United States Office of Air Quality EPA-454/R-99-022

Environmental Protection Planning and Standards December 1997

Agency Research Triangle Park, NC 27711

Air

Guidance For Network

 Design and Optimum

 Site Exposure For PM2.5

 And PM10
GUIDANCE FOR NETWORK DESIGN AND 

OPTIMUM SITE EXPOSURE FOR PM2.5 AND PM10

PREPARED BY

John G. Watson1

Judith C. Chow1

David DuBois1

Mark Green1

Neil Frank2

Marc Pitchford3

PREPARED FOR

Office of Air Quality Planning and Standards

U.S. Environmental Protection Agency

Research Triangle Park, NC 27711

December 15, 1997

Desert Research Institute, University and Community College System of Nevada, PO Box 60220, Reno, NV 89506 1

U.S. EPA/OAQPS, Research Triangle Park, NC 27711 2

National Oceanic and Atmospheric Administration, 755 E. Flamingo, Las Vegas, NV 89119 3
DISCLAIMER

The development of this document has been funded by the U.S. Environmental Protection

Agency, under cooperative agreement CX824291-01-1, and by the Desert Research Institute of

the University and Community College System of Nevada. Mention of trade names or

commercial products does not constitute endorsement or recommendation for use. The examples

presented in this guidance for Alabama and California were selected for illustration purposes only,

and they are not intended to represent the actual networks that might be implemented by the

responsible agencies. This guidance represents EPA’s current views on these issues and do not

bind the States and public as a matter of law. This document has not been subject to the

Agency’s peer and administrative review, and does not necessarily represent Agency policy.
ii

ABSTRACT

This guidance provides a method and rationale for designing monitoring networks to

determine compliance with newly enacted PM2.5 and PM10 National Ambient Air Quality

Standards. It defines concepts and terms of network design, presents a methodology for

defining planning areas and community monitoring zones, identifies data resources and the

uses of those resources for network design, and provides some practical examples of applying

the guidance. PM2.5 monitoring sites are to be population-oriented, measuring exposures

where people live, work, and play. For comparison to the annual PM2.5 standard, the

locations must be community-oriented and as such, these do not necessarily correspond to the

locations of highest PM concentrations in an area. Existing Metropolitan Statistical Areas are

first examined to determine where the majority of the people live in each state. These are then

broken down into smaller populated entities which may include county, zip code, census tract,

or census block boundaries. Combinations of these population entities are combined to define

Metropolitan Planning Areas. These may be further sub-divided into Community Monitoring

Zones, based on examination of existing PM measurements, source locations, terrain, and

meteorology. Finally, PM2.5 monitors are located at specific sites that represent neighborhood

or urban scales to determine compliance with the annual standard and at maximum, population

oriented locations for comparison with the 24-hour standard. Transport and background sites

are located between and away from planning areas to determine regional increments to PM

measured within the planning area.
iii

TABLE OF CONTENTS

Page

Disclaimer..............................................................................................................................i

Preface ..................................................................................................................................ii

Abstract ............................................................................................................................... iii

List of Tables ........................................................................................................................ v

List of Figures...................................................................................................................... vi

1.0 Introduction ................................................................................................................1-1

1.1 Objectives of Guidance.........................................................................................1-2

1.2 Schedule and Approvals for Network Design and Implementation.........................1-2

1.3 Related Documents...............................................................................................1-3

2.0 Concepts of Network Design.......................................................................................2-1

2.1 Particle Properties ................................................................................................2-1

2.2 Concepts ............................................................................................................2-11

2.2.1 Spatial Uniformity .....................................................................................2-11

2.2.2 Receptor Site Zone of Representation........................................................2-11

2.2.3 Community-Oriented Monitoring...............................................................2-13

2.2.4 Background and Regional Transport Monitoring .......................................2-14

2.2.5 Emissions Zone of Influence......................................................................2-16

2.2.6 PM2.5 Sampler Types.................................................................................2-16

2.3 Definitions..........................................................................................................2-17

2.3.1 Theoretical Concepts.................................................................................2-17

2.3.2 Monitoring Boundaries..............................................................................2-18

2.3.3 Monitoring Networks................................................................................2-19

2.3.4 Site Types.................................................................................................2-20

2.4 Network Design Philosophies.............................................................................2-23

2.4.1 Network Design Objectives.......................................................................2-23

2.4.2 Random Sampling .....................................................................................2-24

2.4.3 Systematic Sampling..................................................................................2-24

2.4.4 Judgmental Sampling.................................................................................2-25

2.4.5 Heterogeneous Siting Strategies................................................................2-25

2.4.6 Other Siting Strategies...............................................................................2-26

3.0 Defining State Planning Areas......................................................................................3-1

3.1 Identify Political Boundaries of Populated Areas...................................................3-2

3.2 Identify Natural Air Basins....................................................................................3-8

3.3 Identify Existing Air Quality Monitoring Sites.....................................................3-14

3.4 Reconcile Boundaries with Existing Planning Areas............................................3-14

3.5 Summary............................................................................................................3-20

