AMBIENT AIR MONITORING NETWORK ASSESSMENT 

GUIDANCE 

Analytical Techniques for Technical Assessments of Ambient Air 

Monitoring Networks 

EPA-454/D-07-001 

February 2007 

AMBIENT AIR MONITORING NETWORK ASSESSMENT 

GUIDANCE 

Analytical Techniques for Technical Assessments of 

Ambient Air Monitoring Networks 

Prepared by: 

Sean M. Raffuse 

Dana C. Sullivan 

Michael C. McCarthy 

Bryan M. Penfold 

Hilary R. Hafner 

Sonoma Technology, Inc. 

Petaluma, California 

Prepared for: 

Kevin A. Cavender, Work Assignment Manager 

Ambient Air Monitoring Group 

Contract No. EP-D-05-004 

Work Assignment No. 2-12 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, North Carolina 

iii

ACKNOWLEDGMENTS 

The authors thank Kevin Cavender, the work assignment manager, for his assistance with 

this project. We also acknowledge those involved with the National Analysis (Schmidt, 2001) 

and the preliminary regional assessments, particularly those performed by U.S. Environmental 

Protection Agency Regions 3 and 5, upon which this document draws. 

v

DISCLAIMER 

This document provides guidance to EPA Regional, State, and Tribal air quality 

management authorities and the general public, on how EPA intends to exercise its discretion in 

implementing Clean Air Act provisions and EPA regulations, concerning ambient air 

monitoring. The guidance is designed to implement national policy on these issues. Section 110 

of the Clean Air Act (42 U.S.C. § 7410) and implementing regulations at 40 CFR part 58 contain 

legally binding requirements. This document does not substitute for those provisions or 

regulations, nor is it a regulation itself. Thus, it does not impose binding, enforceable 

requirements on any party, nor does it assure that EPA may approve all instances of its 

application, and thus the guidance may not apply to a particular situation based upon the 

circumstances. EPA and State decision makers retain the discretion to adopt approaches on a 

case-by-case basis that differ from this guidance where appropriate. Any decisions by EPA 

regarding a particular State implementation plan (SIP) demonstration will only be made based on 

the statute and regulations, and will only be made following notice and opportunity for public 

review and comment. Therefore, interested parties are free to raise questions and objections 

about the appropriateness of the application of this guidance to a particular situation; EPA will, 

and States should, consider whether or not the recommendations in this guidance are appropriate 

in that situation. This guidance is a living document and may be revised periodically without 

public notice. EPA welcomes public comments on this document at any time and will consider 

those comments in any future revision of this guidance document. 

 Readers of this document are cautioned not to regard statements recommending the use 

of certain procedures or defaults as either precluding other procedures or information or 

providing guarantees that using these procedures or defaults will result in actions that are fully 

approvable. As noted above, EPA cannot assure that actions based upon this guidance will be 

fully approvable in all instances, and all final actions may only be taken following notice and 

opportunity for public comment. 
vi
vii

FOREWORD 

This document represents guidance for the assessment of technical aspects of ambient air 

monitoring networks. It is designed to be flexible and expandable with additional types of 

analyses and examples as techniques are improved, enhanced, and more broadly applied. Its 

intended audience includes EPA Regional, state, local, and tribal air quality planning agencies. 

Depending on their unique situations, users of this guidance may select one or more analyses, or 

they may creatively modify one of the recommended analyses to facilitate a monitoring network 

assessment. 

The contents of this document are summarized briefly in the following paragraphs: 

• Section 1 summarizes the context of network assessments in general and this specific 

document, including background and key issues. Section 1 provides an overview of the 

requirements for network assessment contained in 40 CFR Part 58, and provides an 

overview of the network assessment process. 

• Section 2 expands on the procedures for network assessments. It introduces 

consideration of the purposes of a monitoring network—i.e., a network’s mission. The 

purposes provide a basis for performing a network assessment. They are the benchmarks 

against which the strengths and weaknesses of the network are measured. Section 2 

continues with specific details for technical approaches to network assessments, 

including three general categories of analyses: site-by-site, bottom-up, and network 

optimization. 

• Section 3 expands on the technical approaches introduced in Section 2. It includes a 

selection of two-page illustrations of analyses for network assessments. 

• Section 4 provides information on the regulatory requirements for the discontinuation of 

a monitor used in National Ambient Air Quality Standards (NAAQS) compliance. The 

section also provides procedures that can be used to determine if a monitor meets the 

requirements. 

• Section 5 concludes this guidance document with a summary and recommendations for 

further development of network assessment guidance with an emphasis on expected 

results and resource requirements. More detailed descriptions of the techniques, and 

more examples, could be added to future versions of this document as techniques are 

refined and more broadly applied. 

• Section 6 lists the references cited in this guidance document. 

• Appendix A discusses project-level example applications of the technical approaches 

discussed in Sections 2 and 3.

ix

TABLE OF CONTENTS 

Section Page

ACKNOWLEDGMENTS ............................................................................................................. iii 

DISCLAIMER .................................................................................................................................v 

FOREWORD ................................................................................................................................ vii 

LIST OF FIGURES ....................................................................................................................... xi 

LIST OF TABLES......................................................................................................................... xi 

1. INTRODUCTION.............................................................................................................. 1-1 

1.1 Background and Key Issues...................................................................................... 1-2 

1.2 Overview of Monitoring Network Technical Assessments...................................... 1-2 

2. APPROACH TO MONITORING NETWORK TECHNICAL ASSESSMENTS............ 2-1 

2.1 Identify Monitoring Needs ....................................................................................... 2-1 

2.2 Methods for Technical Assessment.......................................................................... 2-3 

2.2.1 Overview ...................................................................................................... 2-3 

2.2.2 Site-By-Site Analyses................................................................................... 2-4 

2.2.3 Bottom-Up Analyses .................................................................................... 2-5 

2.2.4 Network Optimization Analyses .................................................................. 2-6 

3. METHOD SUMMARY SHEETS ..................................................................................... 3-1 

4. REMOVING A NAAQS COMPLIANCE MONITOR..................................................... 4-1 

4.1 Attainment reached................................................................................................... 4-1 

4.2 Consistently low concentrations relative to other monitors ..................................... 4-4

4.3 Monitors not measuring violations of NAAQS........................................................ 4-4 

4.4 Monitors with siting issues ....................................................................................... 4-4 

4.5 Upwind monitors ...................................................................................................... 4-5 

4.6 Logistical problems beyond agency control............................................................. 4-5 

5. RECOMMENDATIONS ................................................................................................... 5-1 

6. REFERENCES................................................................................................................... 6-1 

APPENDIX A: EXAMPLE APPLICATIONS ......................................................................... A-1 

xi

LIST OF FIGURES 

Figure Page

3-1. Summary sheet front page example................................................................................. 3-1 

LIST OF TABLES 

Table Page

1-1. Descriptions and examples of steps involved in performing network assessments......... 1-3 

2-1. Typical purposes for ambient air monitoring networks................................................... 2-1 

2-2. Site-by-site analysis techniques ....................................................................................... 2-4 

2-3. Bottom-up analysis techniques ........................................................................................ 2-6 

2-4. Network optimization analysis techniques ...................................................................... 2-7 

4-1. National Ambient Air Quality Standards......................................................................... 4-2 

4-2. Student’s t values ............................................................................................................. 4-3 

4-3. Example computations for four CO monitoring sites...................................................... 4-3 

1-1

1. INTRODUCTION 

The U.S. Environmental Protection Agency (EPA) finalized an amendment to the 

ambient air monitoring regulations on October 17, 2006. As part of this amendment, the EPA 

added the following requirement for state, or where applicable local, monitoring agencies to 

conduct a network assessments once every five years [40 CFR 58.10(e)]. 

“(e) The State, or where applicable local, agency shall perform and submit to the EPA 

Regional Administrator an assessment of the air quality surveillance system every 5 years to 

determine, at a minimum, if the network meets the monitoring objectives defined in appendix D 

to this part, whether new sites are needed, whether existing sites are no longer needed and can 

be terminated, and whether new technologies are appropriate for incorporation into the ambient 

air monitoring network. The network assessment must consider the ability of existing and 

proposed sites to support air quality characterization for areas with relatively high populations 

of susceptible individuals (e.g., children with asthma), and, for any sites that are being proposed 

for discontinuance, the effect on data users other than the agency itself, such as nearby States 

and Tribes or health effects studies. For PM2.5, the assessment also must identify needed 

changes to population-oriented sites. The State, or where applicable local, agency must submit a 

copy of this 5-year assessment, along with a revised annual network plan, to the Regional 

Administrator. The first assessment is due July 1, 2010.” 

This requirement is an outcome of implementing the National Ambient Air Monitoring 

Strategy (NAAMS, the most recent version is dated December 2005, U.S. Environmental 

Protection Agency, 2005). The purpose of the NAAMS is to optimize U.S. air monitoring 

networks to achieve, with limited resources, the best possible scientific value and protection of 

public and environmental health and welfare. 

A network assessment includes (1) re-evaluation of the objectives and budget for air 

monitoring, (2) evaluation of a network’s effectiveness and efficiency relative to its objectives 

and costs, and (3) development of recommendations for network reconfigurations and 

improvements. EPA expects that a multi-level network assessment will be conducted every five 

years (U.S. Environmental Protection Agency, 2005). Initial network assessments for the 

NAAMS were led by EPA and its 10 regional offices in 2001 through 2004 (U.S. Environmental 

Protection Agency, 2003b). This initial assessment, as well as peer-reviews of the NAAMS by 

subcommittees of the EPA Clean Air Scientific Advisory Committee (Hopke, 2003),(Henderson, 

2005), produced the recommendation that guidance for regional-scale network assessments be 

established. 

The NAAMS (U.S. Environmental Protection Agency, 2005), (U.S. Environmental 

Protection Agency, 2005), (Clean Air Scientific Advisory Committee and National Ambient Air 

Monitoring Strategy Subcommittee, 2003) and documentation of the initial national- and 

regional-scale network assessments provide a valuable context and a summary of the key 

technical issues for network assessment guidelines. This document builds on the lessons learned 
1-2

in the NAAMS and focuses on providing guidance on analytical techniques that can be used for 

regional-scale assessments. 

1.1 BACKGROUND AND KEY ISSUES 

 Ambient air monitoring objectives have shifted over time—a situation which has 

induced air quality agencies to re-evaluate and reconfigure monitoring networks. A variety of 

factors contribute to these shifting monitoring objectives: 

• Air quality has changed—for the better in most geographic areas—since the adoption of 

the federal Clean Air Act and National Ambient Air Quality Standards (NAAQS). For 

example, the problems of high ambient concentrations of lead and carbon monoxide have 

largely been solved. 

• Populations and behaviors have changed. For example, the U.S. population has (on 

average) grown, aged, and shifted toward urban and suburban areas over the past four 

decades. In addition, rates of vehicle ownership and annual miles driven have grown. 

• New air quality objectives have been established, including rules to reduce air toxics, fine 

particulate matter (PM2.5),1

 and regional haze. 

• The understanding of air quality issues and the capability to monitor air quality have both 

improved. Together, the enhanced understanding and capabilities can be used to design 

more effective air monitoring networks. 

As a result of these changes, air monitoring networks may have unnecessary or redundant 

monitors or ineffective and inefficient monitoring locations for some pollutants, while other 

regions or pollutants suffer from a lack of monitors. Air monitoring agencies should, therefore, 

refocus monitoring resources on pollutants that are new or persistent challenges, such as PM2.5, 

air toxics, and ground-level ozone and precursors, and should deemphasize pollutants that are 

steadily becoming less problematic and better understood, such as lead and carbon monoxide 

(CO). In addition, monitoring agencies need to adjust networks to protect today’s population and 

environment, while maintaining the ability to understand long-term historical air quality trends. 

Moreover, monitoring networks can take advantage of the benefits of new air monitoring 

technologies and improved scientific understanding of air quality issues. Existing monitoring 

networks should be designed to address multiple, interrelated air quality issues and to better 

operate in conjunction with other types of air quality assessments (e.g., photochemical modeling, 

emission inventory assessments). Reconfiguring air monitoring networks can enhance their 

value to stakeholders, scientists, and the general public. 

1.2 OVERVIEW OF MONITORING NETWORK TECHNICAL ASSESSMENTS 

Analytical techniques to assess the technical aspects of monitoring networks fit within the 

overall framework of regional network assessments discussed in the most recent version of the 

NAAMS (U.S. Environmental Protection Agency, 2005). The NAAMS briefly describes the 

 

1

 Particulate matter of less than 2.5 microns aerodynamic diameter. 
1-3

stepwise procedure for network assessments shown in Table 1-1. This document focuses on 

Steps 3 and 4: statistical analyses and objective situational analyses. 

