore ESSEX 24 005 3001 High 39.310833 -76.474444 Kaz Ito 

Massachusetts

Boston Suffolk DUDLEY SQUARE

ROXBURY

25 025 0042 High 42.329444 -71.082778 Kaz Ito 

Massachusetts

Boston Suffolk DUDLEY SQUARE

ROXBURY

25 025 0042 High 42.329444 -71.082778 Antone

Zanob

Massachus Chicopee Hampden NULL 25 013 0008 High 42.194460 -72.555711 Kaz Ito 

ettsMichiganMichigan

Allen ParkDetroit

Wayne NULLWayne

2626

163163

00010001

HighHigh

42.22861142.228333

-83.208333-83.209167

Kaz ItoAntone

Zanob

Minnesota Minneapolis Hennepin Phillips 27 053 0963 High 2727 10th St.

Minneapolis

Population Exposure (Minneapolis-St. Paul,MN)

44.955396 -93.25827 PM2.5 Joel 

Kaufm

Minnesota Minneapolis Hennepin ANDERSON 

SCHOOL - PHILLIPSNEIGHBORHOOD

27 053 0963 High 44.955396 -93.258270 Kaz Ito 

Minnesota Minneapolis Hennepin ANDERSON 

SCHOOL - PHILLIPSNEIGHBORHOOD

27 053 0963 High 44.955396 -93.258270 Antone

Zanob
Mississippi Gulfport Harrison BEHIND HARRISON

COUNTY YOUTHCOURT

28 047 0008 High 30.390139 -89.049722 Kaz Ito 

Missouri St. Louis St Louis

(City)

BLAIR STREETCATEGORY ACORE SLAM PM2.5.

29 510 0085 High 38.656300 -90.198100 Antone

Zanob

Missouri St. Louis St. Louis

City

BLAIR STREETCATEGORY ACORE SLAM PM2.5.

29 510 0085 High 38.656300 -90.198100 Kaz Ito 

Montana Missoula Missoula NULL 30 063 0031 Medium 46.874912 -113.995253 Kaz Ito 

Nebraska Omaha Douglas NULL 31 055 0019 High 41.247222 -95.975556 Kaz Ito 

Nevada Reno Washoe NULL 32 031 0016 Medium 39.525083 -119.807717 Kaz Ito 

New Elizabeth Union ELIZABETH LAB 34 039 0004 High 40.641440 -74.208360 Kaz Ito 

Jersey

New Jersey

North Brunswick (Township 

Middlesex NEW BRUNSWICK 34 023 0006 High 40.472790 -74.422510 Kaz Ito 

New York

of)

New York Bronx I.S. 52 36 005 0110 High E 156th St

Bet DawsonAnd Kelly

Population Exposure (New York,NYNortheastern NewJersey)

40.81616 -73.90207 PM2.5, SO2,

NO2, Oz

Joel Kaufm

New York New York Bronx IS 52 36 005 0110 High 40.816160 -73.902070 Kaz Ito 

North Carolina

Charlotte Mecklenbur

g

Garinger High School 37 119 0041 High 35.240278 -80.785556 NO2 OZ PM10

CO

Adel H

North Charlotte Mecklenbur Garinger High School 37 119 0041 High 35.240278 -80.785556 Kaz Ito

CarolinaNorth Fargo

g

Cass FARGO NW 38 017 1004 High 46.933754 -96.855350 Kaz Ito 

DakotaOhio Cleveland Cuyahoga GT CRAIG 39 035 0060 High 41.493955 -81.678542 Kaz Ito 
Ohio Cleveland Cuyahoga GT CRAIG 39 035 0060 High 41.493955 -81.678542 Antone

Zanob

Oklahoma Tulsa Tulsa NORTH TULSA -

FIRE STATION#24AT 36TH ANDPEORIANR

40 143 1127 High 36.204902 -95.976537 Kaz Ito 

Oregon Portland Multnomah NULL 41 051 0080 High 45.496667 -122.602222 Kaz Ito 

Pennsylva Philadelphia Philadelphi AMS Laboratory 42 101 0004 High 40.008889 -75.097778 Kaz Ito 

niaPennsylvania

Philadelphia

a

Philadelphi

a

AMS Laboratory 42 101 0004 High 40.008889 -75.097778 Antone

Zanob

Pennsylva Pittsburgh Allegheny NULL 42 003 0008 High 40.465556 -79.961111 Kaz Ito 

niaPennsylvania

Pittsburgh Allegheny NULL 42 003 0008 High 40.465556 -79.961111 Antone

Zanob

Rhode Providence Providence BUILDING 44 007 0022 High 41.807949 -71.415000 Kaz Ito 

IslandSouth Charleston Charleston

ROOFTOP

CHARLESTON 45 019 0049 High 32.790984 -79.958694 Kaz Ito 

CarolinaTennessee Knoxville Knox

PUBLIC WORKS

NULL 47 093 1020 Medium 36.019440 -83.873610 Kaz Ito 

TexasTexas

DallasDallas

DallasDallas

DALLAS HINTONDALLAS HINTON

4848

113113

00690069

HighHigh

32.81995232.819952

-96.860082-96.860082

Kaz ItoAntone

Zanob

Texas Deer Park Harris NW OF W.

LAMBUTH &DURANT 

48 201 1039 High 29.670046 -95.128485 Kaz Ito 

Texas Deer Park Harris

INTERSECTIONNW OF W.LAMBUTH &DURANT 

48 201 1039 High 29.670046 -95.128485 Antone

Zanob

INTERSECTION

TexasTexas

El PasoEl Paso

El PasoEl Paso

CHAMIZALCHAMIZAL

4848

141141

00440044

HighHigh

31.76567331.765673

-106.455225-106.455225

Kaz ItoAntone

Zanob
SITE

Utah Salt Lake

City

Salt Lake UTM 

COORDINATES =PROBE LOCATION

49 035 3006 High 40.736389 -111.872222 Kaz Ito 

Vermont Burlington Chittenden ZAMPIERI STATE

OFFICE BUILDING,CORNER OFCHERRY STREET

50 007 0012 Medium 44.480278 -73.214444 Kaz Ito 

Virginia Not in a city Henrico NULL 51 087 0014 High 37.558333 -77.400278 Kaz Ito 

Washingto Seattle King BEACON HILL 53 033 0080 High 47.570273 -122.308596 Kaz Ito 

nWashington

Seattle King BEACON HILL 53 033 0080 High 47.570273 -122.308596 Antone

Zanob

Washington

Seattle King Beacon Hill 53 033 0080 High 4103 Beacon

Ave. S.

Population Exposure (SeattleTacomaBellevue, WA)

47.570273 -122.308596 PM2.5, SO2,

NO2, Oz, CO

Tim La

West Not in a city Kanawha NULL 54 039 0011 Medium 38.448611 -81.683889 Kaz Ito 

Virginia

Wisconsin Milwaukee Milwaukee DNR SER HQRS 55 079 0026 High 43.061111 -87.912500 Kaz Ito 

SLAMSCalifornia Escondido San Diego NULL 06 073 1002 Medium 600 E. Valley

Pkwy.; Escondido

Population Exposure

33.127778 -117.074167 if available

NO2 OZ PM10PM25 PM25species

Bart O

California Fresno Fresno NULL 06 019 0008 Medium 3425 N First

St; Fresno

Population Exposure

36.781389 -119.772222 if available

NO2 OZ PM10PM25 PM25species

Bart O

California Fresno Fresno 06 019 0008 High 36.781389 -119.772222 Antone

Zanob
(RG&E Substation)

exposure CO, O3 

California Los Angeles Los 

Angeles

NULL 06 037 1103 Medium 1630 N Main

St; LosAngeles

Population Exposure

34.066590 -118.226880 if available

NO2 OZ PM10PM25 PM25species

Bart O

California Los Angeles Los 

Angeles

06 037 1103 High 34.06659 -118.22688 Antone

Zanob

California Los Angeles Los 

Angeles

Los Angeles-NorthMain Street

06 037 1103 High 1630 N Main

St, LosAngeles

Population Exposure (Los AngelesLong Beach,CA 

34.06659 -118.22688 PM2.5, PM10,

SO2, NO2, Oz,CO

Joel Kaufm

California Sacramento Sacrament

o

NULL 06 067 0010 Medium 1309 T St.;

Sacramento

MSA)Population Exposure

38.558333 -121.491944 if available

NO2 OZ PM10PM25 

Bart O

Illinois Chicago Cook Springfield Pump

Station

17 031 0057 Medium 1745 N.