4.0 Defining PM2.5 Community Monitoring Zones .............................................................4-1

4.1 Locate Emissions Sources.....................................................................................4-2
iv

4.2 Identify Meteorological Patterns...........................................................................4-2

4.3 Compare PM Concentrations................................................................................4-6

4.4 Adjust CMZs to Jurisdictional Boundaries............................................................4-9

4.5 Locate Sites..........................................................................................................4-9

5.0 Monitor Siting.............................................................................................................5-1

5.1 Internal Siting Criteria ..........................................................................................5-1

5.2 External Siting Criteria .........................................................................................5-2

5.3 Evaluating Zones of Representation......................................................................5-3

5.4 Evaluating Siting Redundancy...............................................................................5-4

5.5 Evaluating Network Adequacy for Spatial Averaging............................................5-4

5.5.1 Temporal Behavior......................................................................................5-4

5.5.2 Consistent trends.........................................................................................5-5

5.5.3 Spatial Placement of Monitors.....................................................................5-5

5.5.4 Chemical Composition of PM2.5 ...................................................................5-6

5.5.5 Population Density and Air Quality Patterns (additional supporting

evidence).....................................................................................................5-6

6.0 State PM Monitoring Network Descriptions, Annual Reports, and Network

Evaluations..................................................................................................................6-1

6.1 State PM Monitoring Network Descriptions.........................................................6-1

6.2 Annual Measurement Reports...............................................................................6-3

6.3 Annual Network Evaluation..................................................................................6-4

7.0 References...................................................................................................................7-1

Appendix A Sources of Data Used for Network Design and Evaluation

Appendix B Metropolitan Statistical Areas, Primary Metropolitan Statistical Areas,

Consolidated Metropolitan Statistical Areas, and New England County

Metropolitan Statistical Areas in the United States
v

LIST OF TABLES

Page

Table 2.1.1 Chemicals from particles in different emissions sources. 2-5

Table 2.2.1 IMPROVE measurement sites. 2-15

Table 3.1.1 Alabama Metropolitan Statistical Areas and Primary Metropolitan

Statistical Areas. 3-5

Table 3.1.2 California Metropolitan Statistical Areas and Primary Metropolitan

Statistical Areas. 3-6

Table 4.3.1 Uniformity measures for PM10 in Birmingham. 4-7

Table 4.5.1 Number of required core PM2.5 SLAMS monitors per MSA. 4-10
vi

LIST OF FIGURES

Page

Figure 2.1.1 Idealized size distribution of particles in ambient air. 2-2

Figure 2.1.2 Aging times for homogeneously distributed particles of different

aerodynamic diameters in a 100 m deep mixed layer. 2-3

Figure 2.1.3 Size distributions of several particulate source emissions. 2-4

Figure 2.1.4 Hourly variations in the 20th, 50th, and 80th percentiles of PM10 in Las

Vegas, NV. 2-7

Figure 2.1.5 PM10 concentrations at different nearby sites centered around Fresno,

CA. 2-8

Figure 2.1.6 Spatial variation in 24-hour PM10 chemical compositions from the

neighborhood to regional scale. 2-9

Figure 2.1.7 Normalized PM10 concentrations at increasing distances from an

unpaved road. 2-10

Figure 2.4.1 Examples of judgmental and hybrid sampling strategies. 2-26

Figure 3.1.1 Metropolitan Statistical Areas and Primary Metropolitan Statistical

Areas in the continental U.S. with 1990 populations. 3-3

Figure 3.1.2 Metropolitan Statistical Areas and Primary Metropolitan Statistical

Areas in the continental U.S. with 1995 populations. 3-4

Figure 3.1.3 National parks and monuments, national wildlife refuges, national

forests, Indian reservations, and IMPROVE background monitoring

sites. 3-9

Figure 3.1.4 Populated entities in the Birmingham MSA: a) counties, b) zip codes,

c) census tracts, and d) census blocks. 3-10

Figure 3.2.1 The Birmingham MSA in relation to counties, principal cities (+), and

terrain in Alabama. 3-12

Figure 3.2.2 Central California MSAs in relation to counties, principal cities (+), and

terrain. 3-13

Figure 3.3.1 Populations in Jefferson and Shelby county census tracts. 3-15
vii

LIST OF FIGURES (continued)

Page

Figure 3.3.2 Potential Monitoring Planning Area for the Birmingham MSA. 3-16

Figure 3.3.3 Census tract boundaries and past and present PM monitoring sites in

California’s San Joaquin Valley. 3-17

Figure 3.3.4 Potential San Joaquin Valley MPAs: a) Stockton/Modesto, b)

Fresno/Visalia, and c) Bakersfield. 3-18

Figure 4.2.1 Hourly wind transport directions (from N) and distances (km).

1988-92 Birmingham airport winds. 4-3

Figure 4.2.2 Hourly wind transport directions (from N) and distances (km).

1988-92 Fresno airport winds. 4-4

Figure 5.2.1 Recommended distances and elevations of PM sampler inlets from

heavily traveled roadways. 5-3

Figure 5.5.1 Example of spatial homogeneity over a three-year period. 5-5

Figure 5.5.2 Example of temporal trends. 5-5

Figure 5.5.3 Example of spatial averaging. 5-6

Figure 5.5.4 Example of differences in chemical composition between two sites. 5-6

Figure 5.5.5 Example of using population density for monitor placement. 5-7
1-1

1.0 INTRODUCTION

This document provides guidance for locating monitoring stations to measure

compliance with national standards for Suspended Particulate Matter (PM) in the atmosphere.