In some cases, network assessments can be handled simply by answering one or more 

straightforward questions. In others, detailed analytical techniques, such as those discussed in 

Section 2.2 and Section 3, are necessary. A thorough technical assessment will help inform 

decisions about reconfiguring a network. These decisions might include eliminating redundant 

monitors, reducing or expanding the monitoring season, moving monitors to better locations, 

switching a site to different technology (e.g., finer temporal resolution), adding monitors to the 

network, or switching a site to a different pollutant. In practice, a combination of several types 

of analyses might provide the most useful information. Network assessment can be performed at 

many levels (national, regional, local); however, the next level down may need to reassess the 

analyses to ensure the correct decision, given local conditions. 

Table 1-1. Descriptions and examples of steps involved in performing network 

assessments. 

Step Description Examples 

1 Prepare or update a regional description, 

discussing important features that should be 

considered for network design 

Topography, climate, population, 

demographic trends, major emissions 

sources, and current air quality conditions 

2 Prepare or update a network history that 

explains the development of the air 

monitoring network over time and the 

motivations for network alterations, such as 

shifting needs or resources. 

Historical network specifications (e.g., 

number and locations of monitors by 

pollutant and by year in graphical or 

tabular format); history of individual 

monitoring sites 

3 Perform statistical analyses of available 

monitoring data. These analyses can be used 

to identify potential redundancies or to 

determine the adequacy of existing 

monitoring sites. 

Site correlations, comparisons to the 

NAAQS, trend analysis, spatial analysis, 

and factor analysis 

4 Perform situational analyses, which may be 

objective or subjective. These analyses 

consider the network and individual sites in 

more detail, taking into account research, 

policy, and resource needs. 

Risk of future NAAQS exceedances, 

demographic shifts, requirements of 

existing state implementation plans (SIP) 

or maintenance plans, density or 

sparseness of existing networks, scientific 

research or public health needs, and other 

circumstances (such as political factors) 

5 Suggest changes to the monitoring network 

on the basis of statistical and situational 

analyses and specifically targeted to the 

prioritized objectives and budget of the air 

monitoring program. 

Reduction of number of sites for a 

selected pollutant, enhanced leveraging 

with other networks, and addition of new 

measurements at sites to enhance 

usefulness of data 

6 Acquire the input of state and local agencies 

or stakeholders and revise recommendations 

as appropriate 
1-4

 
2-1

2. APPROACH TO MONITORING NETWORK TECHNICAL ASSESSMENTS 

This section provides guidance to the user for identifying monitoring needs and 

introduces network assessment analyses. 

2.1 IDENTIFY MONITORING NEEDS 

Before beginning a network assessment, the purposes of the network must be reviewed 

and prioritized. Networks are likely to be used to meet a variety of purposes, such as monitoring 

compliance with the NAAQS, public reporting of the Air Quality Index (AQI), assessment of 

population exposure to pollutants, assessment of pollutant transport, monitoring of specific 

emissions sources, monitoring of background conditions, evaluating models, and possibly others. 

These purposes may be prioritized as primary or secondary and individual monitors within a 

network may serve different purposes. Each analytical technique selected to support a network 

assessment must be chosen in view of the purposes of the overall network and its individual 

monitoring sites. In addition, the resources invested in each analysis should be proportional to 

the priority of the purposes that are being evaluated. Table 2-1 briefly lists some typical 

purposes for monitoring networks, although this list is neither comprehensive nor universally 

applicable to all pollutants. 

Network assessments quantifiably measure the successes and shortcomings of monitoring 

networks’ capabilities to meet their monitoring purposes. Therefore, clearly defined monitoring 

purposes are the basis for the technical assessment of a monitoring network. Once the purposes 

are defined, appropriate statistical or situational analyses may be considered and selected to 

evaluate each. 

Table 2-1. Typical purposes for ambient air monitoring networks. 

Page 1 of 3 

Purpose Examples Comments 

Meet national requirements Monitors may be sited to address 

NAAQS compliance or may be 

mandated by prior regulations or SIP 

provisions. 

Establish regulatory 

compliance 

Meet state and local 

regulations 

States, or local air districts, may have 

air quality regulations that are more 

stringent than federal requirements. 

Air quality model evaluation Monitors near modeling domain 

boundaries are useful for defining 

boundary conditions. Monitors 

throughout a domain assist model 

application and evaluation. 

Develop scientific 

understanding of air 

quality by supporting 

other types of 

assessments or analyses 

Emission reduction evaluation 

or emission inventory 

evaluation 

Urban core and maximum emission 

area monitors can be helpful for 

evaluating inventories and tracking 

emissions. 
2-2

Table 2-1. Typical purposes for ambient air monitoring networks. 

Page 2 of 3 

Purpose Examples Comments 

Source apportionment Monitors collecting data on many 

species (e.g., speciated PM2.5) and at 

fairly high time resolution (1-in-3-day 

or better) are useful for source 

apportionment analyses. 

Develop scientific 

understanding of air 

quality by supporting 

other types of 

assessments or analyses 

(continued) Temporal variability Sub-daily (e.g., 1-hr, 3-hr) data can 

be used to track diurnal patterns. 

Trend tracking Monitors with long histories are 

valuable for understanding and 

tracking long-term trends. 

Understand historical 

trends in air quality 

Historical consistency Monitoring sites whose sampling 

methods have not been changed help 

maintain consistency for annual 

comparisons. 

Monitor the air quality 

impacts of an emissions 

source 

Monitors located close to specific 

source hot spots are useful for 

tracking emissions from a particular 

source and developing emission 

reduction strategies or tracking 

changes due to controls. 

Monitor the area of maximum 

precursor emissions 

For secondary pollutants such as 

ozone, monitors located in areas of 

maximum precursor emissions are 

useful for modeling and control 

strategy design. 

Monitor the area of maximum 

pollutant concentration 

Monitors located downwind of 

maximum emissions. 

Monitor the background 

concentration 

Properly sited background monitors 

routinely measure the lowest 

expected values in the region. These 

monitors are used to asses regional 

vs. local contributions. 

Characterize specific 

geographic locations or 

emissions sources 

Monitor surrogate pollutants Some measurements are useful as 

surrogates for other pollutants that are 

not widely monitored. For example, 

CO monitors can be used as 

surrogates for wood smoke (Park et 

al., 2005). 
2-3

Table 2-1. Typical purposes for ambient air monitoring networks. 

Page 3 of 3 

Purpose Examples Comments 

Transport/border 

characterization 

Sites located near political boundaries 

or between urban or industrial areas 

are useful for characterizing transport 

of pollutants between jurisdictions. 

Interpolation and 

understanding pollutant 

gradients 

High monitor density improves 

interpolation maps such as those used 

in AIRNow (U.S. Environmental 

Protection Agency, 2003a). Monitors 

near the urban boundary are 

particularly useful for constraining 

the interpolation of high 

concentrations. 

Accountability/Performance 

measurement 

Monitoring data is used to measure 

the effects of air pollution control 

programs and strategies. Monitors in 

impacted areas are most useful for 

assessing the effectiveness of 

controls. 

Track the spatial 

distribution of air 

pollutants 

Forecasting assistance Upwind monitors are useful for air 

quality forecasting. For forecasting 

ozone, NOx measurements are 

helpful. For PM2.5, continuous 

monitors are very valuable. 

Environmental justice Monitors located in areas that have 

large low income and/or minority 

populations may be of particular 

value for assessing environmental 

justice issues. 

Evaluate population 

exposures to air 

pollutants 

Public reporting of the AQI Monitors located where people live, 

work, and play are important for 

addressing exposure and protecting 

public health. 

2.2 METHODS FOR TECHNICAL ASSESSMENT 

2.2.1 Overview 

In this document, techniques for assessing technical qualities of monitoring networks are 

grouped into three broad categories: site-by-site, bottom-up, and network optimization. Site-bysite comparisons rank individual monitors according to specific monitoring purposes; bottom-up 

analyses examine data other than ambient concentrations to assess optimal placement of 

monitors to meet monitoring purposes; and network optimization analyses evaluate proposed 
2-4

network design scenarios. Within these broad categories, specific techniques are rated by their 

complexity on the following scale. 

* Minimal special skills needed; quick 

** May require common tools, readily available data, and/or basic analysis skills; quick 

*** Requires analysis skills; moderate investment of time 

**** Significant analytical skills, specialized tools; time-intensive or iterative 

2.2.2 Site-By-Site Analyses 

Site-by-site analyses are those that assign a ranking to individual monitors based on a 

particular metric. These analyses are good for assessing which monitors might be candidates for 

modification or removal. Site-by-site analyses do not reveal the most optimized network or how 

good a network is as a whole. In general, the metrics at each monitor are independent of the 

other monitors in the network. 

Several steps are involved in site-by-site analysis: 

1. Determine which monitoring purposes are most important 

2. Assess the history of the monitor (including original purposes) 

3. Select a list of site-by-site analysis metrics based on purposes and available resources 

4. Weight metrics based on importance of purpose 

5. Score monitors for each metric 

6. Sum scores and rank monitors 

7. Examine lowest ranking monitors for possible resource reallocation 

The low-ranking monitors should be examined carefully on a case-by-case basis. There 

may be regulatory or political reasons to retain a specific monitor. Also, the site could be made 

potentially more useful by monitoring a different pollutant or using a different technology. 

Table 2-2 lists specific site-by-site analysis techniques, which are summarized in greater detail 

in Section 3. 

Table 2-2. Site-by-site analysis techniques. 

Page 1 of 2 

Technique Complexity 

(Section 2.1.1) Objectives Assessed (See Table 2-1) Summary Page 

Number of other 

parameters monitored 

at the site 

* Overall site value 

Model evaluation 

Source apportionment 

3-7 

Trends impact * to ** Trend tracking 

Historical consistency 

Emission reduction evaluation 

3-9 

Measured 

concentrations 

** Maximum concentration location 

Model evaluation 

Regulatory compliance 

3-11 
2-5

Population exposure 

Deviation from 

NAAQS 

** Regulatory compliance 

Forecasting assistance 

3-13 

Area served ** Spatial coverage 

Interpolation 

Background concentration 

3-15 

Table 2-2. Site-by-site analysis techniques. 

Page 2 of 2 

Technique Complexity 

(Section 2.1.1) Objectives Assessed (See Table 2-1) Summary Page 

Monitor-to-monitor 

correlation 

** to *** Model evaluation 

Spatial coverage 

Interpolation 

3-17 

Population served *** Population exposure 

Environmental justice 

3-23 

Principal component 

analysis 

*** Background concentration 

Forecasting assistance 

3-25 

Removal bias *** Regulatory compliance 

Model evaluation 

Spatial coverage 

Background concentration 

Interpolation 

3-27 

2.2.3 Bottom-Up Analyses 

Bottom-up methods examine the phenomena that are thought to cause high pollutant 

concentrations and/or population exposure, such as emissions, meteorology, and population 

density. For example, emission inventory data can be used to determine the areas of maximum 

expected concentrations of pollutants directly emitted (i.e., primary emissions). Emission 

inventory data are less useful to understand pollutants formed in the atmosphere (i.e., secondarily 

formed pollutants). Multiple data sets can be combined using spatial analysis techniques to 

determine optimum site locations for various objectives. Those optimum locations can then be 

compared to the current network. In general, bottom-up analyses indicate where monitors are 

best located based on specific objectives and expected pollutant behavior. However, bottom-up 

techniques rely on a thorough understanding of the phenomena that cause air quality problems. 

The most sophisticated bottom-up analysis techniques are complex and require significant 

resources (time, data, tools, and analytical skill). Table 2-3 lists the specific bottom-up analysis 

techniques detailed in Section 3. Site-by-site and bottom-up analyses are best performed in 

combination. Site-by-site analyses typically identify network redundancies while bottom-up 

analyses identify network “holes” or deficiencies. 
2-6

Table 2-3. Bottom-up analysis techniques. 

Technique Complexity 

(Section 2.1.1) Objectives Assessed (Table 2-1) Summary Page 

Emission Inventory ** to   Emission reduction evaluation 

Maximum precursor location 

3-19 

Population density **  Population exposure 

Environmental justice 

Not included 

Population change *** Population exposure 

Environmental justice 

Maximum precursor location 

3-21 

Suitability modeling   Population exposure 

Environmental justice 

Source-oriented 

Model evaluation 

Maximum concentration location 

Background concentration 

Transport/border characterization 

3-31 

Photochemical 

modeling 

** ** Maximum concentration location 

Source-oriented 

Transport/border characterization 

Population exposure 

Background concentration 

Not included 

2.2.4 Network Optimization Analyses 

Network optimization techniques are a holistic approach to examining an air monitoring 

network. These techniques typically assign scores to different network scenarios; alternative 

network designs can be compared with the current (base-case) design. 