Springfield

Population Exposure (Chicago, ILNorthwestern Indiana)

41.914733 -87.722725

PM25species

PM2.5 Joel 

Kaufm

Illinois Northbrook Cook Northbrook Water

Plant

17 031 4201 High 750 Dundee

Road

Population Exposure (Chicago, ILNorthwestern Indiana)

42.14 -87.799167 PM2.5, PM10,

SO2, NO2, Oz,CO

Joel Kaufm

Missouri Not in a city Clay 29 047 0005 High 39.303056 -94.376389 Antone

New York Rochester Monroe Rochester 2 36 055 1007 High Yarmouth Rd Population 43.146198 -77.54813 PM2.5, SO2,

ZanobPhilip 
n Zanob

North Carolina

Asheville Buncombe BOARD OF ED

BLDG NW CORNER

37 021 0034 Medium 35.609722 -82.350833 NO2 OZ PM10

CO

Adel H

North Raleigh Wake

PARKING LOTNULL 37 183 0014 High 35.856111 -78.574167 NO2 OZ PM10 Adel H

CarolinaNorth Carolina

WinstonSalem Forsyth Hattie Avenue 37 067 0022 High 1300 Blk. Hattie 

Avenue

Population Exposure (WinstonSalem, NC)

36.110556 -80.226667

CO

PM2.5, PM10,

SO2, NO2, Oz

Joel Kaufm

Ohio Akron Summit 39 153 0023 High 41.088056 -81.541667 Antone

Ohio Columbus Franklin 39 049 0081 High 40.087778 -82.959722

Zanob

Antone

Ohio Toledo Lucas 39 095 0026 High 41.620556 -83.641389

Zanob

Antone

Pennsylva Erie Erie 42 049 0003 High 42.14175 -80.038611

Zanob

Antone

niaPennsylva Harrisburg Dauphin 42 043 0401 High 40.245 -76.844722

Zanob

Antone

niaWashingto Seattle King 53 033 0057 High 47.563333 -122.338333

Zanob

Antone

Other network sites CA Azusa Los 

Angeles

Azusa 06 037 0002 Low 803 N. Loren

Ave., Azusa

HIGHEST CO

OTHER 34.1365 -117.923 PM, NOx Joel 

Kaufm

CA Los 

Angeles

06 037 0016 Low 34.1443 -117.85 NOx Joel 

Kaufm
CA Los 

Angeles

06 037 0031 Low OTHER POPULATIO

N

33.7861 -118.246 NOx Joel 

Kaufm

CA Los 

Angeles

06 037 0113 Low 34.0511 -118.456 NOx Joel 

Kaufm

CA Burbank Los 

Angeles

Burbank 06 037 1002 Low 228 W. Palm

Ave., 

GENERAL/BA

OTHER 34.176 -118.317 PM, NOx Joel 

Kaufm

CA Los Angeles Los 

Angeles

06 037 1103

Burbank

Low HIGHEST 

CO

OTHER 34.0665 -118.226 PM, NOx Joel 

Kaufm

CA Reseda Los 

Angeles

Reseda 06 037 1201 Low 18330 Gault

St., Reseda

OTHER POPULATIO

N

34.1992 -118.532 PM, NOx Joel 

Kaufm

CA Lynwood Los 

Angeles

Lynwood 06 037 1301 High 11220 Long

Beach Blvd.,Lynwood

Population Exposure (Los Angeles, 

33.92899 -118.21071 PM2.5, NO2,

Oz, CO

Joel Kaufm

CA Pico Rivera Los 

Angeles

Pico Rivera #1 06 037 1601 Low

CA)

HIGHEST 

CO

MAX PRECUR

34.014 -118.06 PM, NOx Joel 

Kaufm

CA Pico Rivera Los 

Angeles

Pico Rivera #2 06 037 1602 High 4144 San

Gabriel RiverPkwy, PicoRivera

Population Exposure (Los Angeles, 

34.01407 -118.06995 PM2.5, NO2,

Oz, CO

Joel Kaufm

CA Los 

Angeles

06 037 1701

CA)

Low 34.067 -117.751 NOx Joel 

Kaufm
CA Pasadena Los 

Angeles

Pasadena 06 037 2005 High 752 S.

Wilson Ave.,Pasadena

Population Exposure (Los Angeles, 

34.1326 -118.1272 PM2.5, NO2,

Oz, CO

Joel Kaufm

CA Long Beach Los 

Angeles

North Long Beach(Long Beach)

06 037 4002 Low 3648 N.

Long BeachBlvd., LongBeach

CA)HIGHEST CO

OTHER 33.8237 -118.189 PM, NOx Joel 

Kaufm

CA Long Beach Los 

Angeles

South Long Beach 06 037 4004 Low 1305 E.

Pacific CoastHwy., LongBeach

OTHER 33.7923 -118.175 PM Joel 

Kaufm

CA Los 

Angeles

06 037 5001 Low MAX 

OZONE

POPULATION

33.9228 -118.37 NOx Joel 

Kaufm

CA Los 

Angeles

06 037 5005 Low UPWIND 

BAC

33.9508 -118.43 NOx Joel 

Kaufm

CA Los 

Angeles

06 037 6012 Low OTHER POPULATIO

N

34.3834 -118.528 NOx Joel 

Kaufm

CA Los 

Angeles

06 037 9002 Low POPULATI

ON

34.69 -118.131 NOx Joel 

Kaufm

CA Lancaster Los 

Angeles

06 037 9033 Low 43301 

Division St.,Lancaster, 

POPULATION

34.6713 -118.13 PM, NOx Joel 

Kaufm

CA Orange 06 059 0001

Ca

Low OTHER POPULATIO

N

33.8306 -117.938 PM, NOx Joel 

Kaufm
CA Anaheim Orange Anaheim-Loara 

School

06 059 0007 Low 1630 W.

Pampas 

POPULATION

33.8306 -117.938 PM, NOx Joel 

Kaufm

CA Riverside Orange Riverside-Magnolia 06 059 1003 Low

Lane

7002 

Magnolia 

33.6746 -117.925 NOx Joel 

Kaufm

CA Mission 

Viejo

Orange Mission Viejo 06 059 2022 Low

Ave., Riverside

26081 Via

Pera, Mission 

OTHER POPULATIO

N

33.63 -117.675 PM Joel 

Kaufm

CA Palm 

Springs

Orange 06 059 5001 Low

Viejo, Ca92691

Fs-590 

Racquet Club Ave,

POPULATION

33.9251 -117.952 NOx Joel 

Kaufm

CA Riverside 06 065 0012

Palm Springs

Low POPULATI

ON

UPWIND BAC

33.9208 -116.858 NOx Joel 

Kaufm

CA Riverside Riverside Riverside-Magnolia 06 065 1003 Low 7002 

Magnolia 

HIGHEST CO

OTHER 33.946 -117.4 PM Joel 

Kaufm

CA Indio Riverside 06 065 2002 Low

Ave., Riverside

46-990 

Jackson St.,

OTHER POPULATIO

N

33.7085 -116.215 PM Joel 

Kaufm

CA Palm 

Springs

Riverside 06 065 5001 Low

Indio

Fs-590 

Racquet Club Ave,

OTHER POPULATIO

N

33.8527 -116.541 PM, NOx Joel 

Kaufm

CA Rubidoux 

(West 

Riverside Big Bear 06 065 8001 Low

Palm Springs

501 W.

Valley Blvd.,

HIGHEST CO

OTHER 33.9995 -117.416 PM, NOx Joel 

Kaufm

Riverside)

CA Riverside 06 065 9001

Big Bear City

Low POPULATI

ON

33.6764 -117.33 NOx Joel 

Kaufm
Street, Simi

Valley 

CA San 

Bernardino

06 071 0001 Low POPULATI

ON

REGIONAL T

34.895 -117.023 NOx Joel 

Kaufm

CA Ontario San 

Bernardino

Ontario-Fire Station 06 071 0025 Low 1408 Francis

St.