PM has been shown to adversely affect public health when susceptible populations are

exposed to excessive concentrations (U.S. EPA, 1996; Vedal, 1997). National Ambient Air

Quality Standards (NAAQS) for PM have been established to minimize the adverse effects of

PM on the majority of U.S. residents. This guidance document is based on the new NAAQS

(U.S. EPA, 1997). This document may be revised where necessary as it is further refined by

actual application to network design during the implementation of the new monitoring

program. The NAAQS apply to the mass concentrations of particulates with aerodynamic

diameters lower than 10 μm (PM10) and 2.5 μm (PM2.5) and are described as follows (U.S.

EPA, 1997):

• Twenty-four hour average PM2.5 not to exceed 65 μg/m3

 for a three-year average

of annual 98th percentiles at any population-oriented monitoring site in a

monitoring area.

• Three-year annual average PM2.5 not to exceed 15 μg/m3 concentrations from a

single community-oriented monitoring site or the spatial average of eligible

community-oriented monitoring sites in a monitoring area.

• Twenty-four hour average PM10 not to exceed 150 μg/m3 for a three-year average

of annual 99th percentiles at any monitoring site in a monitoring area.

• Three-year average PM10 not to exceed 50 μg/m3 for three annual average

concentrations at any monitoring site in a monitoring area.

The PM2.5 NAAQS are new. While the PM10 NAAQS retain the same values as the

prior NAAQS (U.S. EPA, 1987), their form is new. Previously, the PM NAAQS applied to

the highest 24-hour or annual averages found within a monitoring planning area, and

monitoring networks were often designed to measure these highest values. These networks

did not necessarily represent the overall exposure of populations to excessive PM

concentrations. Some data from these networks were disregarded by epidemiologists as being

unrelated to health indicators such as hospital admissions and death. Air quality districts may

have been reluctant to locate source-oriented monitors that might assist in understanding

source impacts because such monitors might cause a large area to be designated in nonattainment of NAAQS.

The new forms for these standards are intended to provide more robust measures for

the PM indicator. While PM10 network design and siting criteria are unchanged, new PM2.5

monitoring networks to determine compliance or non-compliance are intended to best

represent the exposure of populations that might be affected by elevated PM2.5 concentrations.

As used in this document, the word compliance means attainment of a NAAQS. This involves

new concepts of spatial averaging and the operation of some monitoring sites for PM2.5

measurements that are not eligible for comparison to one or both of the PM2.5 NAAQS.
1-2

Special Purpose Monitoring sites that help to understand the causes of non-compliance are

encouraged by excluding their data from the compliance determination during the first two

years of their operation. The number of monitors in the existing PM10 network will likely

decrease as new PM2.5 sites are established. The PM2.5 sites may or may not be collocated

with PM10 monitoring locations. This guidance for network design and optimum site

exposure of PM10 and PM2.5 monitors describes how particulate monitoring networks can

comply with these intentions.

1.1 Objectives of Guidance

The objectives of the guidance specified here are to:

• Define concepts and terms of network design.

• Present a methodology for defining planning areas and selecting and evaluating

monitoring sites in a network.

• Summarize the availability and usage of existing resources for network design.

• Demonstrate the methodology in practical applications.

This guidance builds upon the guidance specified by Koch and Rector (1987) for PM10

monitoring associated with the previous PM NAAQS. It also considers recent advances in

sampling theory, the availability of different types of data over the Internet and on CD-ROM,

and the practical experience of different air quality management districts.

Network design guidance must be more specific than in the past with respect to types

of sites and what they represent. It should identify data available to make judgments on site

selection and define methods to use these data for those judgments. It should provide

methods to evaluate the extent to which these judgments were valid. This guidance intends to

provide this specificity.

1.2 Schedule and Approvals for Network Design and Implementation

The implementation of network design, operation and evaluation for the revised PM

NAAQS follows this schedule:

• July 18, 1997: Standards were promulgated.

• September 16, 1997: Standards became effective.

• October–December, 1997: Guidance is applied by state and local agencies in test

areas and procedures are refined. Network deployment is completed.

• January 1, 1998: Network design guidance is finalized and the regulated

requirement of PM2.5 monitoring commences.
1-3

• July 1, 1998: Each state submits a PM monitoring network description to its EPA

Regional Administrator describing its network.

• September 16, 1998: Commence operation of at least one core PM2.5 SLAMS

site in each MSA with population greater than 500,000, one site in each PAMS

area, and two additional SLAMS sites per state. See footnote 1.

• July 1, 1999, 2000, 2001, 2002, etc.: State and local agencies submit annual

monitoring reports and network evaluations, based on data from previous calendar

year.

• September 16, 1999: Commence operation of other required SLAMS sites

(including all required core SLAMS, required regional background and regional

transport SLAMS, continuous PM monitors in areas with population greater than

1 million, and all additional required PM2.5 SLAMS). See footnote 1.