An example of a network optimization analysis is the EPA Region 3 ozone network 

reassessment (Cimorelli et al., 2003). Region 3 utilized an iterative 10-step process: 

1. Select the set of scenarios (i.e., different hypothetical network designs) to be ranked 

2. Define decision criteria for scoring each network design 

3. Gather the data necessary to calculate scores for the decision criteria 

4. Index decision criteria to a common scale 

5. Weight the criteria based on relative importance 

6. Produce initial results (ranking of scenarios) 

7. Iterate – adjust scenarios, decision criteria, and criteria weighting as new information and 

understanding are developed 

8. Obtain feedback from stakeholder deliberation 

9. Finalize network optimization scenario results 

10. Recommend changes 
2-7

The formal analytical process used by Region 3 is called Multi-Criteria Integrated 

Resource Assessment (MIRA) (Cimorelli et al., 2003; Stahl et al., 2002) Forty metrics were 

used as decision criteria in the analysis. These metrics were arranged hierarchically with four 

top-level criteria: air quality, personnel impact, costs, and trends impact. For assessing the air 

quality criteria, Region 3 developed a base-case ozone concentration grid using photochemical 

modeling results. 

Many of the metrics used by Region 3 in their assessment are similar to the analyses 

described as “site-by-site” analyses in this document. When different network scenarios are 

considered, the individual monitor scores for a particular analysis can be summed to provide a 

total score for the entire network. The total score can be compared to other network designs. 

Table 2-4 lists some techniques for network optimization. Further details are provided in 

Section 3. 

Table 2-4. Network optimization analysis techniques. 

Technique Complexity 

(Section 2.1.1) Objectives Assessed (Table 2-1) Summary Page 

Monitor-to-monitor 

correlation 

** to *** Model evaluation 

Spatial coverage 

Interpolation 

3-17 

Principal Component 

Analysis 

*** Background concentration 

Forecasting assistance 

3-25 

Removal bias *** Regulatory compliance 

Model evaluation 

Spatial coverage 

Background concentration 

Interpolation 

3-27 

Positive matrix 

factorization 

**** Source apportionment 

Emission inventory evaluation 

3-29 

3-1

3. METHOD SUMMARY SHEETS 

The following pages represent summary sheets for individual analysis techniques. The 

summaries are designed to provide the vital statistics for the techniques at a glance and to help 

the analyst narrow down the list of possible analyses to perform based on their available 

resources and objectives. These summaries cover a range of analysis techniques that can be 

applied to network assessment; they can be expanded and other summary sheets can be prepared 

as examples become available. A brief introduction to each analysis technique that is covered in 

a summary sheet follows. 

Number of Parameters Monitored

 Sites are ranked by the number of parameters (or instruments) that are collected at a 

particular site. Air quality monitoring sites hosting monitors collocated with other measurement 

instruments are likely more valuable than sites at which fewer parameters are measured. In 

addition, the operating costs can be leveraged among several instruments at these sites. This 

analysis is performed by simply counting the number of other parameters that are measured at a 

physical site. Sites at which many parameters are measured are ranked highest. 

Trend Impacts 

 Monitors that have a long historical record are valuable for tracking trends. In this 

analysis, monitors are ranked based on the duration of their continuous measurement records. 

The analysis can be as simple as ranking the available monitors based on the length of the 

continuous sampling record. The most important monitors are those with the longest continuous 

trend record. 

Measured Concentrations 

 Individual monitors are ranked based on the concentration of pollutants they measure. 

Monitors that measure high concentrations or design values are ranked higher than monitors that 

measure low concentrations. Results can be used to determine which monitors are less useful in 

meeting the selected objective. The analysis is relatively straightforward, requiring only the site 

design values. The greater the design value, the higher the site rank. If more than one standard 

exists for a pollutant (e.g., annual and 24-hr average), monitors can be scored for each standard. 

Deviation from NAAQS 

 Sites measuring concentrations (design values) that are very close to the NAAQS 

exceedance threshold are ranked highest in this analysis. These sites may be considered more 

valuable for NAAQS compliance evaluation. Sites measuring concentrations well above or 

below the threshold do not provide as much information in terms of NAAQS compliance. This 

technique contrasts the difference between the standard and actual measurements or design 

values. It is a simple way to assess a site’s value for evaluating compliance. If a pollutant (e.g.,

annual and 24-hr average) has more than one standard, sites can be scored for each standard. 
3-2

Area Served 

 Sites are ranked based on their area of coverage. Sites that are used to represent a large 

area score high in this analysis. Area of coverage (area served) for a monitor can be determined 

using the Thiessen polygons technique. Each polygon consists of the points closer to one 

particular site than any other site. This technique gives the most weight to rural sites and those 

sites on the edges of urban areas or other monitor clusters. Calculating Theissen polygons is one 

of the simplest quantitative methods for determining an area of representation around sites. 

However, it is not a true indication of which site is most representative of the pollutant 

concentration in a given area. Meteorology (including pollutant transport), topography, and 

proximity to population or emission sources are not considered, so some areas assigned to a 

particular monitor may actually be better represented by a different monitor. 

Monitor-to-Monitor Correlation 

 Concentrations measured at one monitor are compared to concentrations measured at 

other monitors to determine if concentrations correlate temporally. Monitor pairs with 

correlation coefficient values near one are highly correlated and should be ranked lower than 

those with correlation coefficient values near zero. Monitors that do not correlate well with other 

monitors exhibit unique temporal concentration variation relative to other monitors and are likely 

to be important for assessing local emissions, transport, and spatial coverage. Monitors with 

concentrations that correlate well (e.g., r2 > 0.75) with concentrations at another monitor may be 

redundant. This analysis should be performed for each pollutant. 

Emission Inventory 

 Emission inventory data are used to find locations where emissions of pollutants of 

concern are concentrated. These locations can be compared to the current or proposed network. 

This analysis can be scaled to various levels of complexity, depending on available resources. 

At the simplest level, county-level emissions patterns, such as those in the National Emission 

Inventory, can be compared with monitor locations. For measuring maximum precursor or 

primary emissions, monitors should be placed in those counties with maximum emission density. 

More complex methods use gridded emissions and/or species-weighted emissions, depending on 

their importance producing secondary pollutants of concern. 

Population Change 

 High rates of population increase are associated with potential increased emissions 

activity and exposure. Sites are ranked based on population increase in the area of 

representation. Area of representation can be determined using the Thiessen polygons technique. 

The total population change at the census-tract or block-group level that falls within the area of 

coverage of a monitor is assigned to that monitor. This technique gives most weight to sites in 

areas with high rates of population growth and large areas of representation. The population 

change method can also be applied to assess the importance of monitors from an environmental 
3-3

justice perspective. The technique is the same, except that population changes of specific groups 

(e.g., low income or minority) are calculated instead of total population. 

Population Served 

 Large populations are associated with high emissions. Sites are ranked based on the 

number of people they represent. Area of representation can be determined using the Thiessen 

polygons technique. Populations at the census-tract or block-group level that fall within the area 

of representation of a monitor are assigned to that monitor. This technique gives the most weight 

to sites that are in areas of high population and have large areas of representation. This 

technique was one of five site-by-site criteria used in the national-scale network assessment. 

 

Principal Component Analysis (PCA) 

 PCA can be applied to find monitoring sites that show a pattern of variability similar to 

other monitoring sites. PCA assigns each monitor to a group of monitors at which pollutant 

concentrations behave similarly to each other. This analysis can be useful for finding 

redundancy in the network. It is also useful in selecting sites for other analyses (e.g., source 

apportionment). PCA is commonly available in statistical software packages. Hourly or daily 

samples with high data completeness at each site are required to perform the analysis. 

Removal Bias 

 Measured values are interpolated across a domain using the entire network. Sites are 

then systematically removed and the interpolation is repeated. The absolute difference between a 

concentration measured at a site and the concentration predicted by interpolation with the site 

removed is the site’s removal bias. Greater bias or uncertainty indicates a more important site 

for developing interpolations to represent concentrations across a domain. Those sites with low 

bias may be providing redundant information. This analysis can also be performed on groupings 

of sites to test various site removal scenarios. Variations of this method were performed in the 

National Analysis, as well as in draft assessments for EPA Regions 3 and 4. 

Positive Matrix Factorization (PMF) 

 Sites are assigned to a group according to similar variability in concentrations. Sites 

within the same group may be redundant. PMF also predicts concentrations. The predicted 

concentrations for each group can be compared to the actual concentrations at each site to 

determine specific monitors that are not contributing useful information and can be 

removed/relocated. PMF requires specialized software and large data records from many sites. 

Suitability Modeling 

 Suitability modeling is a method for identifying suitable monitoring locations based on 

specific criteria. Geographic map layers representing important criteria, such as emissions 

source influence, proximity to populated places, urban or rural land use, and site accessibility, 
3-4

can be compiled and merged to develop a composite map representing the combination of 

important criteria in a defined area. Furthermore, each map layer input can be assigned a 

weighting factor based on the relative importance of each layer in the overall suitability model. 

The results identify the best locations to site monitors based on input criteria. 

Figure 3-1 shows an example front page of an analysis technique summary sheet. This 

page contains basic information about the type of analysis, the objectives that can be assessed, 

and the complexity and resources required. Resources include desktop GIS (ArcGIS, MapInfo, 

etc.) and statistical (SAS, S-Plus, Systat, etc.) tools and data. The front page of the summary 

sheet also lists some advantages and disadvantages of the analysis and lists other analyses that 

can provide similar information but may be more or less complex. The back page provides more 

detail about the technique, including an example, interpretation, and references for more 

information. 
3-5

Figure 3-1. Summary sheet front page example.
3-6
3-7

Number of Other Parameters Monitored 

Overview

Monitors that are collocated with other 

measurements at a particular air quality 

site are likely more valuable than sites 

that measure fewer parameters, 

particularly for source apportionment 

and other air quality studies. In 

addition, the operating costs can be 

leveraged among several instruments at 

these sites. Sites are ranked by the 

number of parameters (or instruments) 

that are collected at the particular site. 

Type: Site-by-site analysis 

Complexity: * 

Size of network: any 

Pollutants: any

Objectives Assessed

• Overall site value 

• Model evaluation 

• Source apportionment 

Resources

Tools Data 

 GIS Statistical 

Software Concentrations Site Locations Population Historical Data 

Site 

Information 

Emission 

Inventory Other 

Required   

Helpful 

Advantages

• Simple to perform (given data) 

• Good first step in understanding monitor 

sites 

Disadvantages

• Method does not “weight” the 

measurements (some pollutant 

measurements may be more useful than 

others) 

• Up-to-date information on the pollutants 

measured at particular sites can be 

difficult to acquire 

Similar Analyses (Complexity)

None 

Count of additional parameters measured at Washington ozone 

sites within AQS 
3-8

Number of Other Parameters Monitored 

Analysis Goals 

This analysis is performed by simply counting the number of other parameters that are measured 

at the physical site. Sites with many parameters measured are ranked highest. The metric 

addresses two aspects of monitor value. First, collocated measurements of several pollutants are 

valuable for many air quality analyses, such as source apportionment, model evaluation, and 

emission inventory reconciliation. Second, having a single site with multiple measurements is 

more cost-effective to operate than having monitors scattered at several sites. Other cost-based 

metrics were included in the Region 3 2003 network assessment. 

Example

This example in and around the Seattle, 

Washington, area was created in ESRI 

ArcGIS 9.1, using the following steps: 

1. Download monitor information from 

the Air Quality System (AQS) 

database. 

2. Use the monitor coordinate information 

to determine which monitoring sites are 

within the study domain. 

3. Sum the monitoring (measurements) 

parameters for each monitor location 

and determine the best locations to 

utilize in future air quality studies. 

Interpretation

The table at right is an extract of the analysis example for 

Seattle. The monitor locations are ranked by the number of 

parameters measured. As shown in the table, three monitors 

are located within the project study domain and measure 

numerous parameters. The site measuring 98 parameters is the 

most valuable for scientific analyses, such as emission 

inventory reconciliation and source apportionment. 

 

References 

Cimorelli A.J., Chow A.H., Stahl C.H., Lohman D., Ammentorp E., Knapp R., and Erdman T. 

(2003) Region III ozone network reassessment. Presented at the Air Monitoring & Quality 

Assurance Workshop, Atlanta, GA, September 9-11 by the U.S. Environmental Protection 

Agency, Region 3, Philadelphia, PA. Available on the Internet at 

http://www.epa.gov/ttn/amtic/files/ambient/pm25/workshop/atlanta/r3netas.pdf last accessed 

September 9, 2005. 