OTHER POPULATIO

N

34.0372 -117.69 PM Joel 

Kaufm

CA Victorville San 

Bernardino

06 071 0306 Low 14306 Park

Ave., 

POPULATION

REGIONAL T

34.51 -117.33 PM, NOx Joel 

Kaufm

CA San 

Bernardino

06 071 1004

Victorville, Ca

Low GENERAL/

BA

HIGHEST CO

34.1037 -117.629 NOx Joel 

Kaufm

CA Fontana San 

Bernardino

Fontana-Arrow Highway

06 071 2002 Low 14360 Arrow

Blvd., 

HIGHEST CO

OTHER 34.1 -117.492 PM, NOx Joel 

Kaufm

CA San 

Bernardino

San Bernardino

San Bernardino 06 071 9004 Low

Fontana

24302 4th

St., San

HIGHEST CO

OTHER 34.1068 -117.274 PM, NOx Joel 

Kaufm

CA Thousand 

Oaks

Ventura 06 111 0007 Low

Bernardino, Ca

2323 

Moorpark 

OTHER POPULATIO

N

34.21 -118.869 PM, NOx Joel 

Kaufm

CA Piru Ventura 06 111 0009 Low

Road, Thousand Oaks3301 Pacific

Avenue, Piru, 

OTHER POPULATIO

N

34.4046 -118.81 PM Joel 

Kaufm

CA Ventura 06 111 1004

Ca 93040

Low OTHER POPULATIO 34.4483 -119.23 NOx Joel 

CA Simi Valley Ventura 06 111 2002 Low 5400 

Cochran 

HIGHEST CO

NOTHER 34.2775 -118.684 PM, NOx

Kaufm

Joel 

Kaufm
ON Kaufm

CA Ventura 06 111 2003 Low GENERAL/ POPULATIO 34.2804 -119.313 NOx Joel 

CA El Rio Ventura 06 111 3001 Low Rio Mesa

BAGENERAL/

NHIGHEST 34.255 -119.142 PM, NOx

Kaufm

Joel 

CT Fairfield 09 001 0010

School, ElRio

Low

BA

GENERAL/

COHIGHEST 41.1708 -73.1947 PM

Kaufm

Joel 

CT Fairfield 09 001 0113 Low

BA

HIGHEST 

COOTHER 41.1836 -73.1902 PM

KaufmJoel 

CT Fairfield 09 001 1123 Low

CO

POPULATI 41.3991 -73.443 PM

Kaufm

Joel 

CT Fairfield 09 001 2124 Low

ON

HIGHEST POPULATIO 41.063 -73.5288 PM

Kaufm

Joel 

CT Fairfield 09 001 3005 Low

CO

HIGHEST 

NPOPULATIO 41.1125 -73.4072 PM

Kaufm

Joel 

CT Fairfield 09 001 9003 Low

CO

GENERAL/

NPOPULATIO 41.1183 -73.3366 PM, NOx

Kaufm

Joel 

CT Hartford 09 003 1003 Low

BAGENERAL/

NHIGHEST 41.7847 -72.6316 NOx

Kaufm

Joel 

CT New Haven 09 009 0018 Low

BAHIGHEST 

COOTHER 41.2938 -72.9013 PM

KaufmJoel 

CT New Haven 09 009 0026 Low

COPOPULATI 41.2911 -72.8941 PM

Kaufm

Joel 
Justison St. CO Kaufm

CT New Haven 09 009 0027 Low GENERAL/ HIGHEST 41.3011 -72.9027 PM, NOx Joel 

CT New Haven 09 009 1123 Low

BAHIGHEST 

COOTHER 41.3108 -72.9169 PM, NOx

Kaufm

Joel 

CT New Haven 09 009 2008 Low

COPOPULATI 41.3313 -72.9197 PM

Kaufm

Joel 

CT New Haven 09 009 2123 Low

ONHIGHEST OTHER 41.5505 -73.0436 PM

KaufmJoel 

CT New Haven 09 009 9005 Low

COHIGHEST MAX 41.3411 -72.9213 PM, NOx

Kaufm

Joel 

DE Kent 10 001 0002 Low State Road

COGENERAL/

PRECUROTHER 38.9847 -75.5555 PM

KaufmJoel 

DE Dover Kent 10 001 0003 Low

384, KillensPond RdWater St.

BAPOPULATI 39.155 -75.518 PM

Kaufm

Joel 

DE Bellefonte New Castle 10 003 1003 Low

Dover

River Road

ONHIGHEST POPULATIO 39.7611 -75.4919 PM

Kaufm

Joel 

DE New Castle 10 003 1007 Low

ParkLums Pond

COOTHER

NPOPULATIO 39.5511 -75.7308 PM

Kaufm

Joel 

DE Newark New Castle 10 003 1012 Low

State ParkUniv. Del. OTHER

NPOPULATIO 39.6919 -75.7616 PM

Kaufm

Joel 

DE Wilmington New Castle 10 003 2004 Low

North Campus

Mlk Blvd And HIGHEST 

NOTHER 39.7394 -75.558 PM, NOx

Kaufm

Joel 
DE Seaford Sussex 10 005 1002 Low 350 Virginia OTHER POPULATIO 38.6444 -75.613 PM Joel 

DC District of 11 001 0025

Ave Seaford

Low

N

POPULATI 38.9752 -77.0227 NOx

Kaufm

Joel 

DC

ColumbiaDistrict of 11 001 0041 Low

ON

HIGHEST POPULATIO 38.8972 -76.9527 PM, NOx

Kaufm

Joel 

DC

ColumbiaDistrict of 11 001 0042 Low

CO

GENERAL/

NHIGHEST 38.8808 -77.0325 PM

Kaufm

Joel 

DC

ColumbiaDistrict of 11 001 0043 Low

BA

HIGHEST 

COMAX 38.9188 -77.0125 PM, NOx

Kaufm

Joel 

IL Chicago

ColumbiaCook Farr Dormitory 17 031 0014 Low 3300 S

CO OZONEPOPULATI 41.8342 -87.6238 PM

Kaufm

Joel 

IL Chicago Cook Washington 17 031 0022 Medium

Michigan Ave

3535 E.

114th St.

ONPopulation Exposure (Chicago, ILNorthwestern Indiana)

41.689195 -87.539318 PM2.5

Kaufm

Joel 

Kaufm
ON Kaufm

IL Chicago Cook S.E. Chicago 17 031 0050 Medium 103rd And

Luella

Source Oriented (Chicago, ILNorthwestern Indiana),Population Exposure (Chicago, ILNorthwestern Indiana)

41.709561 -87.568576 PM2.5, SO2 Joel 

Kaufm

IL Chicago Cook Mayfair Pumping Stn. 17 031 0052 Medium 4850 Wilson

Ave.