• September 16, 2000: Commence operation of additional sites (e.g., sites

classified as SLAMS/SPM to complete the mature network). See footnote.1

1.3 Related Documents

Other documents related to PM monitoring networks are:

• The Federal Register for July 18, 1997, pages 38652-38760 and 38764-38854,

describe the proposed new PM standards, monitoring requirements, and

designation of reference and equivalent methods for PM2.5 (U.S. EPA, 1997).

 

1

 Network deployment schedules as defined by 40 CFR part 58. Accelerated schedule may be dictated by

additional guidance and EPA policy.
2-1

2.0 CONCEPTS OF NETWORK DESIGN

Several new concepts and definitions are embodied in the form of the revised air

quality standards. A brief overview of these concepts and definitions is given in this section.

2.1 Particle Properties

A wide variety of suspended particles are found in a typical atmosphere. Size, chemical

composition, concentration, and temporal variability all have the potential to affect public

health and perception of pollution. Several of these same properties allow suspended particles

to be attributed to their sources.

Friedlander (1970, 1971) proposes a size-composition probability density function

(PDF) to describe the number of suspended particles at given times and points in space with

specified chemical composition and particle size. While a useful theoretical concept, the exact

PDF can never be obtained in practice with current technology. Since all sizes and every

chemical component of particles cannot be measured everywhere at all times, the

measurement problem must be narrowed in scope to identify those properties that are

important for compliance.

Figure 2.1.1 shows the major features of the mass distribution of particle sizes found in

the atmosphere. The “nucleation” range, also termed “ultrafine particles”, consists of particles

with diameters less than ~0.08 μm that are emitted directly from combustion sources or that

condense from cooled gases soon after emission. The lifetimes of particles in the nucleation

range are usually less than one hour because they rapidly coagulate with larger particles or

serve as nuclei for cloud or fog droplets. This size range is detected only when fresh

emissions sources are close to a measurement site or when new particles have been recently

formed in the atmosphere.

The “accumulation” range consists of particles with diameters between 0.08 and

~2 μm. These particles result from the coagulation of smaller particles emitted from

combustion sources, from condensation of volatile species, from gas-to-particle conversion,

and from finely ground dust particles. The nucleation and accumulation ranges constitute the

“fine particle size fraction”, and the majority of sulfuric acid, ammonium bisulfate, ammonium

sulfate, ammonium nitrate, organic carbon and elemental carbon is found in this size range.

Particles larger than ~2 or 3 μm are called “coarse particles”; they result from grinding

activities and are dominated by material of geological origin. Pollen and spores also inhabit

the coarse particle size range, as do ground up trash, leaves, and tires. Coarse particles at the

low end of the size range also occur when cloud and fog droplets form in a polluted

environment, then dry out after having scavenged other particles and gases.
2-2

0

2

4

6

8

10

0.01 0.1 1 10 100

Particle Aerodynamic Diameter (microns)

Relative Concentration

Nucleation Accumulation Coarse CondensationDroplet

PM10

PM2.5

TSP

Figure 2.1.1. Idealized size distribution of particles in ambient air (Chow et al., 1995).

Particle size fractions commonly measured by air quality monitors are identified in

Figure 2.1.1 by the portion of the size spectrum that they occupy. The mass collected is

proportional to the area under the distribution within each size range. The Total Suspended

Particulate (TSP) size fraction ranges from 0 to ~40 μm, the PM10 fraction ranges from 0 to

10 μm, and the PM2.5 size fraction ranges from 0 to 2.5 μm in aerodynamic diameter. No

sampling device operates as a step function, passing 100% of all particles below a certain size

and excluding 100% of the particles larger than that size. When sampled, each of these size

ranges contains a certain abundance of particles above the upper size designation of each

range.

Figure 2.1.2 shows typical residence times in the atmosphere for particle sizes within

each size range, based on gravitational settling in mixed and stirred chambers (Hinds, 1982).

Particles in the fine particle (PM2.5) size fraction have substantially longer residence times, and

therefore the potential to affect PM concentrations further distant from emissions sources,

than particles with aerodynamic diameters exceeding 2 or 3 μm. In this regard, fine particles

act more like gases than like coarse particles.
2-3

Figure 2.1.2. Residence times for homogeneously distributed particles of different

aerodynamic diameters in a 100 m deep mixed layer. Gravitational settling is

assumed for both still and stirred chamber models (Hinds, 1982).

Figure 2.1.1 shows the accumulation range to consist of at least two sub-modes, which

is contrary to many other presentations that show only a single peak in this region. Recent

measurements of chemically specific size distributions show these sub-modes in several

different urban areas. John et al. (1990) interpreted the peak centered at ~0.2 μm as a

“condensation” mode containing gas-phase reaction products. John et al. (1990) interpreted

the ~0.7 μm peak as a “droplet” mode resulting from growth by nucleation of particles in the

smaller size ranges and by reactions that take place in water droplets. The liquid water

content of ammonium nitrate, ammonium sulfate, sodium chloride, and other soluble species

increases with relative humidity, and this is especially important when relative humidity

exceeds 70%. When these modes contain soluble particles, their peaks shift toward larger

diameters as humidity increases.