AIRS Code 

Number of 

Parameters 

Measured 

Study 

Domain 

530330080 98 

530110011 14 

530330023 14 

530330017 11 

530570018 11 

530090012 10 

530630001 9 
3-9

Trends Impact 

Overview

Monitors that have a long historical record are 

valuable for tracking trends. In this analysis, sites 

are ranked based on the duration of the continuous 

measurement record. The analysis can be as 

simple as ranking the available monitors based on 

the length of the continuous sampling record. 

This technique places the most importance on 

sites with the longest continuous trend record. 

Type: Site-by-site analysis 

Complexity: * to ** 

Size of network: any 

Pollutants: any 

Objectives Assessed

• Trend tracking 

• Historical consistency 

• Emission reduction evaluation 

Resources

Tools Data 

 GIS Statistical 

Software Concentrations Site Locations Population Historical Data 

Site 

Information 

Emission 

Inventory Other 

Required  

Helpful  

Advantages

• Simple analysis, requiring few statistical 

tools 

• Useful for identifying long-term trend 

sites 

• A good first look at monitor history 

Similar Analyses (Complexity)

• Number of other parameters 

monitored (*) 

• Measured concentrations (**) 

• Deviation from NAAQS (**) 

Disadvantages

• Length of continuous record does not 

ensure that data are of good quality 

throughout the time period 

• Magnitude or direction of past trends are 

not necessarily good predictors of future 

trends 

• Does not take into account changes in 

population, emissions, or meteorology 

• Overemphasis on sites with long 

historical record can be misleading as 

land use changes and other factors may 

contribute to concentration changes at a 

site. 

National ozone trends from EPA ozone trend report, 2003.
3-10

Trends Impact 

Analysis Goals 

Determining the trends impact of a monitor can be done simply. One approach is to rank sites 

based on their length of continuous sampling. Sites with the longest term of operation would 

score higher than those with shorter records, since they would be more useful for long-term trend 

analysis. Additional factors that could be used to adjust the simple ranking scale include (1) the 

magnitude and direction of trends observed to date at the site, (2) the suitability of a site’s 

location for monitoring trends after a significant event (e.g., enactment of a specific control 

measure), or (3) proximity of another monitor that could be used to continue the trend record. A 

site may be weighted as less important if changes in sampling and analysis methodology lead to 

a discontinuous record. Weighing these factors would require consideration of the overall goals 

of the monitoring network and the importance of the historical record. 

Example 

This table shows the number of annual 

averages available for tetrachloroethylene at 

toxics trends sites from 1990 to 2003. For 

this analysis, sites with the longest record 

would be rated higher than those with 

shorter records. 

References 

Cimorelli A.J., Chow A.H., Stahl C.H., Lohman D., Ammentorp E., Knapp R., and Erdman T. 

(2003) Region III ozone network reassessment. Presented at the Air Monitoring & Quality 

Assurance Workshop, Atlanta, GA, September 9-11 by the U.S. Environmental Protection 

Agency, Region 3, Philadelphia, PA. Available on the Internet at 

http://www.epa.gov/ttn/amtic/files/ambient/pm25/workshop/atlanta/r3netas.pdf last accessed 

September 9, 2005. 

City, State AQS SiteID Years

Stockton, CA 06-077-1002 13 

Baltimore, MD 24-510-0040 12 

Los Angeles, CA 06-037-1002 11 

San Francisco, CA 06-001-1001 10 

Fresno, CA 06-019-0008 10 

Baltimore, MD 24-005-3001 10 

Los Angeles, CA 06-037-1103 9 

Los Angeles, CA 06-037-4002 9 

San Diego, CA 06-073-0003 9 

San Francisco, CA 06-075-0005 9 

San Jose, CA 06-085-0004 9 

Baltimore, MD 24-510-0006 9 

Sacramento, CA 06-061-0006 8 

San Diego, CA 06-073-0001 8 

Oxnard, CA 06-111-2002 8 

Chicago, IL-IN-WI 18-089-2008 8 

Baltimore, MD 24-510-0035 8 
3-11

Measured Concentrations 

Overview

Individual sites are ranked based on the 

concentration of pollutants they measure. 

Monitors that measure high concentrations or 

design values are ranked higher than 

monitors that measure low concentrations. 

Results can be used to determine which 

monitors are less useful in meeting the 

selected objective. 

 

Type: Site-by-site analysis 

Complexity: ** 

Size of network: any 

Pollutants: any above detection limits

Objectives Assessed

• Maximum concentration location 

• Model evaluation 

• Regulatory compliance 

• Population exposure 

Resources 

Tools Data 

 GIS Statistical 

Software Concentrations Site Locations Population Historical Data 

Site 

Information 

Emission 

Inventory Other 

Required  

Helpful  

Advantages

• Identifies key sites from a regulatory 

perspective based on maximum 

concentrations. 

Disadvantages

• Does not account for monitor-siting 

problems; monitors may not be 

measuring maximum concentrations if 

not properly placed. 

• Only focuses on high concentrations; 

low-concentration monitors may be 

useful for representing rural locations or 

background concentrations. 

Similar Analyses (Complexity)

Deviation from NAAQS (**) 

Emission inventory (** to  ) 

1-hour PM2.5 concentrations in the Seattle area (ug/m3) 
3-12

Measured concentrations

Analysis Goals 

Sites that measure high concentrations are important for assessing NAAQS compliance and 

population exposure (AQI) and for performing model evaluations. The analysis is relatively 

straightforward, requiring only the site design values. The greater the design value, the higher 

the site rank. If more than one standard exists for a pollutant (e.g., annual and 24-hr average), 

monitors can be scored for each standard. 

Example 

This metric was one of five used in 

the 2000 National Analysis. The map 

shows the results for CO monitors. 

Sites in red record the highest CO 

concentrations and are the most 

valuable based on this metric. Sites 

in blue record the lowest values and 

are candidates for removal or 

repurposing. 

References 

Schmidt M. (2001) Monitoring strategy: national analysis. Presented at the Monitoring Strategy 

Workshop, Research Triangle Park, NC, October by the U.S. Environmental Protection Agency. 

Available on the Internet at http://www.epa.gov/ttn/amtic/netamap.html. 
3-13

Deviation from NAAQS 

Overview

Sites that measure concentrations (design 

values) that are very close to the NAAQS 

exceedance threshold are ranked highest in 

this analysis. These sites may be considered 

more valuable for NAAQS compliance 

evaluation. Sites well above or below the 

threshold do not not provide as much 

information in terms of NAAQS compliance. 

 

Type: Site-by-site analysis 

Complexity: **  

Size of network: any 

Pollutants: Any with NAAQS or other 

standards 

Objectives Assessed

• Regulatory compliance 

• Forecasting assistance 

Resources

Tools Data 

 GIS Statistical 

Software Concentrations Site Locations Population Historical Data 

Site 

Information 

Emission 

Inventory Other 

Required  

Helpful    

Advantages

• Assesses monitor importance for 

determining NAAQS compliance 

Similar Analyses (Complexity)

Measured concentrations (**) 

Removal bias (***) 

Disadvantages

• If design values vary from year to year, 

historical data should be included in the 

analysis 

• Care is needed in interpreting absolute 

differences 

Ozone monitors in California and their deviation (ppb) from the 

maximum 8-hr NAAQS for a single day. 
3-14

Deviation from NAAQS 

Analysis Goals 

This technique contrasts the difference between the standard and actual measurements or design 

values. It is a simple way to assess a monitor’s value for evaluating compliance. The design 

values for each pollutant should be calculated as they impact regulatory compliance. If a 

pollutant (e.g., annual and 24-hr average) has more than one standard, monitors can be scored for 

each standard. The absolute value of the difference between the measured design value and the 

standard can be used to score each monitor. Monitors with the smallest absolute difference will 

rank as most important. However, monitors that have higher design values than the standard 

(i.e., those in violation of the standard) may be considered more valuable from the standpoint of 

compliance and public health than those with design values lower than the standard, but with a 

similar absolute difference. Thus, absolute values of the difference can be ranked by peak 

concentration. It may be desirable to use more than one year of design values to look for 

consistency from year to year. 

Example

Deviation from the NAAQS was one 

of five metrics used in the 2000 

National Analysis. The analysis used 

one design value (1998–2000) and 

considered monitors above and below 

the standard equally. The map shows 

the results. Red circles denote sites 

that are nearest the standard, blue 

circles are those well above or below 

the standard, and black circles are in 

between. 

Interpretation 

The red sites are ranked highest in this analysis. Depending on the network assessment 

objectives, the number of red-site monitors might be adjusted. Blue sites are candidates for 

removal or repurposing. 

References 

Schmidt M. (2001) Monitoring strategy: national analysis. Presented at the Monitoring Strategy 

Workshop, Research Triangle Park, NC, October by the U.S. Environmental Protection Agency. 

Available on the Internet at http://www.epa.gov/ttn/amtic/netamap.html. 
3-15

Area Served 

Overview

Sites are ranked based on their area of 

coverage. Sites that are used to 

represent a large area score high in this 

analysis. Area of coverage (area served) 

for a monitor can be determined using 

the Thiessen polygons technique. Each 

polygon consists of the points closer to 

one particular site than any other site. 

This technique gives the most weight to 

rural sites and those on the edges of 

urban areas or other monitor clusters. 

Type: Site-by-site analysis 

Complexity: ** 

Size of network: Moderate or larger 

Pollutants: O3, PM2.5, SO2, some toxics

Objectives Assessed

• Spatial coverage 

• Interpolation 

• Background concentration 

Resources

Tools Data 

GIS Statistical 

Software Concentrations Site Locations Population Historical Data 

Site 

Information 

Emission 

Inventory Other 

Required   

Helpful 

Advantages

• Simple and quick to perform 

• Gives weight to remote and urban 

boundary sites that are necessary for 

proper interpolation (e.g., for AIRNow 

maps) 

Similar Analyses (Complexity)

• Population served (***) 

• Suitability modeling ( ) 

Disadvantages

• Does not take into account topography 

or actual air basins 

• Does not take into account population or 

emissions 

• May artificially weight monitors at the 

edge of the analysis domain 

Thiessan polygons showing the area served by ozone monitors 

(dots) in and around EPA Region 10. 
3-16

Area Served 

Analysis Goals 

Area served was one of five site-by-site criteria used in the national-scale network assessment. 

In the National Assessment, the “area served” metric was used as a proxy for the spatial 

coverage of each monitor. Theissen polygons (also called Voronoi diagrams) are applied as a 

standard technique in geography to assign a zone of influence or representativeness to the area 

around a given point. These polygons can be determined using a GIS package. Calculating 

Theissen polygons is one of the simplest quantitative methods for determining an area of 

representation around sites. However, it is not a true indication of which site is most 

representative in concentration to a given area. Meteorology (including pollutant transport), 

topography, and proximity to population or emission sources are not considered, so some areas 

assigned to a particular monitor may actually be better represented by a different monitor. More 

accurate determinations of representative monitors require a more sophisticated spatial analysis 

technique, such as suitability modeling, 

photochemical modeling, or parameter weighted 

distance. 

Example 

The map shows results of a study to determine 

zones of representativeness for meteorology towers 

in central California using a parameter-weighted 

distance technique. The method takes into account 

several factors to determine the “nearest” site: 

elevation, slope, time of day, season, height above

ground, average wind speed, predominant wind 

direction, and geographic distance. The result is a 

zone of influence around each site that is more 

realistic than simple Theissan polygons, which 

only consider distance. In this map, the green 

areas are those that are best represented by the 

allocated tower for surface meteorological 

conditions during winter morning hours while red 

areas are not well represented by any of the 

existing measurements. 

Interpretation

Regardless of the method for determining the boundaries of influence, the interpretation is the 

same. Sites with a greater area served are ranked higher than sites that only cover a small area. 

Sites that rank highly with this metric are valuable for interpolation, background concentration, 

and spatial coverage. 

References 

Knoderer C.A. and Raffuse S.M. (2004) CRPAQS surface and aloft meteorological representativeness (California Regional 

PM10/PM2.5 Air Quality Study Data Analysis Task 1.3). Web page prepared for the California Air Resources Board, Sacramento, 

CA, by Sonoma Technology, Inc., Petaluma, CA. Available on the Internet at http://www.sonomatechdata.com/crpaqsmetrep/ 

(STI-902324-2786). 

O'Sullivan D. and Unwin D.J. (2003) Geographic Information Analysis, John Wiley & Sons, Inc., Hoboken, New Jersey. 

U.S. Environmental Protection Agency (2001) National assessment of the existing criteria pollutant monitoring networks O3

, CO, 

NO2

, SO2, Pb, PM10, PM2.5 - Part 1. Outputs from the National Network Assessment Introduction and Explanation, July 21. 