Population Exposure (Chicago, ILNorthwestern Indiana),Highest Concentration (Chicago, ILNorthwestern Indiana)

41.967429 -87.749819 PM2.5, PM10 Joel 

Kaufm

IL Chicago Cook Springfield Pump 17 031 0057 Low 1745 N. POPULATI 41.9147 -87.7227 PM Joel 

IL

Station

Cook 17 031 0063

Springfield

Low

ON

HIGHEST POPULATIO 41.8772 -87.6344 NOx

Kaufm

Joel 

IL Cook 17 031 0075 Low

CO N

POPULATI 41.9641 -87.6586 NOx

Kaufm

Joel 
IL Cicero Cook 17 031 6005 Low 13th St. & POPULATI 41.8642 -87.7488 PM Joel 

50th Ave. ON Kaufm

IL Chicago Cook Lawndale Comm-Ed 17 031 0076 Low 7801 GENERAL/ HIGHEST 41.7513 -87.7137 PM, NOx Joel 

IL Mccook Cook 17 031 1016 Low

Lawndale

50th St. And

BAHIGHEST 

COPOPULATIO 41.8011 -87.8319 PM

Kaufm

Joel 

IL Blue Island Cook 17 031 2001 Medium

Glencoe

12700 

Sacramento

CO NPopulation Exposure (Chicago, ILNorthwestern Indiana)

41.663997 -87.696468 PM2.5, PM10

Kaufm

Joel 

Kaufm

IL Schiller Park Cook 17 031 3103 Low 4743 HIGHEST POPULATIO 41.9652 -87.8763 PM, NOx Joel 

IL Summit Cook 17 031 3301 Low

Mannheim Rd.

60th St. &

CO NPOPULATI 41.7827 -87.8052 PM

Kaufm

Joel 

IL Cook 17 031 4002

74th Ave.

Low

ON

HIGHEST POPULATIO 41.8552 -87.7524 NOx

Kaufm

Joel 

IL Des Plaines Cook 17 031 4007 High 9511 W.

Harrison St

CO NPopulation Exposure (Chicago, ILNorthwestern Indiana)

42.060278 -87.863333 PM2.5, Oz

Kaufm

Joel 

Kaufm

IL Northbrook Cook Northbrook Water 17 031 4201 Low 750 Dundee MAX POPULATIO 42.14 -87.7991 PM, NOx Joel 

Plant Road OZONE N Kaufm
IN Pittsboro Hendricks 18 063 0003 Low High School, HIGHEST OTHER 39.8808 -86.5421 NOx Joel 

Pittsboro CO Kaufm

IL Cook 17 031 8003 Low POPULATI

ON

41.6313 -87.568 NOx Joel 

Kaufm

IL Naperville DuPage City Hall 17 043 4002 Low 400 S. Eagle

St.

POPULATION

41.7711 -88.1525 PM Joel 

Kaufm

IL Elgin Kane 17 089 0003 Low 258 Lovell

St.

POPULATION

42.0502 -88.2802 PM Joel 

Kaufm

IL Lake 17 097 1007 Low Illinois Beach

State Park

EXTREME DO

HIGHEST CO

42.4675 -87.81 PM Joel 

Kaufm

IL McHenry 17 111 0001 Low POPULATI

ON

42.2214 -88.242 PM Joel 

Kaufm

IL Will 17 197 1002 Low HIGHEST 

CO

POPULATION

41.5266 -88.1163 PM Joel 

Kaufm

IL Will 17 197 1011 Low GENERAL/

BA

POPULATION

41.2215 -88.1909 NOx Joel 

Kaufm

IN Gibson 18 051 0010 Low 38.2762 -87.5529 NOx Joel 

Kaufm

IN Hendricks 18 063 0001 Low Cr 800 N

And Cr 275E

HIGHEST CO

OTHER 39.8769 -86.4738 NOx Joel 

Kaufm

IN Hendricks 18 063 0002 Low 206 N.

Meridian St.

HIGHEST CO

OTHER 39.8633 -86.4707 NOx Joel 

Kaufm
IN Dune Acres Porter 18 127 0020 Low Indiana 

Dunes N.Lakeshore

HIGHEST CO

POPULATION

41.6313 -87.0869 PM Joel 

Kaufm

IN East 

Chicago

Lake 18 089 0006 High Franklin 

School Alder

& 142nd St

HIGHEST CO

POPULATION

41.6361 -87.4408 PM Joel 

Kaufm

IN Gary Lake 18 089 0022 High 201 

Mississippi St., IitriBunker

HIGHEST CO

POPULATION

41.6066 -87.3047 PM, NOx Joel 

Kaufm

ININ

GaryGriffith

LakeLake

1818

089089

00260027

HighHigh

25th And

Burr Street

Ready Eldon

HIGHEST CO

POPULATION

POPULATI

41.57341.5466

-87.4058-87.4263

PMPM

Joel 

Kaufm

Joel 

School, 1345N. Broad St.

ON Kaufm

IN Gary Lake 18 089 1003 High Ivanhoe 

School 15th& Gerry Sts

POPULATION

41.5888 -87.4077 PM Joel 

Kaufm

ININ Hammond

LakeLake

1818

089089

10162004

HighHigh Purdue Univ

CalumetHIGHEST CO POPULATIO N POPULATI ON 41.6002 41.5852 -87.3347 -87.4744 PMPM Joel Kaufm

Joel Kaufm

IN Hammond Lake 18 089 2010 High

Powers Building 6937

1921 Davis POPULATI 41.6783 -87.5083 PM Joel 

IN Indianapolis Marion 18 097 0073 Low

St., Robertsdale, Clark H.S.

Naval 

ONHIGHEST OTHER 39.7891 -86.0608 NOx

KaufmJoel 

Avionics Center, 6125E. 16th St.

CO Kaufm
IA Linn 19 113 0036 Low POPULATI

ON

41.9702 -91.62 PM Joel 

Kaufm

IN Ogden 

Dunes (Wickliffe)