The peak of the coarse mode may shift between ~6 and 25 μm. A small shift in the

50% cut-point of a PM10 sampler has a large influence on the mass collected because the coarse

mode usually peaks near 10 μm. On the other hand, a similar shift in cut-point near 2.5 μm has

a small effect on the mass collected owing to the low quantities of particles in the 1 to 3 μm

size range.
2-4

Figure 2.1.3. Size distributions of several particulate source emissions (Ahuja et al. 1989;

Houck et al., 1989, 1990).

Six major components account for nearly all of the PM10 mass in most urban areas: 1)

geological material (oxides of aluminum, silicon, calcium, titanium, and iron); 2) organic

carbon (consisting of hundreds of compounds); 3) elemental carbon; 4) sulfate; 5) nitrate; and

6) ammonium. Liquid water absorbed by soluble species is also a major component when the

relative humidity exceeds ~70%, but much of this evaporates when filters are equilibrated

prior to weighing. Water-soluble sodium and chloride are often found in coastal areas, and

certain trace elements are found in areas highly influenced by industrial sources.

Although total mass measurements are somewhat dependent on the sampling and

analysis methods (Chow, 1995), with reasonable assumptions regarding the chemical form of

mineral oxides and organic species, the mass concentrations of PM10 and PM2.5 can be

reproduced within experimental precision (typically < ±10%) by summing the measured

concentrations of these six chemical components. Comparison of the “reconstructed mass”

from this method to measured total mass, when possible, is recommended as a data validation

technique. Approximately half of PM10 is often composed of geological material. Geological

material often constitutes less than ~10% of the PM2.5 mass concentrations, however, as most

of it is found in the coarse particle size fraction. As shown in Figure 2.1.3 (from Ahuja et al.,

1989; Houck et al., 1989, 1990), most particles emitted by common sources, with the

exception of fugitive dust sources, are in the PM2.5 fraction.
2-5

Dominant Chemical Abundances in Percent Mass

Source Type Particle size < 0.1% 0.1 to 1 % 1 to 10 % > 10 %

Paved Road Dust Coarse Cr, Sr, Pb, Zr

SO4

=

, Na+, K+, P, S, Cl, 

Mn, Zn, Ba, Ti

Elemental Carbon (EC), 

Al, K, Ca, Fe Organic Carbon(OC), Si

Unpaved Road Dust Coarse

NO3

-

, NH4

+

, P, Zn, Sr, 

Ba

SO4

=

, Na+, K+, P, S, Cl, 

Mn, Ba,Ti OC, Al, K, Ca, Fe Si

Construction Coarse Cr, Mn, Zn, Sr, Ba SO4

=

, K+, S, Ti OC, Al, K, Ca, Fe Si

Agricultural Soil Coarse NO3, NH4

+

, Cr, Zn, Sr

SO4

=

, Na+, K+, S, Cl, Mn, 

Ba, Ti OC, Al, K, Ca, Fe Si

Natural Soil Coarse Cr, Mn, Sr, Zn, Ba Cl-, Na+, EC, P, S, Cl, Ti OC, Al, Mg, K, Ca, Fe Si

Lake Bed Coarse Mn, Sr, Ba K

+

, Ti

SO4

=

, Na+, OC, Al, S, 

Cl, K, Ca, Fe Si

Motor Vehicle Fine Cr, Ni, Y

NH4

+

, Si, Cl, Al, Si, P, 

Ca, Mn, Fe, Zn, Br, Pb Cl-, NO3

-

, SO4

=

, NH4

+

, S OC, EC

Vegetative Burning Fine

Ca, Mn, Fe, Zn, Br, Rb, 

Pb NO3

-

, SO4

=

, NH4

+

, Na+, S Cl-, K+, Cl, K OC, EC

Residual Oil Combustion Fine

K

+

, OC, Cl, Ti, Cr, Co, 

Ga, Se NH4

+

, Na+, Zn, Fe, Si V, OC, EC, Ni S, SO4

=

Incinerator Fine V, Mn, Cu, Ag, Sn K

+

, Al, Ti, Zn, Hg

NO3

-

, Na+, EC, Si, S, 

Ca, Fe, Br, La, Pb SO4

=

, NH4

+

, OC, Cl

Coal-Fired Boiler Fine

Cl, Cr, Mn, Ga, As, Se, 

Br, Rb, Zr

NH4

+

, P, K, Ti, V, Ni, Zn, 

Sr, Ba, Pb

SO4

=

, OC, EC, Al, S, 

Ca, Fe Si

Oil-Fired Power Plant Fine V, Ni, Se, As, Br, Ba Al, Si, P, K, Zn

NH4

+

, OC, EC, Na, Ca, 

Pb S, SO4

=

Smelter Fine Fine V, Mn, Sb, Cr, Ti

Cd, Zn, Mg, Na, Ca, K, 

Se Fe, Cu, As, Pb S

Antimony Roaster Fine V, Cl, Ni, Mn SO4

=

, Sb, Pb S None reported

Marine Fine and Coarse

Ti, V, Ni, Sr, Zr, Pd, Ag, 

Sn, Sb, Pb

Al, Si, K, Ca, Fe, Cu, 

Zn, Ba, La NO3

-

, SO4

=

, OC, EC Cl-, Na+, Na, Cl

Table 2.1.1. Chemicals from particles in different emissions sources

The actual chemical components found in a given ambient sample have a strong

correspondence to the chemical composition of the source emissions in the monitored airshed.