Available on the Internet at http://www.epa.gov/ttn/amtic/netamap.html. 
3-17

Monitor-to-Monitor Correlation 

Overview

Measured concentrations at one monitor are 

compared to concentrations at other monitors 

to determine if concentrations correlate 

temporally. Monitors with concentrations that 

correlate well (e.g., r2 > 0.75) with 

concentrations at another monitor may be 

redundant. Conversely, a monitor with 

concentrations that do not correlate with other 

nearby monitored concentrations may be 

unique and have more value for spatial 

monitoring objectives. This analysis should be 

performed for each pollutant. 

Type: Site-by-site; Network optimization 

Complexity: **  to *** 

Size of network: large 

Pollutants: O3, PM2.5, some toxics 

Objectives Assessed

• Model evaluation 

• Spatial coverage 

• Interpolation 

Resources

Tools Data 

 GIS Statistical 

Software Concentrations Site Locations Population Historical Data 

Site 

Information 

Emission 

Inventory Other 

Required    

Helpful    

Advantages

• Gives measure of site’s uniqueness and 

representativeness 

• Useful for identifying redundant sites 

Disadvantages

• Large data requirements 

• Requires high data completeness 

• Correlations are probably pollutant 

specific

Similar Analyses (Complexity)

• Measured concentrations (**) 

• Principal Component Analysis (***) 

• Removal Bias (***) 

Figure from EPA Region 5 network assessment showing

monitor-to-monitor correlation in and around the Chicago area.
3-18

Monitor-to-Monitor Correlation 

Analysis Goals 

Determining the monitor-to-monitor correlation in a network requires at least two steps: 

(1) determining the temporal correlation between monitors through a regression analysis of 

concentrations; and (2) ranking the monitor’s uniqueness. Step one can be accomplished most 

simply by calculating Pearson correlation coefficients (r2) between each monitoring pair. Simple 

linear regressions can introduce error in the correlation coefficients, since they assume the 

ordinal axis has no error. Alternative methods include calculating Deming Regression or other 

types of correlation coefficients. In addition, choice of monitoring metrics may influence results 

(i.e., 1-hr peak ozone, every hour, 8-hr peak ozone, 24-hr average). Site pairs that have 

correlation coefficients with values near one are highly correlated and should be ranked lower 

than those with correlation coefficient values near zero. Sites that do not correlate well with 

other sites have unique temporal concentration variation relative to other sites and are likely to 

be important for assessing local emissions, transport, and spatial coverage. Conversely, those 

monitors that correlate with many other monitors may be redundant. 

Example 

This example shows a correlogram for 

ozone monitors located in the Chicago 

metropolitan area. Distance between 

monitors in kilometers is on the x-axis, 

and monitor-to-monitor correlation 

coefficients (r2) are on the y-axis. The 

correlogram shows that ozone 

concentrations are highly correlated at 

most sites in Chicago with values above 

0.8, and only decrease weakly as a 

function of distance. 

This plot was created by calculating correlation coefficients and 

distance between sites. 

Interpretation

This plot could be used to justify removing redundant sites, since concentrations correlate so 

well between most sites. Those monitor pairs with the lowest correlations (values around 0.6) 

would be rated as most important to retain. Note that high correlation may exist in ranges of 

concentrations; it is important to evaluate correlation above certain levels, as these days may be 

driving NAAQS decisions. 

References 

U.S. Environmental Protection Agency (2003) Region 5 network assessment. Presented at the Air Monitoring & Quality 

Assurance Workshop, Atlanta, CA, September 9-11 by the U.S. Environmental Protection Agency, Region 5. Available on the 

Internet at <http://www.epa.gov/ttn/amtic/files/ambient/pm25/workshop/atlanta/r5netas.pdf> last accessed September 9, 2005. 

Ito K., De Leon S., Thurston G.D., Nadas A., and Lippman M. (2005) Monitor-to-monitor temporal correlation of air pollution in 

the contiguous U.S. J. Exposure Analy. Environ. Epidem. 15, 172-184. 

Figure from EPA Region 5 network assessment
3-19

Emission Inventory 

Overview

Emission inventory data are used to 

find locations where emissions of 

pollutants of concern are concentrated. 

These locations can be compared to the 

current or proposed network. Does the 

network capture the areas of maximum 

emissions? This analysis can be scaled 

to various levels of complexity, 

depending on resources. The simplest 

version looks at county-level emissions 

of a single pollutant. More complex 

methods use gridded emissions and/or 

species-weighted emissions, depending 

on their importance in producing the 

secondary pollutant(s) of concern. 

Type: Bottom-up analysis 

Complexity: ** to   

Size of network: any 

Pollutants: primary pollutants and secondary 

precursors

Objectives Assessed

• Emission reduction evaluation 

• Maximum precursor location 

Resources

Tools Data 

 GIS Statistical 

Software Concentrations Site Locations Population Historical Data 

Site 

Information 

Emission 

Inventory Other 

Required 

Helpful   

Gridded 

and/or 

speciated 

Advantages

• Scaleable in complexity and spatial 

resolution 

• Can find areas where primary pollutant 

concentrations will be high 

Similar Analyses (Complexity)

Site suitability modeling (** **) 

Disadvantages

• Emission inventory data are not always 

current or may be incomplete or inaccurate 

• Emission inventory quality varies by 

pollutant and source type 

• More useful high resolution emission 

inventory data are not readily available 

and difficult to produce 

• Does not consider transport 

County level NOx emission inventory for three states in EPA 

Region 10. Darker shades represent greater emissions density. 
3-20

Emission Inventory 

Analysis Goals 

Emission inventory information is useful for determining locations of maximum emissions. At 

the simplest level, county-level emission data, such as the National Emission Inventory, can be 

compared with monitor locations. For measuring maximum precursor or primary emissions, 

monitors should be placed in those counties with maximum emission density (tons per year per 

square mile). More refined site placement decisions can be considered with more refined 

emission inventory data or wind data to indicate the up- and downwind directions. State and 

local air quality agencies can supply gridded emission inventories, which will depict more 

focused areas for measuring maximum precursor or primary emissions. Speciated emissions 

inventory data can also be used. The process of disaggregating inventory pollutants into 

individual chemical species components or groups of species will help determine placement of 

monitors that have pollutant-specific monitoring objectives. 

Example 

This example in and around the San Francisco Bay 

Area was created in ESRI ArcGIS 9.1 using the 

following steps: 

1. Acquire a gridded emission inventory for the 

greater San Francisco Bay Area 

2. Overlay an existing monitor network on the 

gridded inventory 

3. Determine the estimated emissions amount at each 

monitor location based on the grid cell it falls 

within 

4. For areas with high estimated emissions values, 

calculate the distance to the closest monitor 

location 

Interpretation 

The table at right is an extract of the analysis example for the 

San Francisco Bay Area. The high emission estimates 

listed by grid cell coincide with a distance value to the 

closest monitor location. A zero distance means a monitor is 

located within that grid cell. The table shows that the grid cell 

containing the largest amount of estimated emissions has a monitor 

over 10 kilometers away. The two blue circles on the map show 

areas of high emission density with no current monitors. These 

areas may be good candidates for future monitoring sites. 

Cell ID ; 

emissions 

(lbs/day) 

Distance to 

closest monitor 

(kilometers) 

4850 ; 936 10.7 

1099 ; 788 0 

1323 ; 777 1.4 

3395 ; 664 2.1 

745 ; 655 11.5 

4021 ; 627 3.1 

5223 ; 585 2.7 

788 ; 565 8.5 
3-21

Population Change 

Overview

High rates of population increase are 

associated with increased potential 

emissions activity and exposure. Sites 

are ranked based on population increase 

in the area of representation. Area of 

representation can be determined using 

the Thiessen polygons technique or a 

more sophisticated method (see Area 

Served). The total population change at 

the census-tract or block-group level that 

falls within the area of coverage of a 

monitor is assigned to that monitor. This 

technique gives most weight to sites in 

areas with high rates of population 

growth and large areas of representation. 

Type: Site-by-site analysis; bottom up 

Complexity: *** 

Size of network: any 

Pollutants: O3, PM2.5, SO2, some toxics

Objectives Assessed

• Population exposure 

• Environmental justice 

• Maximum precursor location 

Resources

Tools Data 

 GIS Statistical 

Software Concentrations Site Locations Population Historical Data 

Site 

Information 

Emission 

Inventory Other 

Required     

Helpful Demographics 

Advantages

• Assesses site importance for population 

exposure, an important regulatory goal 

• Flexible (a few possible methods) 

• Helpful for determining where 

monitoring may be required in the future 

• Helps identify monitors near which 

emissions may have substantially 

changed 

Similar Analyses (Complexity)

• Area served (**) 

• Population served (***) 

• Suitability modeling ( ) 

Disadvantages

• Does not take into account topography 

or actual air basins (using basic method) 

• Highly resolved population data may be 

difficult to work with 

• Changing census boundaries make it 

difficult to compare populated areas over 

time 

1990 to 2000 population change in and around Los Angeles. 
3-22

Population Change 

Analysis Goals 

Determining the population change near a particular monitor requires two steps: (1) identify the 

area of responsibility for each monitor; and (2) determine the percent change in population 

within each area of responsibility. Step 1 can be done most simply using the Theissen polygons 

technique; however, a more sophisticated method that takes into account distance, meteorology, 

topography, etc. can also be applied (see Area Served). Step 2 can be performed using U.S. 

Census population data at a variety of geographic levels (i.e., census block group, census tract). 

However, because census boundaries change over time, it is difficult and time-intensive to link 

localized census boundary data. The link between census boundary files is necessary to join the 

comparison population values and find an accurate percent change in population. One way to 

accomplish this is by gridding both data sets to a common grid scale. 

Sites that score high with this metric are important for assessing population exposure and 

tracking future emissions growth. The population change method can also be applied to assess 

the importance of monitors from an environmental justice perspective. The technique is the 

same, except population changes of specific groups (e.g., low income or minority) are calculated 

instead of total population. Population change can also be applied as a bottom-up technique. 

Using the census data, areas of rapid growth can be located and considered as potential locations 

for new monitors. 

Example

This example in and around the Los Angeles, 

California, area was created in ESRI ArcGIS 9.1 

using the following steps: 

1. Create Theissen polygon coverage of 

monitoring sites 

2. Link the 1990 and 2000 census tract 

polygons by tract ID in order to get total 

change in population by census tract 

3. Convert census tract polygons to centroid 

points 

4. Calculate the percent change in population 

for each monitoring area by spatially joining 

Theissen polygons to census tract centroids 

Interpretation

The table at right is an extract of the analysis example for Los 

Angeles. The area around site location 4 has seen a 13% increase in 

population and has, therefore, grown in importance for monitoring 

population exposure. 

References 

U.S. Census Bureau, Census 2000; 1990 Census, Population 

and Housing Unit Counts, United States. Available at 

<http://www.census.gov/>) 

Site 

Location 

% Population 

Change 1990 to 

2000 

1 5% 

2 12% 

3 10% 

4 13% 

5 5% 

6 6% 

7 5% 

8 5% 
3-23

Population Served 

Overview

Large populations are associated with 

high emissions. Sites are ranked based 

on the number of people they represent. 

Area of representation can be determined 

using the Thiessen polygons technique or 

a more sophisticated method (see Area 

Served). Populations at the census-tract 

or block-group level that fall within the 

area of representation of a monitor are 

assigned to that monitor. This technique 

gives the most weight to sites that are in 

areas of high population and have large 

areas of representation. 

Type: Site-by-site analysis 

Complexity: ** * 

Size of network: Moderate or larger 

Pollutants: O3, PM2.5, SO2, some toxics

Objectives Assessed

• Population exposure 

• Environmental justice

Resources

Tools Data 

 GIS Statistical 

Software Concentrations Site Locations Population Historical Data 

Site 

Information 

Emission 

Inventory Other 

Required    

Helpful Demographics 

Advantages

• Assesses site importance for population 

exposure, an important regulatory goal 

• Flexible (a few possible methods) 

Disadvantages

• Does not take into account topography 

or actual air basins (using basic method) 

• Highly resolved population data may be 

difficult to work with 

Similar Analyses (Complexity)

• Area served (**) 

• Counties served (**) 

• Population change (***) 

• Suitability modeling ( ) 

Population density and ozone monitor areas of representation in 

western Washington. Darker colors represent greater population. 
3-24

Population Served 

Analysis Goals 

Calculating the population served by a particular monitor requires two steps: (1) determine the 

area of representation for each monitor; and (2) determine the population within each area of 

representation. Step 1 can be performed most simply using the Theissen polygons technique; 

however, a more sophisticated method that takes into account distance, meteorology, 

topography, etc. could also be applied (see Area Served). Sites that score high with this metric 

are important for assessing population exposure. This technique was one of five site-by-site 

criteria used in the national-scale network assessment. Theissen polygons (also called Voronoi 

diagrams) are applied as a standard technique in geography to assign a zone of influence or 

representativeness to the area around a given point. 