Porter 18 127 0024 Low 84 Diana Rd/

Water Treatment Plant

HIGHEST CO

POPULATION

41.6175 -87.1991 PM Joel 

Kaufm

IN St. Joseph 18 141 1008 Low OTHER POPULATIO

N

41.6936 -86.2366 NOx Joel 

Kaufm

IN Spencer 18 147 0008 Low 37.9811 -87.0325 NOx Joel 

Kaufm

IN Evansville Vanderburg

h

18 163 0012 Low 425 West Mill

Road/ FireStation #17

HIGHEST CO

OTHER 38.0216 -87.5694 NOx Joel 

Kaufm

IA Waterloo Black Hawk 19 013 0008 Low HIGHEST 

CO

POPULATION

42.493 -92.3438 PM Joel 

Kaufm

IA Cerro 

Gordo

19 033 0019 Low OTHER POPULATIO

N

43.1616 -93.2083 PM Joel 

Kaufm

IA Clinton Clinton 19 045 0021 Low Roosevelt St. OTHER POPULATIO

N

41.8749 -90.1774 PM Joel 

Kaufm

IA Emmet 19 063 0003 Low GENERAL/

BA

POPULATION

43.3975 -94.8172 PM Joel 

Kaufm

IA Iowa City Johnson 19 103 2001 Low 2200 East

Court

POPULATION

41.6573 -91.5034 PM Joel 

Kaufm

IA Cedar 

Rapids

Linn 19 113 0033 Low 408 E. Linn

St. Coggon,Iowa

POPULATION

42.2805 -91.5269 NOx Joel 

Kaufm
IA Davenport Scott 19 163 0015 Medium 10th St. &

Vine St.Davenport

HIGHEST CO

OTHER 41.53 -90.5875 PM Joel 

Kaufm

IA Cedar 

Rapids

Linn 19 113 0037 Low 1599 Wenig

Rd Ne

POPULATION

42.0083 -91.6786 PM Joel 

Kaufm

IA Muscatine Muscatine 19 139 0015 Low 1409 

Wisconsin

POPULATION

41.4008 -91.0677 PM Joel 

Kaufm

IAIA

EmmetsburgDes Moines

Palo AltoPolk

1919

147153

10020030

LowLow

Iowa Lakes

Community College

1907 

GENERAL/BAOTHER

POPULATIONPOPULATIO

43.123341.603

-94.6933-93.643

PMPM

Joel 

Kaufm

Joel 

Carpenter, Des MoinesIowa

N Kaufm

IAIA Des Moines

Polk

Polk

19

19

153

153

0058

0059

Low

Low Se 18th And

POPULATI

ONHIGHEST OTHER

41.607741.5833

-93.5719-93.5838

NOxPM

Joel 

KaufmJoel 

IA Clive Polk 19 153 2510 Low

Scott, National ByProducts

9401 Indian

COOTHER POPULATIO 41.6027 -93.7477 PM

Kaufm

Joel 

Hills Drive,Clive 50325

N Kaufm

IA Polk 19 153 2520 Low OTHER POPULATIO

N

41.6647 -93.6141 PM Joel 

Kaufm

IA Davenport Scott 19 163 0014 Medium Scott County

Park

GENERAL/BA

POPULATION

41.6991 -90.5219 NOx Joel 

Kaufm
Essex, 

Baltimore Co.

IA Davenport Scott 19 163 0018 Medium 3029 N

Division St.Davenport

POPULATION

41.55 -90.6 PM Joel 

Kaufm

IA Davenport Scott 19 163 0019 Medium 300 Wellman

St. Davenport

POPULATION

SOURCE ORI

41.5177 -90.6186 PM Joel 

Kaufm

IA Story 19 169 2530 Low OTHER POPULATIO

N

42.0413 -93.6138 PM Joel 

Kaufm

IA Clarion Wright 19 197 0004 Low 2446 Quincy

Ave. Clarion

GENERAL/BA

POPULATION

42.6953 -93.6559 PM Joel 

Kaufm

MD Anne 

Arundel

Davidsonville FamilyRecreation Center

24 003 0014 High Queen Anne

And WaysonRoads

POPULATION

38.9025 -76.653 PM Joel 

Kaufm

MD Fort Meade

(U.S. Army)

Anne Arundel

24 003 0019 Low 9001 

'Y'Street,Ft.Meade

GENERAL/BA

POPULATION

39.1011 -76.7294 PM, NOx Joel 

Kaufm

MDMD

Glen BurnieRiviera 

Anne 

ArundelAnne 

2424

003003

10032002

HighLow

7409 Balto

And Annapolis Blvd8515 Jenkins

POPULATIONGENERAL/ POPULATIO

39.169539.1597

-76.6279-76.5116

PMPM

Joel 

Kaufm

Joel 

MD

BeachCockeysville

Arundel

Baltimore 24 005 1007 High

Rd RivieraBeach

Padonia E.S.

BAGENERAL/

NHIGHEST 39.4608 -76.6311 PM

Kaufm

Joel 

MD Essex Baltimore 24 005 3001 High

9834 Greenside Dr. Cockeysv

600 Dorsey

Avenue, 

BAHIGHEST CO

COMAX PRECUR

39.3108 -76.4744 PM, NOx

Kaufm

Joel 

Kaufm
MD Baltimore Baltimore SEPS 24 510 0008 Medium S E Police Population 39.28768 -76.547616 PM2.5 Joel 

Station 5600Eastern Ave.

Exposure (Baltimore, MD)

Kaufm

MD Cecil 24 015 0003 Low 4600 POPULATI 39.7011 -75.86 PM Joel 

MD Edgewood Harford 24 025 1001 High

Telegraph Road, Fairhill, CecilCo.Edgewood 

ONHIGHEST POPULATIO 39.41 -76.2966 PM

Kaufm

Joel 

Army ChemCenter, Waehli Road

CO N Kaufm

MD Rockville Montgomer

y

24 031 3001 Medium Lathrop E.

Smith Env.Ed Center ,

POPULATION

39.1144 -77.1069 PM Joel 

Kaufm

MD Beltsville Prince 

George's

24 033 0030 Medium

5110 Me

Howard 

University'S Beltsville Laborator

GENERAL/BA

HIGHEST CO

39.0552 -76.8783 NOx Joel 

Kaufm

MD Greater 

Upper Marlboro

Prince George's

24 033 8003 Medium P.G. Co.

Equestrian Cntr, 14900Pennsylv

POPULATION

38.8119 -76.7441 PM Joel 

Kaufm

MD Hagerstown Washington 24 043 0009 Low 18530 

Roxbury Road, Hagerstown

POPULATION

39.5655 -77.7219 PM Joel 

Kaufm

MD Baltimore Baltimore NEPS 24 510 0006 Medium N E Police

Sta, 1900Argonne Dr,Balto

Population Exposure (Baltimore, MD)

39.340556 -76.582222 PM2.5 Joel 

Kaufm

MD Baltimore Baltimore NWPS 24 510 0007 Medium N W Police

Station 5700Reistertown Rd.

Population Exposure (Baltimore, MD)

39.344444 -76.685278 PM2.5 Joel 

Kaufm
MN Hennepin 27 053 0957 Low HIGHEST 

CO

45.0211 -93.2819 NOx Joel 

Kaufm

MD Baltimore Baltimore 

(City)

24 510 0035 High Fmc Corp.

1701 EPatapsco 

Avenue

GENERAL/BA

HIGHEST CO

39.2327 -76.5797 PM Joel 

Kaufm

MD Baltimore Baltimore Oldtown 24 510 0040 Medium Old Town

Fire Station1100 HillenStreet

Population Exposure (Baltimore, MD)

39.298056 -76.604722 PM2.5 Joel 

Kaufm

MD Baltimore Baltimore 

(City)

24 510 0049 High POPULATI

ON

39.2616 -76.6375 PM Joel 

Kaufm

MN Blaine Anoka 27 003 1002 High Anoka 

County Airport 2289Co. Rd. J

GENERAL/BA

POPULATION

45.1397 -93.2076 NOx Joel 

Kaufm

MN Cloquet Carlton 27 017 7416 Low 175 

University Rd

GENERAL/BA

POPULATION

46.7052 -92.5236 NOx Joel 

Kaufm

MN Dakota 27 037 0020 High 12821 Pine

Bend Trail

POPULATION

SOURCE ORI

44.7653 -93.0324 NOx Joel 

Kaufm

MN Rosemount Dakota 27 037 0423 High 2142 120th

Street East

POPULATION

SOURCE ORI

44.775 -93.0627 NOx Joel 

Kaufm

MN Apple Valley Dakota 27 037 0470 High 225 Garden

View Drive

HIGHEST CO

OTHER 44.7407 -93.2372 PM Joel 

Kaufm

MN Dakota 27 037 6018 Low POPULATI

ON

REGIONAL T

44.75 -92.8877 PM Joel 

Kaufm
(Minneapoli

s-St. Paul,MN)

MN Minneapolis Hennepin Richfield 27 053 0961 High 7020 12th

Ave S,Minneapolis, 

POPULATION

44.8775 -93.2588 PM Joel 

Kaufm

MN Minneapolis Hennepin Phillips 27 053 0963 High

Mn

2727 10th St.

Mpls

POPULATION

WELFARE RE

44.9553 -93.2582 PM Joel 

Kaufm

MN Minneapolis Hennepin 27 053 1007 High 4646 

Humboldt Ave. N.

POPULATION

SOURCE ORI

45.0418 -93.2987 PM Joel 

Kaufm

MN St. Louis

Park

Hennepin St. Louis Park 27 053 2006 High 5005 

Minnetonka Blvd.