Table 2.1.1 (from Chow, 1995) shows the relative abundance of several elements, inorganic

compounds, and carbon from different source types. The most abundant species in air are

also most abundant in source emissions, with the exception of sulfate, nitrate, and ammonium.

Spatial gradients in the concentrations of one or more of these species dominated by a single

source provide a good means of evaluating the zone of influence of that source.

Sulfate, nitrate, and ammonium abundances in directly emitted particles are not

sufficient to account for the concentrations of these species measured in the atmosphere.

Ambient mass concentrations contain both primary and secondary particles. Primary particles

are directly emitted by sources and usually undergo few changes between source and receptor.

Atmospheric concentrations of primary particles are, on average, proportional to the

quantities that are emitted.
2-6

Secondary particles are those that form in the atmosphere from gases that are directly

emitted by sources. Sulfur dioxide, ammonia, and oxides of nitrogen are the precursors for

sulfuric acid, ammonium bisulfate, ammonium sulfate, and ammonium nitrate particles.

“Heavy” volatile organic compounds (HVOC, those containing more than eight carbon atoms)

may also change into particles; the majority of these transformations result from intense

photochemical reactions that also create high ozone levels. Secondary particles usually form

over several hours or days and attain aerodynamic diameters between 0.1 and 1 μm, as shown

in Figure 2.1.1. Several of these particles, notably those containing ammonium nitrate, are

volatile and transfer mass between the gas and particle phase to maintain a chemical

equilibrium. This volatility has implications for ambient concentration measurements as well

as for gas and particle concentrations in the atmosphere.

Ambient concentrations of secondary aerosols are not necessarily proportional to

quantities of emissions since the rate at which they form may be limited by factors other than

the concentration of the precursor gases. Secondary particulate ammonium nitrate

concentrations depend on gaseous ammonia and nitric acid concentrations as well as

temperature and relative humidity. A nearby source of ammonia may cause a localized

increase in PM2.5 concentrations by shifting the equilibrium from the gas to the particulate

ammonium nitrate phase (Watson et al., 1994). Ammonium sulfate may form rapidly from

sulfur dioxide and ammonia gases in the presence of clouds and fogs, or slowly in dry air.

Because fine particle deposition velocities are slower than those of the gaseous precursors,

PM2.5 may travel much farther than the precursors, and secondary particles precursors are

often found far from their emissions sources and may extend over scales exceeding 1,000 km.

Compliance measurements are taken at fixed monitoring sites for specified time

intervals, usually 24 hours. While fixed site monitoring is an effective surrogate for actual

exposure, the air that people breathe depends on where they are, the most common locations

being the home, the workplace, the automobile, and the outdoors. Most outdoor human

exposure occurs during the daytime, so it is important to understand how particle

concentrations differ between day and night.

Figure 2.1.4 shows a clear diurnal cycle of hourly PM 2.5 concentrations measured with

a TEOM during the 26-day IMS95 Winter Study (Chow and Egami, 1997). This plot shows

a distinct diurnal pattern for the 50th and 80th percentile concentrations which is consistent

with emissions estimates and meteorological patterns during the winter in the southern San

Joaquin Valley. Because much of the PM 2.5 is directly or indirectly related to emissions from

motor vehicle exhaust, peaks of PM 2.5 concentrations during the morning and evening rush

hours are expected at urban sites. The evening peak is suspected to be the accumulation of

emissions from motor vehicle exhaust superimposed on domestic cooking and residential

wood combustion contributions. Since transport and mixing are lowest during the cold

evening hours, pollutant concentrations can build up rapidly after sunset and frequently carry

over to the next morning.

Meyer et al. (1992) show a similar diurnal pattern during wintertime in a mountainous

California community where wood is burned, with the evening peak remaining high well past
2-7

Figure 2.1.4. Hourly variations in the 20th, 50th, and 80th percentiles of PM2.5 in

Bakersfield, CA (Chow and Egami, 1997).

midnight. In some communities where fugitive dust is a major emitter, peak PM10 concentrations

may occur during the afternoon when ventilation is good, but high winds raise the dust into the air.

The data in Figure 2.1.4 imply that a person’s maximum outdoor exposure to suspended particles

near the measurement site occurs during morning and evening commuting periods.

A PM sampler location, especially its proximity to local sources, can play a large role

in its ability to assess spatial variability and source contributions. Figure 2.1.5 illustrates the

spatial variability of PM10 mass in a saturation monitoring network in California’s San Joaquin

Valley (Chow and Egami, 1997). During this study, most of the PM10 mass was in the PM2.5

fraction. All scales of representation show the highly variable nature of fine particulate mass

averaged over 24 hours. The variations are most noticeable in the urban areas where the

variability was attributable to residential wood smoke and holiday driving patterns. Figure

2.1.6 shows the difference in PM10 chemical components in the same air basin. Sulfate, nitrate,

and ammonium concentrations in these 24-hour samples are fairly uniform over each scale of

representation. Organic carbon and crustal concentrations are more variable between

measurement locations.
2-8

Figure 2.1.5. PM10 concentrations at different nearby sites centered around Fresno, CA.
2-9

Figure 2.1.6. Spatial variation in 24-hour PM10 chemical compositions from the

neighborhood to regional scale.
2-10

y = 0.9403e-0.0461x

R

2

 = 0.8969

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30 35 40 45

Distance from source (m).