The “population served” method can also be applied to assess the importance of monitors from 

an environmental justice perspective. The technique is the same, except populations of specific 

groups (e.g., low income or disadvantaged) are used instead of total population. 

Example

This example in and around the 

Seattle, Washington, area was created 

in ESRI ArcGIS 9.1 using the 

following steps: 

1. Create Theissen polygon coverage 

of PM2.5 monitoring sites 

2. Convert census block group 

polygons (available on ESRI data 

CDs) to centroid points 

3. Sum population in each 

monitoring area by spatially 

joining Theissen polygons to 

block group centroids 

Interpretation

The table at right is an extract of the analysis example for Washington 

State. Note that the population served varies by two orders of magnitude. 

The actual population values could be used to weight the sites, or they 

could simply be ranked. If the population values are used, the highly 

populated monitor sites will be given much greater weight than the 

sparsely populated monitor sites. This method could also be used within a 

network optimization assessment. For each network scenario, an average 

population served can be calculated. Scenarios with a lower average 

population served cover fewer persons per monitor, which may be less 

desirable. 

References 

U.S. Environmental Protection Agency (2001) National assessment of the existing criteria pollutant 

monitoring networks O3, CO, NO2, SO2, Pb, PM10, PM2.5 - Part 1. Outputs from the National Network 

Assessment Introduction and Explanation, July 21. Available on the Internet at http://www.epa.gov/ttn/amtic/netamap.html. 

O'Sullivan D. and Unwin D.J. (2003) Geographic Information Analysis, John Wiley & Sons, Inc., Hoboken, New Jersey. 

AIRS Code Population 

Served 

530630016 423,089 

530332004 383,571 

530110013 379,893 

530610005 349,160 

 

530750003 32,633 

530210002 28,538 

530330037 25,245 

530750006 12,363 

530130001 9,092 

530010003 8,961 

530750005 2,392 
3-25

Principal Component Analysis 

Overview

Principal component analysis (PCA) 

can be applied to find monitoring 

sites that have a pattern of variability 

similar to other monitoring sites. 

PCA assigns each monitor to a group 

of monitors at which pollutant 

concentrations behave similarly to 

each other. This analysis can be 

useful for finding redundancy in the 

network. It is also useful in selecting 

sites for other analyses (e.g., source 

apportionment). 

 

Type: Network Optimization 

Complexity: ** * 

Size of network: large 

Pollutants: O3, PM2.5, SO2, toxics

Objectives Assessed

• Background concentration 

• Forecasting assistance 

Resources

Tools Data 

 GIS Statistical 

Software Concentrations Site Locations Population Historical Data 

Site 

Information 

Emission 

Inventory Other 

Required    

Helpful   

Advantages

• Can identify potentially redundant 

monitors 

• Highlights spatial trends in data that help 

identify hot spots and large sources 

• Useful for site selection for other 

investigatory analyses 

• Identifies areas in similar air basins 

Disadvantages

• Requires analyst skill to avoid overinterpretation 

• Groups monitors by variability, not by 

concentration 

• Some monitors may appear in multiple 

groups 

• Requires high data completeness and 

lots of data 

Similar Analyses (Complexity)

Monitor-to-monitor correlation (** to ***) 

Removal bias (***) 

Example of resulting factor groups for ozone monitoring sites in the 

Seattle area. 
3-26

Principal Component Analysis (PCA) 

Analysis Goals

PCA is a useful tool for examining possible monitor redundancies. PCA identifies recurring and 

independent signals within large and noisy data sets such as ambient data (Eder et al., 1993). 

The results can be used to identify groups of sites with similar variance in measured 

concentrations. PCA is commonly available in statistical software packages. Hourly or daily 

samples (the more the better) with high data completeness at each site are required to perform the 

analysis. 

 

Example 

The example comes from an analysis of visibility 

measurements at Class I areas in the Central 

Regional Air Planning Association (CENRAP). 

Each color represents an identified cluster of sites 

that have similar variance patterns in visibility. 

Similar techniques have been applied to ozone in 

rural sites in the eastern United States (Eder et al., 

1993; Lehman et al., 2004). 

For its 2002 network assessment, EPA Region 5 

performed positive matrix factorization (PMF) on 

ozone monitors. PMF is a more complex analysis 

that achieves similar goals. 

Interpretation

The direct outputs from PCA or other factor analysis 

tools are not site groupings. Rather, they are 

principal components that describe a percentage of 

the concentration variance at a particular site. 

Sometimes, a given site may be in multiple principal 

components (factors), which can indicate a site that is in a transition zone between factors. 

Therefore, the results require interpretation to assign a specific monitor to a particular group or to

understand the “transition zones” in the network. The groupings are useful to select sites for 

additional analyses, assess zones of influence for a given pollutant, and identify possible 

redundant sites. 

References 

Eder B.K., Davis J.M., and Bloomfield P. (1993) A characterization of the spatiotemporal variability of non-urban ozone 

concentrations over the eastern United States. Atmos. Environ. 27A, 2645-2668. 

Lehman J., Swinton K., Bortnick S., Hamilton C., Baldridge E., Eder B., and Cox B. (2004) Spatio-temporal characterization of 

tropospheric ozone across the eastern United States. Atmos. Environ. 38, 4357-4369. 

Sullivan D.C., Hafner H.R., Brown S.G., MacDonald C.P., Raffuse S.M., Penfold B.M., and Roberts P.T. (2005) Analyses of the 

causes of haze for the Central States (phase II) summary of findings. Executive summary prepared for the Central States Regional 

Air Planning Association by Sonoma Technology, Inc., Petaluma, CA, STI-904780.08-2754-ES, August. 

U.S. Environmental Protection Agency (2003) Region 5 network assessment. Presented at the Air Monitoring & Quality 

Assurance Workshop, Atlanta, CA, September 9-11 by the U.S. Environmental Protection Agency, Region 5. Available on the 

Internet at http://www.epa.gov/ttn/amtic/files/ambient/pm25/workshop/atlanta/r5netas.pdf last accessed September 9, 2005. 
3-27

Removal Bias 

Overview

Measured values are interpolated across 

the domain using the entire network. 

Sites are then systematically removed and 

the interpolation is repeated. The 

absolute difference between the 

concentration measured at a site and the 

concentration predicted by interpolation 

with the site removed is the site’s 

removal bias. The greater the bias, the 

more important the site is for 

interpolation. This analysis can also be 

performed on groupings of sites to test 

various site removal scenarios. 

Type: Site-by-site; Network optimization 

Complexity: *** 

Size of network: large 

Pollutants: O3, PM2.5, SO2, some toxics 

Objectives Assessed

• Interpolation 

• Spatial coverage 

• NAAQS compliance 

• Background concentration 

• Model evaluation 

Resources

Tools Data 

 GIS Statistical 

Software Concentrations Site Locations Population Historical Data 

Site 

Information 

Emission 

Inventory Other 

Required    

Helpful  

Advantages

• Gives measure of site’s importance for 

several objectives 

• Useful for site-by-site ranking and 

network optimization 

Disadvantages

• Requires geostatistical tools 

• Does not account for geographic features 

• Most useful for pollutants with large 

networks 

Similar Analyses (Complexity)

• Monitor-to-monitor correlation (**) 

• Principal Component Analysis (***) 

Figure showing the removal bias of a single monitoring site in EPA 

Region 10. The darker shade represents greater bias. 
3-28

Removal Bias 

Analysis Goals 

Removal bias is a sensitivity analysis to determine how important a particular monitor (or set of 

monitors) is for interpolating concentrations across the domain. Variations of this method were 

performed in the National Analysis, as well as the draft assessments for EPA Regions 3 and 4. 

The basic method is to compare interpolations with and without data from specific monitors to 

determine either the bias or uncertainty that results from the removal of those monitors. Greater 

bias or uncertainty indicates a more important site for developing interpolations to represent 

concentrations across the domain. Those sites with low bias may be providing information that 

is redundant. With a base concentration field across the entire domain (developed through 

photochemical modeling), hypothetical monitors can also be tested. 

Example 

For the National Analysis, a site-by-site 

approach was used. That is, each site 

was removed individually and the 

resulting uncertainty at the site was 

calculated. Region 4 applied a network 

optimization technique, removing 

certain classes of sites (e.g., rural, urban 

core) and calculating interpolation bias. 

The image at right is from the Region 4 

assessment. It shows the bias in 8-hr 

ozone when all urban sites are 

removed: positive bias is shown in red 

and negative bias in green. 

Interpretation 

It is perhaps counterintuitive that 

removing all urban sites would produce a positive bias in concentrations of ozone. This is likely 

because 8-hr ozone concentrations are often at maximum downwind from the areas of maximum 

precursor emissions (urban areas). 

When looking at individual contributions to bias or uncertainty, as in the National Analysis, it is 

important to avoid over-interpretation. For example, clustered sites may all have low individual 

biases because of their redundancy and may all be candidates for removal. However, removing 

all of those sites would potentially create a large bias in the area. If a suite of monitors are 

targeted for removal, it would be useful to perform a bias analysis on the resulting network to 

ensure that the combined effects of removal are acceptable. 

References 

Cimorelli A.J., Chow A.H., Stahl C.H., Lohman D., Ammentorp E., Knapp R., and Erdman T. (2003) Region III ozone network 

reassessment. Presented at the Air Monitoring & Quality Assurance Workshop, Atlanta, GA, September 9-11 by the U.S. 

Environmental Protection Agency, Region 3, Philadelphia, PA. Available on the Internet at 

http://www.epa.gov/ttn/amtic/files/ambient/pm25/workshop/atlanta/r3netas.pdf last accessed September 9, 2005. 

Schmidt M. (2001) Monitoring strategy: national analysis. Presented at the Monitoring Strategy Workshop, Research Triangle 

Park, NC, October by the U.S. Environmental Protection Agency. Available on the Internet at 

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

U.S. Environmental Protection Agency (2002) Assessment of the ambient air monitoring networks. Draft report prepared for the 

U.S. Environmental Protection Agency, Research Triangle Park, NC, by the U.S. Environmental Protection Agency, Region 4, 

October. Available on the Internet at http://www.epa.gov/ttn/amtic/files/ambient/pm25/workshop/atlanta/r4netas.pdf last 

accessed September 9, 2005. 
3-29

Positive Matrix Factorization (PMF) 

Overview

Positive matrix factorization (PMF) can 

be applied to a network of monitoring 

sites to look for areas with similar 

concentrations and variability. With 

PMF, monitors are grouped with other 

monitors that behave similarly. PMF also 

predicts concentrations at each site. 

These predictions can help determine 

which sites within a particular group are 

providing useful (i.e. not redundant) 

information.

Type: Network Optimization 

Complexity:   

Size of network: large 

Pollutants: O3, PM2.5, SO2, toxics

Objectives Assessed

• Background concentration 

• Forecasting assistance 

• Transport/border characterization 

• Interpolation and understanding 

pollutant gradients

Resources

Tools Data 

 GIS Statistical 

Software Concentrations Site Locations Population Historical Data 

Site 

Information 

Emission 

Inventory Other 

Required   

PMF 

software, 

uncertainty 

estimates 

Helpful    

Advantages

• Can identify potentially redundant 

monitors 

• Highlights spatial trends in data that 

help identify hot spots and large sources 

• Identifies areas in similar air basins 

• Provides predicted concentrations and 

time series of factors 

Similar Analyses (Complexity)

• Monitor-to-monitor correlation 

(**  to ***) 

• Principal component analysis (***) 

• Removal bias (***)

Disadvantages

• Requires analyst skill to avoid overinterpretation 

• Groups monitors by variability, not by 

concentration 

• May assign some monitors to multiple 

groups 

• Requires high data completeness (all 

sites must have values for every day 

included in the model) and lots of data 

• Requires specialized software 

Example of one factor group for ozone monitoring sites in EPA 

region 5 (USEPA, 2003). 
3-30

Positive Matrix Factorization 

Analysis Goals

PMF is traditionally used as a source apportionment tool in which data from many different 

parameters measured at the same site are used to determine common sources of the parameters. 

PMF does this by separating the data into “factors” that can be interpreted as source profiles. If 

multiple sites measuring the same parameter are input into PMF, the factors constructed by PMF 

are interpreted as groups of similar sites. In addition to the factors, PMF also predicts 

concentrations for each factor. The factors can be used to determine redundant monitors in a 

network, and the ratios of actual-to-predicted concentrations for each site can indicate which 

sites are providing useful information. PMF also produces time series of each factor that can be 

used to assess transport. Specialized software, available from EPA, is necessary to perform PMF, 

and only days with measurements at all sites can be used in analysis. (Interpolation can be used 

to increase data completeness.) 