POPULATION

44.95 -93.3428 PM Joel 

Kaufm

MN Mille Lacs 27 095 3051 Low Hcr 67 Box

194

POPULATION

REGIONAL T

46.207 -93.7594 PM Joel 

Kaufm

MN Rochester Olmsted 27 109 5008 Medium 1801 9th Ave

S. E.Rochester, 

GENERAL/BA

HIGHEST CO

43.9969 -92.4503 PM Joel 

Kaufm

MNMN St. Paul

RamseyRamsey Red Rock Road

2727

123123

08640866

LowMedium

Mn 55904

1450 Red

Rock Road,

POPULATI

ONHighest Concentrati

44.991944.899379

-93.183-93.017155

NOxPM2.5, PM10

Joel 

Kaufm

Joel 

Kaufm

MN St. Paul Ramsey St Paul Health

Centre

27 123 0868 Medium

St. Paul, Mn

555 Cedar

Street

on (Minneapolis-St. Paul,MN)Population Exposure 

44.952442 -93.098475 PM2.5, PM10 Joel 

Kaufm
NJ Camden Camden 34 007 0003 Low Copewood 

E. Davis Sts;Trailer

HIGHEST CO

POPULATION

39.923 -75.0976 PM, NOx Joel 

Kaufm

MN St. Paul Ramsey Harding High School 27 123 0871 Medium 1540 East

6th Street

Population Exposure 

44.961451 -93.035894 PM2.5 Joel 

Kaufm

(Minneapolis-St. Paul,MN)

MN Ramsey 27 123 0872 Low POPULATI

ON

44.9311 -93.156 PM Joel 

Kaufm

MN Virginia Saint Louis 27 137 7001 Low City Hall

Roof

POPULATION

47.5233 -92.5363 PM Joel 

Kaufm

MN Duluth Saint Louis 27 137 7550 Low 1202 East

University Circle

POPULATION

46.8201 -92.0894 PM Joel 

Kaufm

MN Duluth Saint Louis 27 137 7551 Low 2424 W 5th

St

HIGHEST CO

POPULATION

46.7666 -92.133 PM Joel 

Kaufm

MN Shakopee Scott 27 139 0505 High 917 Dakota

St., Shakopee, Mn 55379

POPULATION

44.7914 -93.5125 PM Joel 

Kaufm

MN St. Cloud Stearns 27 145 3052 Low 1321 

Michigan Ave, St.Cloud Mn

POPULATION

45.5498 -94.1334 PM Joel 

Kaufm

MO Mercer 29 129 0001 Low

56304

OTHER SOURCE 

ORI

40.56 -93.4183 NOx Joel 

Kaufm

NJ Fort Lee Bergen Fort Lee Library 34 003 0003 High Fort Lee

Library,Center Avenue

POPULATION

40.8516 -73.9733 PM Joel 

Kaufm
NJ Chester Morris 34 027 3001 High Bldg.#1, Bell

Labs, OffRoute 513

GENERAL/BA

POPULATION

40.7876 -74.6763 PM Joel 

Kaufm

NJ Pennsauken 

(Pensauken)

Camden 34 007 1007 Low Pennsauken 

Twp; MorrisDelair Wtp

HIGHEST CO

POPULATION

39.9888 -75.0491 PM Joel 

Kaufm

NJ Newark Essex 34 013 0015 High Mary Willis

Cultural Ctr,18th Av,Newark

POPULATION

40.7319 -74.2052 PM Joel 

Kaufm

NJ Jersey City Hudson 34 017 1003 High 355 Newark

Ave,Consolidated FireHouse

HIGHEST CO

POPULATION

40.7254 -74.0522 PM Joel 

Kaufm

NJ Lawrence 

(Township Of)

Mercer 34 021 0005 Low Rider 

College;Lawrence Township

HIGHEST CO

MAX OZONE

40.283 -74.7426 NOx Joel 

Kaufm

NJ Trenton Mercer 34 021 0008 Low 120 

Academy Street, Trenton 

HIGHEST CO

POPULATION

40.2222 -74.7636 PM Joel 

Kaufm

NJ Mercer 34 021 8001 Low

Public Libr.

Washington 

Crossing State Park

POPULATION

REGIONAL T

40.3124 -74.8726 PM Joel 

Kaufm

NJ North 

Brunswick (Township Of)

Middlesex 34 023 0006 Medium Cook 

College, LogCabin Road

HIGHEST CO

POPULATION

40.4727 -74.4225 PM Joel 

Kaufm

NJ East 

Brunswick (Township Of)

Middlesex 34 023 0011 Medium R.U. Veg

Research Farm 3,Ryders Ln,

GENERAL/BA

POPULATION

40.4621 -74.4294 NOx Joel 

Kaufm

NJ Morristown Morris 34 027 0004 High

Newb

16 Early St,

Morristown

POPULATION

40.803 -74.4833 PM Joel 

Kaufm
Jersey)

NY New York Bronx 36 005 0110 High E 156th St

Bet DawsonAnd Kelly

GENERAL/BA

OTHER 40.8161 -73.902 PM, NOx Joel 

Kaufm

NJ Paterson Passaic 34 031 0005 High Health 

Department 176 Broadway

POPULATION

40.9186 -74.1677 PM Joel 

Kaufm

NJ Elizabeth Union 34 039 0004 High New Jersey

Turnpike Interchange 13

HIGHEST CO

POPULATION

40.6414 -74.2083 PM Joel 

Kaufm

NJ Elizabeth Union 34 039 0006 High Mitchell 

Building,600 North Broad

HIGHEST CO

POPULATION

40.673 -74.2136 PM Joel 

Kaufm

NJ Rahway Union 34 039 2003 High

StreetRahway FireDept, 1300Main Street

POPULATION

40.606 -74.2749 PM Joel 

Kaufm

NJNY

PhillipsburgNew York

Warren

Bronx Morrisania

34

36

041

005

0006

0080

High

Medium

Pburg 

Municipal Bldg, 675Corliss Ave

Morrisania 

Center, 1225

POPULATION

-

Population Exposure 

40.687240.83608

-75.1813-73.92021

PMPM2.5

Joel 

Kaufm

Joel 

Kaufm

NY New York Bronx NY Botanical

Gardens

36 005 0083 Medium

57 GerardAve.

200th Street

And 

(New York,NYNortheastern NewJersey)Population Exposure 

40.86586 -73.88075 PM2.5, SO2,

NO2, CO

Joel Kaufm

Southern Blvd

(New York,NYNortheastern New
NY New York New York 36 061 0056 High Ps 59, 288 E. 