Normalized Concentration

Figure 2.1.7. Normalized PM10 concentrations at increasing distances from an unpaved road

(Watson et al., 1996). Samples were taken at 2 m above ground level.

PM2.5 concentrations are often more homogeneously distributed over space than are

the contributions from coarse-mode, geological sources. There are exceptions, however, as

shown by Chow et al. (1989) in comparing high wood smoke contributions between a

residential and an urban sampling site separated by less than 10 km. The stagnant air

conditions prevailing during high wintertime episodes caused the wood smoke to contribute

nearly 50% of PM10 at the residential site, but to contribute less than 10% of PM10 at the

urban-commercial site. This is also evident in the variability of organic carbon in

Figure 2.1.6.

These spatial variations occur because particles deposit and disperse rapidly with

distance from an emissions source. Figure 2.1.7 shows how PM10 caused by dust emitted by

an unpaved road decreases with downwind distance from the edge of the road. Figure 2.1.2

indicates that deposition over time intervals required to traverse these distances is low, so that

much of the decrease in concentration is probably due to vertical mixing and dispersion.

Outdoor particle mass concentrations, corresponding indoor measurements, and

measurements from personal exposure monitors carried by test subjects are often poorly

correlated. The correspondence between these three types of samples is much better for some

chemical species, such as sulfate. When indoor concentrations were apportioned to sources in
2-11

Riverside, CA (Pellizari et al., 1993), particle loadings in outdoor air accounted for more than

60% of the indoor PM2.5. Particles from smoking, cooking, house dust, and other indoor

emissions constituted the remainder of indoor concentrations.

The lack of correlation between indoor and outdoor measurements does not mean that

outdoor concentrations are unimportant. While residents can control indoor emissions

through personal actions such as using filtered vacuum cleaners and exhausting cooking

emissions, there is little that they can do to prevent the incursion of pollution from outdoor

air. Smaller particles, such as PM2.5, are more likely to penetrate indoors than are the coarse

particles, which are more likely to deposit within the cracks and seams where air penetrates.

Coarse particles also deposit to surfaces more rapidly due to gravitational settling in the stilled

air of most indoor environments.

Most of the evidence relating ambient measurements of suspended particles taken in

compliance networks to personal exposures shows that: 1) ambient concentrations, especially

those for PM2.5 particles, constitute a major fraction of the particles to which humans are

exposed; and 2) ambient levels generally represent a lower bound on the concentrations to

which people are commonly exposed.

2.2 Concepts

Several new concepts are explicit or implicit in the new standards and their

implementation. These relate to how particle concentrations vary over a monitored area, how

measurements correspond to population levels, and how nearby and distant sources affect

measurement locations.

2.2.1 Spatial Uniformity

Spatial uniformity is the extent to which particle concentrations vary over a specified

area. It is expressed as a spatial coefficient of variation of measured concentrations from

many samplers in an area and as the deviation of measurements taken by a single sampler from

the spatial average of all samplers. An annual coefficient of variation (standard deviation

divided by the mean) of less than 10%, and a 20% maximum deviation of a single sampler

from the mean, are desirable indicators of spatial uniformity for determining compliance with

standards. This translates into an annual spatial standard deviation of no more than 1.5 μg/m3,

and maximum deviations of no more than 3 μg/m3, at concentrations near the annual PM2.5

standard of 15 μg/m3.

2.2.2 Receptor Site Zone of Representation

PM10 and PM2.5 concentrations measured at any receptor result from contributions of

emissions from nearby and distant sources and the zone of representation of a monitoring site

depends on the relative amounts contributed by sources on different spatial scales. The

dimensions given below are nominal rather than exact and are presented as defined in 40 CFR

part 58. They indicate the diameter of a circle, or the length and width of a grid square, with a

monitor at its center.
2-12

• Collocated Scale (1 to 10 m): Collocated monitors are intended to measure the

same air and involve separations of 1 to 5 m between samplers. Collocated

measurements should not differ by more than the operational precision of the

monitoring method. Monitors are operated on collocated scales to evaluate the

equivalence of different measurement methods and procedures and to quantify the

measurement accuracy and precision of the same measurement methods and

procedures. The distance between collocated samplers should be large enough to

preclude the air sampled by any of the devices from being affected by any of the

other devices, but small enough so that all devices obtain air containing the same

pollutant concentrations.

• Microscale (10 to 100 m): Microscale monitors show significant differences

between PM2.5 monitors separated by 10 to 50 m. This often occurs when

monitors are located right next to a low-level emissions source, such as a busy

roadway, construction site, wood stove chimney, or short stack. Compliance

monitoring site exposure criteria intend to avoid microscale influences even for

source-oriented monitoring sites. A microscale zone of representation is primarily

useful for studying emissions rates and zones of influence, as illustrated in

Figure 2.1.7.