 

Example 

PMF was used in an assessment of 

the ozone monitoring network in EPA 

Region 5. This example shows the 

normalized coefficient of variation of 

the ratio of actual-to-predicted ozone 

concentrations for each day modeled 

(Rizzo and Scheff, 2004). A value 

close to 0 implies that a site could 

potentially be removed without 

significant loss of information about 

ozone concentrations in that area. 

Interpretation

PMF outputs “factors,” which are 

groups of sites with similar 

concentrations. The output of PMF 

tells the contribution each factor makes to a specific site’s concentrations. One site can be 

included in multiple factors, each with a unique contribution. The results need to be evaluated to 

determine which group each monitor should be assigned to. These groupings, similar to PCA 

results, can be used to examine zones of influence for a pollutant and identify areas with 

redundant sites. PMF also provides predicted concentrations which can be compared to actual 

concentrations to determine individual sites that can be removed or relocated. 

References 

Rizzo, M.J. and Scheff, P.A. (2004) Assessing Ozone Networks Using Positive Matrix Factorization. Environ. Progress. 23 (2), 

110-119. 

Paatero P., Hopke P.K., Hoppenstock J., and Eberly S.I. (2003) Advanced factor analysis of spatial distributions of PM2.5 in the 

eastern United States. Environ. Sci. Technol. 37 (11), 2460-2476. 

U.S. Environmental Protection Agency (2003) Region 5 network assessment. Presented at the Air Monitoring & Quality 

Assurance Workshop, Atlanta, CA, September 9-11 by the U.S. Environmental Protection Agency, Region 5. Available on the 

Internet at http://www.epa.gov/ttn/amtic/files/ambient/pm25/workshop/atlanta/r5netas.pdf last accessed September 9, 2005. 
3-31

Suitability Modeling 

Overview

Suitability modeling is a method for identifying suitable 

monitoring locations based on specific criteria. Geographic 

map layers representing important criteria, such as emissions 

source influence, proximity to populated places, urban or rural 

land use, and site accessibility can be compiled and merged to 

develop a composite map representing the combination of 

important criteria for a defined area. Furthermore, each map 

layer input can be assigned a weighting factor based on the 

relative importance of each layer in the overall suitability 

model. The results provide the best locations to site monitors 

based on the input criteria. 

Type: Bottom-up analysis 

Complexity:   

Size of network: any

Pollutants: any 

Objectives Assessed

• Population exposure 

• Environmental justice 

• Source-oriented monitoring 

• Model evaluation 

• Maximum concentration location 

• Background concentration 

• Transport/border characterization

Resources

Tools Data 

 GIS Statistical 

Software Concentrations Site Locations Population Historical Data 

Site 

Information 

Emission 

Inventory Other 

Required      Demographics 

Helpful  Meteorology 

Advantages

• Assesses site importance for population 

exposure—an important regulatory goal 

• Flexible (able to run several model 

scenarios) 

• Does not require ambient data 

• Graphic results are useful to a broad 

audience 

Similar Analyses (Complexity)

Area served (** ) 

County served (**) 

Population served (***) 

Population change (***)

Disadvantages

• Time-intensive 

• Weighting scheme is subjective; analysis 

is iterative 

• Requires skilled GIS analyst 

• GIS data layers can be difficult and 

costly to acquire 

Suitability model conceptual diagram. Input feature data are 

converted to gridded surfaces, classified to a common scale, 

weighted, and combined to form the output model. 
3-32

Suitability Modeling

Analysis Goals 

Suitability modeling can be used to determine ideal locations for potential monitoring or to 

assess existing monitors. The first step of a suitability analysis involves selecting criteria that 

can address monitoring objectives. The second step of the analysis is to acquire and process the 

spatial data for the suitability model within a GIS. The third and last step is to develop and run a 

suitability model for different model scenarios (see analysis approach figure below). 

Example 

In this example, suitability modeling was 

used to determine candidate sites for 

monitoring diesel particulate matter 

(DPM) in and around Phoenix, Arizona. 

Because we are interested in identifying 

locations where emissions for a particular 

pollutant are likely to be high, we must 

be able to spatially characterize the 

distribution of emissions for each major 

pollutant source category. 

The example was created using ESRI 

ArcGIS 9.1 (Spatial Analysis extension) 

using the following steps: 

1. Assess an emission inventory to 

determine the predominant sources of 

DPM in the region and determine the best available data to represent the spatial pattern of the 

identified emissions sources in the Phoenix region.

2. Acquire and process the spatial data (map layers) required for the analysis. For example, a 

map of roadways and associated traffic volumes for heavy- and light-duty vehicles were used 

to characterize the spatial distribution of emissions from on-road mobile sources. 

3. Develop and run the suitability model for different model scenarios. Three model scenarios 

were defined to examine the spatial distribution of DPM emissions: (1) development of a 

composite map to represent the spatial distribution and density of DPM emissions based on 

the locations of DPM sources (hot spots), (2) proximity of total population to DPM sources, 

and (3) proximity of sensitive population groups to DPM sources (see the figure above). 

Interpretation

Existing monitor locations, not originally located to investigate DPM, were suitable. Other 

locations in this fast-growing area were identified that would be suitable for assessment of DPM 

impacts on the population. 

References 

Hafner H.R., Penfold B.M., and Brown S.G. (2005) Using spatial analysis techniques to select 

monitoring locations. Presentation at the U.S. Environmental Protection Agency’s 2005 National 

Air Quality Conference: Quality of Air Means Quality of Life, San Francisco, CA, February 

12-13 (STI-2645). 
4-1

4. REMOVING A NAAQS COMPLIANCE MONITOR 

In addition to the requirement for state or local monitoring agencies to conduct a network 

assessment every 5 years, the October 17, 2006 amendments to the national monitoring 

regulations added a requirement that a state or local agency seek the Regional Administrator’s 

approval prior to shutting down a State or Local Air Monitoring Site (SLAMS) Federal 

Reference Method (FRM), Federal Equivalent Method (FEM), or Approved Regional Method 

(ARM) monitor. While the Regional Administrator may approve any monitor shutdown on a 

case-by-case basis, the monitoring regulations specify several situations where the state or local 

agency can be confident the request for monitor shutdown will be approved [40 CFR 58.14(c)]. 

The following paragraphs describe these situations.

4.1 ATTAINMENT REACHED AND EXPECTED TO BE MAINTAINED 

A monitor can be removed (after Regional Administrator approval) if it is currently in 

attainment with the applicable NAAQS standard and if the following four tests can be met: 

1. The PM2.5, ozone, carbon monoxide (CO), PM10, sulfate dioxide (SO2), lead, or nitrogen 

dioxide (NO2) monitor showed attainment during the previous five years. 

2. The probability is less than 10% that the monitor will exceed 80% of the applicable 

NAAQS during the next three years based on the concentrations, trends, and variability 

observed in the past. 

3. The monitor is not specifically required by an attainment plan or maintenance plan. 

4. The monitor is not the last monitor in a nonattainment area or maintenance area that 

contains a contingency measure triggered by an air quality concentration in the latest 

attainment or maintenance plan adopted by the state and approved by EPA. 

Tests 1, 3 and 4 are straightforward and do not require additional guidance. However, Test 2 

is more complicated. While other methods may be approved by the Regional Administrator, one 

approach to conservatively demonstrate the second test is to use Equation 1. 

NAAQS

n

t s

X 8.0 *

*

+ < (1)

Where X is the average design value for the last 5 yeas (or more), t is the student’s t 

value for n-1 degrees of freedom at the 90% confidence level, s is the standard deviation of the 

design values, n is the number of records (i.e., number of design values), and NAAQS is the 

standard of interest. 

Values for 0.8*NAAQS are provided in Table 4-1. Values for n, n-1, and student’s t 

value are provided in Table 4-2. A minimum of five years of data for pollutants with annual 

NAAQS (CO, NO2, SO2, PM10, lead) and five design values for O3 and PM2.5 are required for 

this demonstration. 
4-2

Table 4-1. National Ambient Air Quality Standardsa

Criteria Pollutant Form of the NAAQS NAAQS 0.8* NAAQS 

CO 8-hrb 9 ppm 7.2 ppm 

 1-hrb 35 ppm 28 ppm 

Lead Quarterly average 1.5 μg/m3 1.2 μg/m3 

NO2 Annual arithmetic 

mean 

0.053 ppm 0.042 ppm 

PM10 24-hrb 150 μg/m3 120 μg/m3

PM2.5 Annuald arithmetic 

mean 

15.0 μg/m3 12.0 μg/m3

 24-hre 35 μg/m3 28 μg/m3

Ozone 8-hrf 0.08 ppm 0.06 ppm 

 1-hrg 0.12 ppm 0.10 ppm 

SO2 Annual arithmetic 

mean 

0.03 ppm 0.02 ppm 

 24-hrb 0.14 ppm 0.11 ppm 

a

 As of Febrary 22, 2007. Current NAAQS can be found at http://www.epa.gov/air/criteria.html

b

 Not to be exceeded more than once per year 

c

 To attain this standard, the three-yr average of the weighted annual mean PM10 concentration at each monitor within an area 

must not exceed 50 μg/m3

. 

d

 To attain this standard, the three-yr average of the weighted annual mean PM2.5 concentrations from single or multiple 

community-oriented monitors must not exceed 15.0 μg/m3

. 

e

 To attain this standard, the three-year average of the 98th percentile of 24-hr concentrations at each population-oriented monitor 

within an area must not exceed 65 μg/m3. 

f

 To attain this standard, the three-year average of the fourth highest daily maximum 8-h4 average ozone concentrations 

measured at each monitor within an area over each year must not exceed 0.08 ppm. 

g

 The standard is attained when the expected number of days per calendar year with maximum hourly average concentrations 

above 0.12 ppm is < 1, as determined by 40CFR, appendix H. As of June 15, 2005, EPA revoked the 1-hr ozone standard in all 

areas except the 14 8-hr ozone nonattainment Early Action Compact (EAC) Areas. 
4-3

Table 4-2. Values for n, n-1, and student’s t value. 

Number of Data 

Values (n) 

Degrees of 

Freedom 

(n-1) 

Student’s t value 

(90% confidence) 

5 4 2.13 

6 5 2.02 

7 6 1.94 

8 7 1.89 

9 8 1.86 

10 9 1.83 

11 10 1.81 

12 11 1.80 

13 12 1.78 

14 13 1.77 

15 14 1.76 

16 15 1.75 

17 16 1.75 

18 17 1.74 

19 18 1.73 

20 19 1.73 

 Note that the use of Equation 1 is just one approach that can be used to determine if Test 

2 is met. Other approaches can be approved by the Regional Administrator. In particular, 

approaches that are sensitive to trends over the 5 years and/or that utilize the daily or hourly data 

rather than the design values may also be appropriate. 

As an example, consider the following CO measurements at several sites that have been 

operating for five years (Table 4-3). In this example, none of the sites are in a designated 

nonattainment or maintenance area. 

Table 4-3. Example computations for four CO monitoring sites. 

Site 2001 2002 2003 2004 2005 Average 

X

Standard 

Deviation 

s 

t N 90% Upper CI 

8-hour 6.8 7.2 9.6 6.3 6.4 7.26 1.35 2.13 5 8.6

1 1-hour 25 26 22 22 19 22.8 2.77 2.13 5 25

8-hour 4.9 6.3 6.5 4.4 2.0 4.82 1.81 2.13 5 6.5

2 1-hour 34 15 18 22 28 23.4 7.67 2.13 5 31

8-hour 5.1 4.9 4.8 5.2 5.0 5.00 0.16 2.13 5 5.2

3 1-hour 24 26 25 23 22 24.0 1.58 2.13 5 26

8-hour 7.4 6.8 6.4 6.4 6.3 6.66 0.46 2.13 5 7.1

4 1-hour 28 27 22 25 23 25.0 2.55 2.13 5 27

CI = confidence interval 
4-4

 Site 1 fails Test 1 because the design value for year 2003 exceeds the NAAQS, and fails 

Test 2 because the 90% upper confidence interval (8.6 ppm) is greater than 80% of the 

applicable 8-hour NAAQS. Therefore, site 1 should not be removed. Site 2 fails Test 2 because 

the 90% upper confidence interval (31 ppm) is greater than 80% of the applicable 1-hour 

NAAQS, and therefore this site should not be removed. The remaining sites pass all four tests, 

and could be shut down after Regional Administrator approval.