57th Street,Manhattan

GENERAL/BA

HIGHEST CO

40.7591 -73.9665 PM, NOx Joel 

Kaufm

NY Dutchess 36 027 1004 Low OTHER POPULATIO

N

41.6948 -73.9144 PM Joel 

Kaufm

NY Kings 36 047 0052 Low HIGHEST 

CO

OTHER 40.6415 -74.0183 PM Joel 

Kaufm

NY Kings 36 047 0076 Low OTHER POPULATIO

N

40.6718 -73.9782 PM Joel 

Kaufm

NY New York Kings 36 047 0122 High Jhs 126 424

Leonard St

POPULATION

40.7198 -73.9478 PM Joel 

Kaufm

NY East 

Meadow

Nassau 36 059 0005 Low Eisenhower 

Park,Merrick Av&Old Country R

GENERAL/BA

HIGHEST CO

40.7432 -73.5854 NOx Joel 

Kaufm

NY Cedarhurst Nassau 36 059 0008 Low Lawrence 

High School,Arling

POPULATION

40.631 -73.7347 PM Joel 

Kaufm

NY Nassau 36 059 0012

ton Place

Low OTHER POPULATIO

N

40.789 -73.6364 PM Joel 

Kaufm

NY Nassau 36 059 0013 Low OTHER POPULATIO

N

40.7607 -73.4906 PM Joel 

Kaufm

NY New York 36 061 0010 High HIGHEST 

CO

OTHER 40.7394 -73.9861 PM Joel 

Kaufm
NY New York Richmond 36 085 0055 High Post Office,

364 PortRichmond Ave.

POPULATION

40.633 -74.1371 PM Joel 

Kaufm

NYNY

New YorkNew York

New YorkNew York JHS 45

3636

061061

00620079

HighMedium

Post Office,350 Canal StreetSchool Is 45,2351 1st

GENERAL/BA

HIGHEST CO

Population Exposure 

40.720540.79937

-74.004-73.93334

PMPM2.5

Joel 

Kaufm

Joel 

Kaufm

Avenue (New York,

NYNortheastern New

NY New York New York 36 061 0128 High Ps 19 185

1st Avenue

Jersey)POPULATION

40.73 -73.9844 PM Joel 

Kaufm

NY Newburgh Orange 36 071 0002 Low 55 Broadway GENERAL/

BA

POPULATION

41.4994 -74.0097 PM Joel 

Kaufm

NY Queens 36 081 0094 Low OTHER POPULATIO

N

40.7779 -73.8431 PM Joel 

Kaufm

NY Queens 36 081 0096 Low OTHER POPULATIO

N

40.7703 -73.8284 PM Joel 

Kaufm

NY Queens 36 081 0097 Low GENERAL/

BA

OTHER 40.7552 -73.7586 NOx Joel 

Kaufm

NY Queens 36 081 0098 Low GENERAL/

BA

SOURCE ORI

40.7842 -73.8475 NOx Joel 

Kaufm

NY New York Queens Queens College 2 36 081 0124 High 14439 

Gravett Road

GENERAL/BA

OTHER 40.7362 -73.8231 PM, NOx Joel 

Kaufm
Street 

NC Edgecomb

e

37 065 0003 Low EXTREME 

DO

GENERAL/BA

35.9533 -77.7858 PM Joel 

Kaufm

NY New York Richmond 36 085 0067 High Susan 

Wagner Hs,Brielle Ave.&

GENERAL/BA

POPULATION

40.5973 -74.1261 PM Joel 

Kaufm

NY Suffolk 36 103 0001 Low

Manor Rd,

POPULATI

ON

40.7458 -73.4202 PM Joel 

Kaufm

NY Mamaronec

k

Westchester

36 119 1002 High 5th Avenue

& Madison,Thruway Exit

POPULATION

40.93 -73.7692 PM Joel 

Kaufm

NC Burlington Alamance 37 001 0002 High

9

827 S

Graham &Hopedale Rd

EXTREME DO

POPULATION

36.089 -79.4078 PM Joel 

Kaufm

NC Black 

Mountain

Buncombe 37 021 0034 Medium 175 Bingham

Road Asheville Nc

POPULATION

35.6097 -82.3508 PM Joel 

Kaufm

NC Caswell 37 033 0001 Low 7074 Cherry

Grove Rd,

GENERAL/BA

POPULATION

36.307 -79.4674 PM Joel 

Kaufm

NC Chatham 37 037 0004 Medium

Reidsville

Rt 4 Box 62 GENERAL/

BA

POPULATION

35.7572 -79.1597 PM Joel 

Kaufm

NC Fayetteville Cumberlan

d

37 051 0009 Medium 4533 

Raeford Rd

POPULATION

35.0414 -78.9531 PM Joel 

Kaufm

NC Durham Durham 37 063 0001 Medium Health Dept,

300 E Main

HIGHEST CO

POPULATION

35.9919 -78.8963 PM Joel 

Kaufm
Candor 

NC Chapel Hill Orange 37 135 0007 Medium Mason Farm

Road AtColumbia

POPULATION

35.9019 -79.0566 PM Joel 

Kaufm

NC Rocky 

Mount

Edgecombe

37 065 0004 Medium 900 

Springfield 

GENERAL/BA

POPULATION

35.9335 -77.75 PM Joel 

Kaufm

NC WinstonSalem Forsyth North Forsyth H.S. 37 067 0024 Low

Road

North 

Forsyth HighSchool

POPULATION

36.1713 -80.2819 {shut down

Jan. '06}

Joel Kaufm

NC WinstonSalem Forsyth Clemmons 37 067 0030 High Fraternity Church Road Population Exposure 

(WinstonSalem, NC)

36.026 -80.342 PM2.5, Oz Joel 

Kaufm

NCNC Greensboro

Guilford

Guilford Mendenhall

37

37

081

081

0009

0013

Low

Low 205 

Wiloughby Blvd

POPULATI

ONPopulation Exposure (Greensboro, NC),

36.075836.109167

-79.7944-79.801111

PMPM2.5, PM10,

Oz

Joel 

KaufmJoel Kaufm

General/Background (Greensboro, NC)

NC Charlotte Mecklenbur

g

37 119 0041 Medium 1130 

Eastway 

OTHER POPULATIO

N

35.2402 -80.7855 NOx Joel 

Kaufm

NC Spruce Pine Mitchell 37 121 0001 Low

Drive

City Hall

Summit St

GENERAL/BA

POPULATION

35.9152 -82.0733 PM Joel 

Kaufm

NC Candor Montgomer

y

37 123 0001 Low 112 Perry

Drive, 

GENERAL/BA

POPULATION

35.26 -79.84 PM Joel 

Kaufm
ON T Kaufm

PA Cumberlan

d

42 041 0101 Low POPULATI

ON

40.2465 -77.1867 PM Joel 

Kaufm

NC Lumberton Robeson 37 155 0005 Low 1170 

Linkhaw 

GENERAL/BA

POPULATION

34.6425 -78.9902 PM Joel 

Kaufm

NC Raleigh Wake 37 183 0014

Road

Medium 3801 Spring

Forest Rd.