4.2 CONSISTENTLY LOW CONCENTRATIONS RELATIVE TO OTHER 

MONITORS 

Four tests must be passed in order to be sure a monitor can be removed on the basis that it 

is redundant because it has measured consistently low concentrations relative to other monitors: 

1. The CO, PM10, SO2, lead, or NO2 monitor has consistently measured lower 

concentrations of the same pollutant than another monitor in the same county (or 

portion of a county with a distinct attainment area or maintenance area, as applicable) 

during the previous five years. 

2. Control measures scheduled to be implemented or discontinued during the next five 

years do not apply to the areas around both monitors. 

3. Control measure changes will have similar effects on measured concentrations such 

that the retained monitor would remain the higher reading of the two monitors being 

compared. 

4. The monitor is not specifically required by an attainment plan or maintenance plan. 

4.3 MONITORS NOT MEASURING VIOLATIONS OF NAAQS 

Two tests must be passed in order to be sure a monitor can be removed that has not 

measured violations of the NAAQS: 

1. Any monitor for any pollutant in a county (or portion of a county within a distinct 

attainment, nonattainment, or maintenance area) that has not measured violations of 

the applicable NAAQS in the previous five years may be eligible for removal. 

2. The approved State Implementation Plan (SIP) provides for a specific, reproducible 

approach to representing the air quality of the affected county in the absence of actual 

monitoring data. 

4.4 MONITORS WITH SITING ISSUES 

A monitor that has been determined by EPA not to be comparable to the relevant 

NAAQS because of monitor siting (see FR Section 58.30) may be recommended for removal. 
4-5

4.5 UPWIND MONITORS 

For a monitor that is designed to measure concentrations upwind of an urban area to 

characterize transport into the area, the following two criteria should be met for removal: 

1. The monitor has not recorded violations of the relevant NAAQS in the previous five 

years. 

2. The monitor discontinuation is tied to start-up of another station also characterizing 

transport. 

4.6 LOGISTICAL PROBLEMS BEYOND AGENCY CONTROL 

 A SLAMS monitor not eligible for removal under any of the above criteria may be 

moved to a nearby location with the same scale of representation if logistical problems beyond 

the State’s control make it impossible to continue operation at its current site. 
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5-1

5. RECOMMENDATIONS 

Network assessment facilitates developing an optimal balance between scientific quality, 

protection of public and environmental health and welfare, and available resources. It is a tool 

for identifying opportunities to 

• redistribute resources to valued programs from low-priority or low-benefit ones; 

• create additional resources for programs previously thought to be unaffordable; 

• extract more value from existing networks; and 

• fully leverage the value of EPA’s or other agencies’ existing networks. 

Before beginning a network assessment, the purposes of the monitoring network—i.e., 

the network’s mission (e.g., establish regulatory compliance, further scientific understanding)—

should be established or carefully revisited and prioritized. With the network’s purposes and 

priorities in mind, users of this guidance document may perform the analyses described singly or 

in combination to design a technical network assessment suitable for their circumstances. Siteby-site comparisons help identify monitoring sites within an existing network that are most or 

least valuable relative to the purposes of the network. Bottom-up analyses yield appraisals of 

existing monitoring sites’ value relative to their optimal placement. Network optimization 

analyses are particularly useful when considering alternative scenarios for network design. 

This guidance document addresses specific technical elements of the overall framework 

for network assessments that is discussed in the most recent draft of the NAAMS (U.S. 

Environmental Protection Agency, 2005)—specifically, statistical and objective situation 

analyses. However, the NAAMS recognizes other key elements of network assessment, such as 

subjective situational analyses, cost considerations, sensitive populations, preparation of regional 

descriptions or network histories, and solicitation of input from state and local agencies or 

stakeholders. Further, the NAAMS acknowledges the importance of considering non-technical 

factors, such as political or justice-related issues. Therefore, this guidance document represents a 

starting point for the development of further guidance for network assessments. It is designed to 

be flexible and expandable with additional types of technical analyses and examples as 

techniques are improved, enhanced, and more broadly applied. In addition, the development of 

additional guidance covering other key elements of network assessments and non-technical 

considerations are areas for further work. 

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6. REFERENCES 

Cimorelli A.J., Chow A.H., Stahl C.H., Lohman D., Ammentorp E., Knapp R., and Erdman T. 

(2003) Region III ozone network reassessment. Presented at the Air Monitoring & 

Quality Assurance Workshop, Atlanta, GA, September 9-11. Available on the Internet at 

<http://www.epa.gov/ttn/amtic/files/ambient/pm25/workshop/atlanta/r3netas.pdf> last 

accessed September 9, 2005. 

Clean Air Scientific Advisory Committee and National Ambient Air Monitoring Strategy 

Subcommittee (2003) Summary minutes of the Clean Air Scientific Advisory Committee 

(CASAC) National Ambient Air Monitoring Strategy (NAAMS) Subcommittee premeeting public teleconference. Summary minutes, July. 

Eder B.K., Davis J.M., and Bloomfield P. (1993) A characterization of the spatiotemporal 

variability of non-urban ozone concentrations over the eastern United States. 

Atmospheric Environment 27A, 2645-2668. 

Hafner H.R., Penfold B.M., and Brown S.G. (2005) Using spatial analysis techniques to select 

monitoring locations. Presentation at the U.S. Environmental Protection Agency’s 2005 

National Air Quality Conference: Quality of Air Means Quality of Life, San Francisco, 

CA, February 12-13 (STI-2645). 

Henderson R. (2005) Clean Air Scientific Advisory Committee advisory on implementation 

aspects of the U.S. Environmental Protection Agency's final draft National Air 

Monitoring Strategy. Letter, April. 

Hopke P.K. (2003) Clean Air Scientific Advisory Committee review of the U.S. Environmental 

Protection Agency's National Ambient Air Monitory Strategy. Letter, November. 

Main H.H. and Roberts P.T. (2001) Recommendations for the PAMS network in the Northeast 

and Mid-Atlantic States. Report, STI-900860-2067-FR, June. 

Stahl C.H., Cimorelli A.J., and Chow A.H. (2002) A new approach to environmental decision 

analysis: Multi-Criteria Integrated Resource Assessment (MIRA). Bulletin of Science, 

Technology, & Society 22 (6), 443-459. 

U.S. Environmental Protection Agency (2001) National assessment of the existing criteria 

pollutant monitoring networks O3, CO, NO2, SO2, Pb, PM10, PM2.5 - Part 1. Outputs from 

the National Network Assessment Introduction and Explanation, July 21. Available on 

the Internet at http://www.epa.gov/ttn/amtic/netamap.html. 

U.S. Environmental Protection Agency (2003a) AirNow web site. Available on the Internet at 

<http://www.airnow.gov> last accessed January 17, 2005. 

U.S. Environmental Protection Agency (2003b) Air Monitoring & Quality Assurance Workshop 

- September 2003. Air Monitoring & Quality Assurance Workshop, Atlanta, GA, 

September 9-11, 2003. Available on the Internet at 

<http://www.epa.gov/ttn/amtic/pmatla.html> last accessed September 9, 2005. 

U.S. Environmental Protection Agency (2003c) Region 5 network assessment. Presented at the 

Air Monitoring & Quality Assurance Workshop, Atlanta, CA, September 9-11. Available 

on the Internet at 

<http://www.epa.gov/ttn/amtic/files/ambient/pm25/workshop/atlanta/r5netas.pdf> last 

accessed September 9, 2005. 

U.S. Environmental Protection Agency (2005) National ambient air monitoring strategy. Draft 

report, December. 
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U.S. Environmental Protection Agency (2006) Revisions to ambient air monitoring regulations. 

Federal Register. Vol. 71, No. 10, pages 2701-2808. To be codified at 40 CFR Parts 53 

and 58, January 17. 
A-1

APPENDIX A 

EXAMPLE APPLICATIONS 

Several network assessments and other projects related to network assessment have 

already been executed for air quality monitoring networks. Here we present a few examples, 

covering a range of scales, objectives, and available resources. 

A.1 NATIONAL ASSESSMENT 

A national assessment was performed in 2000 on the criteria pollutant monitoring 

network. Its goal was to provide broad directional recommendations on a national level and act 

as a guide for more focused regional (and local) assessments. The National Assessment (U.S. 

Environmental Protection Agency, 2001) utilized five site-by-site analysis metrics and several 

weighting schemes to rank individual monitors. Each pollutant was considered separately. The 

five metrics used were (in the terms of this document) area served, population served, measured 

concentrations, deviation from NAAQS, and uncertainty on removal. Divestment opportunities 

were highlighted using the ranked monitors. The National Assessment is available at 

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

A.2 REGIONAL ASSESSMENTS 

In fiscal year 2003, each of the ten EPA regions began their own network assessments, 

building from the 2000 National Assessment. These assessments varied greatly in their methods 

and depth, partly motivating this document. The approaches taken by three of the regions are 

highlighted below. 

A.2.1 Region 3 Ozone Network Assessment 

EPA Region 3 employed a network optimization technique for its network assessment. 

The technique was based on an iterative analytical decision making processed called MultiCriteria Integrated Resource Assessment (MIRA) (Cimorelli et al., 2003),(Stahl et al., 2002). In 

brief, the technique started with several possible network configurations and ranked them using 

40 decision criteria organized hierarchically; the four primary level criteria are trends impact, 

costs, air quality, and personnel impact. The “air quality” criterion was the ability of the network 
A-2

to capture and properly interpolate concentrations from a base-case scenario developed with 

photochemical modeling. MIRA incorporates stakeholder feedback and participation throughout 

the process, and network configurations, design criteria, and weighting schemes were modified 

as learning proceeded. 

A.2.2 Region 4 Network Assessment 

EPA Region 4 utilized EPA monitoring re-engineering guidance (U.S. Environmental 

Protection Agency, 1998) for all criteria monitors except ozone and PM2.5. The re-engineering 

guidance suggests monitors that do not exceed 60% of the NAAQS are candidates for 

termination. From this baseline, Region 4 worked with state and local agencies to determine 

which candidate monitors were of low value or redundant and which monitors provided useful 

research information or satisfied regulatory requirements. 

Because none of the ozone monitors and only one of the PM2.5 monitors in Region 4 were 

below the 60% threshold, additional geospatial analyses were performed using the National 

Assessment (U.S. Environmental Protection Agency, 2001) as a guide. Region 4 used the 

removal bias technique to determine the effects of removing certain classes of monitors (e.g., 

urban core, downwind, upwind). 

A.2.3 Region 5 PM2.5 Network Assessment 

The EPA Region 5 PM2.5 network assessment process was organized by the Lake 

Michigan Air Directors Consortium (LADCO) ((U.S. Environmental Protection Agency, 2003c). 

It is a site-by-site analysis, similar to the National Assessment, and considered four metrics: 

measured concentrations, monitor-to-monitor correlation, population change, and monitor 

density. Rather than weighting each metric and developing a combined score, the metrics were 

considered in a stepwise fashion. Sites were first ranked only on the most important metric 

(monitor density), the highest scoring monitors (i.e., those farthest from other monitors) were 

then eliminated from consideration for removal. The remaining monitors were ranked based on 

the next most important metric and so on. The monitors in the final list were then considered 

individually for possible elimination. 

A.3 PHOTOCHEMICAL ASSESSMENT MONITORING STATIONS (PAMS) 

NETWORK ASSESSMENT 

In 2001, a portion of the PAMS network (the northeast and mid-Atlantic) was assessed 

with the goal of balancing and redirecting resources to meet evolving program objectives (Main 

and Roberts, 2001). Starting with the existing network, the analysis identified the minimum type 

and number of observations required to satisfy PAMS goals, determined what monitors met the 

those goals, developed recommendations for eliminating monitors that were not required, and 

identified ways to further enhance the PAMS program in the long-term with the resources saved. 

To determine monitors that could be eliminated, the study looked at site pairs in close proximity 

and performed several statistical data analysis techniques to determine similarity, including 
A-3

medians, interquartile ranges, confidence intervals, and p-values. They also found that some 

sites were designated as types (upwind, maximum ozone, etc.) that did not match the data. 

A.4 PHOENIX DIESEL PARTICULATE MATTER SITE SUITABILITY 

MODELING 

Diesel particulate matter (DPM) is an issue of increasing concern for protecting public 

health. The Arizona Department of Environmental Quality (ADEQ) sponsored a study to 

determine possible locations for placing monitors to measure DPM ((Hafner et al., 2005). 

Suitability modeling was used to predict areas of high DPM emissions within Phoenix, Arizona. 

Maps of emission sources, emissions activity data, and meteorology were combined within a GIS 

model to produce a composite map identifying regions where DPM emissions are likely to be 

high. 
United States Office of Air Quality Planning and Standards Publication No

Environmental Protection Air Quality Assessment Division February 2007 Agency Research Triangle Park, NC