GENERAL/BA

MAX OZONE

35.8561 -78.5741 PM Joel 

Kaufm

NC Wake 37 183 0015 Low POPULATI

ON

35.79 -78.6197 PM Joel 

Kaufm

NC Boone Watauga 37 189 0003 Low 361 

Jefferson Road, Boone

EXTREME DO

GENERAL/BA

36.2219 -81.663 PM Joel 

Kaufm

NC Goldsboro Wayne 37 191 0005 Medium Dillard 

Middle 

POPULATION

35.3692 -77.9938 PM Joel 

Kaufm

School, Devereau St

Ohio DaytonPA

Montgomer

yAdams

3942

113001

00310001

HighLow EXTREME POPULATIO

39.759444

39.92

-84.144444

-77.31 PM, NOx

Antone

Zanob

Joel 

PA Berks 42 011 0009 Low

DOHIGHEST 

NOTHER 40.3202 -75.9266 NOx

Kaufm

Joel 

PA Bucks 42 017 0012 Low

CO

OTHER POPULATIO 40.1072 -74.8822 PM, NOx

Kaufm

Joel 

PA Chester 42 029 0100 Low

N

POPULATI REGIONAL 39.8344 -75.7686 PM

Kaufm

Joel 
a CO N Kaufm

PA Philadelphi

a

42 101 0020 Low POPULATI

ON

40.0022 -75.2202 PM Joel 

Kaufm

PA Dauphin 42 043 0401 Low HIGHEST OTHER 40.245 -76.8447 PM, NOx Joel 

PA Delaware 42 045 0002 Low

COHIGHEST OTHER 39.8355 -75.3725 NOx

KaufmJoel 

PA Lackawann 42 069 2006 Low

COHIGHEST OTHER 41.4427 -75.623 NOx

KaufmJoel 

PA

aLancaster 42 071 0007 Low

CO

HIGHEST OTHER 40.0466 -76.2833 PM, NOx

Kaufm

Joel 

PA Lehigh 42 077 0004 Low

CO

OTHER POPULATIO 40.6119 -75.4325 PM, NOx

Kaufm

Joel 

PA Luzerne 42 079 1101 Low OTHER

NPOPULATIO 41.2655 -75.8463 NOx

Kaufm

Joel 

PA Montgomer 42 091 0013 Low OTHER

NPOPULATIO 40.1122 -75.3091 PM, NOx

Kaufm

Joel 

PA

yNorthampto 42 095 0025 Low OTHER

NPOPULATIO 40.628 -75.3411 PM, NOx

Kaufm

Joel 

PA

nPerry 42 099 0301 Low GENERAL/

NPOPULATIO 40.4569 -77.1655 PM, NOx

Kaufm

Joel 

PA Philadelphi 42 101 0004 Low

BA NHIGHEST POPULATIO 40.0088 -75.0977 PM

Kaufm

Joel 
BA Kaufm

SC Richland 45 079 0007 Low GENERAL/

BA

MAX OZONE

34.0958 -80.9623 PM Joel 

Kaufm

PA Philadelphi 42 101 0024 Low POPULATI 40.0763 -75.0119 PM Joel 

PA

aPhiladelphi 42 101 0047 Low

ONHIGHEST POPULATIO 39.9447 -75.1661 PM

Kaufm

Joel 

PA

aPhiladelphi 42 101 0136 Low

CO

HIGHEST 

NPOPULATIO 39.9275 -75.2227 PM

Kaufm

Joel 

PA

aYork 42 133 0008 Low

COHIGHEST 

NOTHER 39.9652 -76.6994 PM, NOx

Kaufm

Joel 

SC Chesterfiel 45 025 0001 Low

COGENERAL/ POPULATIO 34.6171 -80.1987 PM

Kaufm

Joel 

SC

dFlorence 45 041 0002 Low

BA

OTHER

NPOPULATIO 34.1676 -79.8504 PM

Kaufm

Joel 

SC Greenville 45 045 0008 Low OTHER

N

POPULATIO 34.8404 -82.4029 PM

Kaufm

Joel 

SC Greenville 45 045 0009 Low GENERAL/

NOTHER 34.901 -82.313 PM

KaufmJoel 

SC Greenwood 45 047 0003 Low

BAOTHER POPULATIO 34.2145 -82.1731 PM

Kaufm

Joel 

SC Lexington 45 063 0008 Low GENERAL/

NOTHER 34.0528 -81.1549 PM

KaufmJoel 
University 

Ave

WI Dodge 55 027 0007 Low Mayville, 

Near N6705Madison Rd

GENERAL/BA

POPULATION

43.435 -88.5277 PM Joel 

Kaufm

SC Richland 45 079 0019 Low OTHER POPULATIO 33.9932 -81.0241 PM Joel 

N Kaufm

SC Spartanbur

g

45 083 0010 Low OTHER POPULATIO

N

34.9268 -82.0052 PM Joel 

Kaufm

Texas Port ArthurVA

Jefferson

Arlington

48

51

245

013

0022

0020

High

Low POPULATI

29.863889

38.8575

-94.317778

-77.0591 PM

Antone

Zanob

Joel 

VA Fairfax 51 059 0030 Low

ON

GENERAL/ HIGHEST 38.7727 -77.1055 PM

Kaufm

Joel 

VA Fairfax 51 059 1005 Low

BA CO

POPULATI 38.8375 -77.1632 PM

Kaufm

Joel 

VA Fairfax 51 059 5001 Low

ON

POPULATI 38.9319 -77.1988 PM

Kaufm

Joel 

VA Loudoun 51 107 1005 Low

ON

POPULATI 39.0244 -77.49 PM

Kaufm

Joel 

WI Dane 55 025 0025 Low

ONHIGHEST POPULATIO 43.0819 -89.3766 PM

Kaufm

Joel 

WI Madison Dane 55 025 0047 Low City Well #6,

2557 

COHIGHEST CO

NPOPULATION

43.0733 -89.4358 PM

Kaufm

Joel 

Kaufm
ON Kaufm

WI Milwaukee 55 079 0051 Low POPULATI

ON

SOURCE ORI

43.0344 -88.0431 PM Joel 

Kaufm

WI Douglas 55 031 0025 Low HIGHEST 46.7302 -92.0797 PM Joel 

WI Grant 55 043 0009 Low 128 Hwy 61,

COPOPULATI REGIONAL 42.6921 -90.6863 PM

Kaufm

Joel 

WI Jefferson 55 055 0008 Low

Potosi Township

ON

HIGHEST 

T

43.1838 -88.9941 PM

Kaufm

Joel 

WI Pleasant Kenosha 55 059 0019 High Chiwaukee 

COGENERAL/ HIGHEST 42.5047 -87.8093 PM

Kaufm

Joel 

WI

PrairieMilwaukee Milwaukee 55 079 0010 Medium

Prairie, 11838 FirstCourt

Health 

Center, 1337

BAOTHER

COPOPULATION

43.0166 -87.9333 PM

Kaufm

Joel 

Kaufm

WI Milwaukee Milwaukee 55 079 0026 Medium

So 16th St

Dnr Ser

Hdqrts, 2300N M. L. KingJr Dr

HIGHEST CO

MAX PRECUR

43.0611 -87.9125 PM, NOx Joel 

Kaufm

WI Milwaukee Milwaukee 55 079 0041 Medium Uwm North

Campus, 2114 EKenwood 

HIGHEST CO

MAX PRECUR

43.0752 -87.8844 NOx Joel 

Kaufm

WI Milwaukee Milwaukee 55 079 0043

Blvd

Medium Virginia Fire

Station, 100

POPULATION

SOURCE ORI

43.0264 -87.9111 PM Joel 

Kaufm

WI Milwaukee 55 079 0050

W Virginia St

Low POPULATI 43.0977 -88.0077 PM Joel 
ON Kaufm

WI Wood 55 141 0016 Low HIGHEST 

CO

POPULATION

44.3825 -89.8191 PM Joel 

Kaufm

WI Milwaukee Milwaukee 55 079 0059 Medium Federal GENERAL/ HIGHEST 42.955 -87.9341 PM Joel 

WI Milwaukee Milwaukee 55 079 0099 Medium

Aviation Adm, 4942 S16th St

Milw Fire

BAHIGHEST 

COPOPULATIO 43.0397 -87.9205 PM

Kaufm

Joel 

WI Grafton Ozaukee 55 089 0008 Medium

Dept Hq, 711W Wells St

Grafton, 

Hwy32 And

COHIGHEST CO

NPOPULATION

43.343 -87.9208 PM

Kaufm

Joel 

Kaufm

WI Ozaukee 55 089 0009 Medium

I43

Harrington 

Beach State

GENERAL/BA

HIGHEST CO

43.498 -87.81 PM Joel 

Kaufm

WI Somerset St. Croix 55 109 1002 High

Park, 531Hwy D

Hwy 64,

Somerset 

POPULATION

REGIONAL T

45.1244 -92.6625 PM Joel 

Kaufm

WI Sauk 55 111 0007 Low

Town Hall

Devils Lake

State Park,

GENERAL/BA

OTHER 43.4355 -89.6802 PM Joel 

Kaufm

WI Taylor 55 119 8001 Low

E12886 Tower Rd

1 Mi E. GENERAL/ POPULATIO 45.2038 -90.6 PM Joel 

WI Waukesha Waukesha 55 133 0027

Perkinstown On Sr.M

Medium 1310 

BA NHIGHEST OTHER 43.0202 -88.215 PM

KaufmJoel 

WI Waukesha 55 133 0034

Cleveland Ave

Low

CO

POPULATI 43.0072 -88.2297 PM

Kaufm

Joel 
United States Office of Air Quality Planning and Standards Publicatio

Environmental Protection Health and Environmental Impacts Division Agency Research Triangle Park, NC