• Middle Scale (100 to 500 m): Middle-scale monitors show significant

differences between locations that are ~0.1 to 0.5 km apart. These differences may

occur near large industrial areas with many different operations or near large

construction sites. Monitors with middle-scale zones of representation are often

source-oriented, used to determine the contributions from emitting activities with

multiple, individual sources to nearby community exposure monitors.

• Neighborhood Scale (500 m to 4 km): Neighborhood-scale monitors do not

show significant differences in particulate concentrations with spacing of a few

kilometers. This dimension is often the size of emissions and modeling grids used

in large urban areas for PM source assessment, so this zone of representation of a

monitor is the only one that should be used to evaluate such models. Sources

affecting neighborhood-scale sites typically consist of small individual emitters,

such as clean, paved, curbed roads, uncongested traffic flow without a significant

fraction of heavy-duty vehicles, or neighborhood use of residential heating devices

such as fireplaces and wood stoves.

• Urban Scale (4 to 100 km): Urban-scale monitors show consistency among

measurements with monitor separations of 10’s of km. These monitors represent a

mixture of particles from many sources within the urban complex, including those

from the smaller scales. PM measurements at urban-scale locations are not

dominated by any particular neighborhood, however. Urban-scale sites are often

located at higher elevations and away from highly traveled roads, industries, and

residential heating. Monitors on the roofs of two- to four-story buildings, in the

urban core area, are often good representatives of the urban scale.
2-13

• Regional-Scale Background (100 to 1,000 km): Regional-scale background

monitors show consistency among measurements for monitor separations of a few

hundred kilometers. Background concentrations are often more consistent for

specific chemical compounds, such as sulfate or nitrate, than they are for PM mass

concentrations. Regional-scale PM is a combination of naturally occurring aerosol

from windblown dust and marine aerosol as well as particles generated in urban

and industrial areas that may be more than 1,000 km distant. Regional-scale sites

are best located in rural areas away from local sources, and at higher elevations.

National parks, national wilderness areas, and many state and county parks and

reserves are appropriate areas for regional-scale sites. Many of the IMPROVE

sites characterize PM regional scale background in different regions of the U.S.

• Continental-Scale Background (1,000 to 10,000 km): Continental-scale

background monitors show little variation even when they are separated by more

than 1,000 km. They are hundreds of kilometers from the nearest significant

emitters. Though these sites measure a mixture of natural and diluted manmade

source contributions, the manmade component is at its minimum expected

concentration. The Jarbidge Wilderness IMPROVE site in northern Nevada is a

good example of a continental-scale background site for PM in North America.

• Global-Scale Background(>10,000 km): Global-scale background monitors are

intended to quantify concentrations transported between different continents as

well as naturally-emitted particles and precursors from sea spray, volcanoes, and

windblown dust. Yellow sand from China has been detected at the Mauna Loa,

HI, laboratory (Darzi and Winchester, 1982; Braaten and Cahill, 1986), and red

dust from Africa’s Sahara desert has been detected at Mt. Yunque, PR. Other

global-scale sites include McMurdo, Palmer, and Ahmundson-Scott stations in

Antarctica (Lowenthal et al., 1996), Pt. Barrow, AK, and Mace Head, Ireland.

2.2.3 Community-Oriented Monitoring

Community-oriented (core) monitoring sites are beyond the zone of influence of a

single source, and should have neighborhood- to urban- scale zones of representation. The

principal purpose of community-oriented monitoring sites is to approximate the short-term

and long-term exposures of large numbers of people where they live, work, and play. A

monitor placed at the fence line of an emissions source would not be considered to represent

community exposures, even though there might be residences abutting that fence line. A

monitor placed in the middle of an area adjacent to a source would, however, be deemed a

community exposure monitor for that neighborhood provided that the location represented a

zone of at least 0.5 km in diameter. The fence line monitor might still be operated because it

provides information on how much the nearby source contributes to the community-oriented

site. The data from the fence line monitor would not be used to determine annual NAAQS

compliance, though it might be used to make comparisons to the 24-hour standard or to

design control strategies to bring the area into compliance with the annual NAAQS.
2-14

2.2.4 Background and Regional Transport Monitoring

Background and regional transport (or boundary) monitors are located outside of local

air quality jurisdictions to determine how much of the PM at community-oriented sites derives

from external sources. Background sites are intended to quantify regionally representative

PM2.5 for sites located away from populated areas and other significant emission sources.

Transport sites are intended to measure fine particle contributions from upwind source areas,

or mixtures of source areas, that move into a planning area.

Most planning areas contain at least one substantial metropolitan area. Several of

these also include industrial sources, either concentrated in one or a few districts or dispersed

throughout the planning area. Air quality planning areas also contain less developed areas that

may be distant from the densely populated centers and industrial emitters. These may include

agricultural areas, dormant lands, large parks, wildlife and nature preserves, large military

bases, etc.

Transport sites should be located upwind of planning area boundaries, outside of the

urban-scale zone of influence. For the most part, transport sites are between planning areas,

or between districts containing large emitters (e.g., industrial complexes, isolated point

sources) and a planning area. Measurements from transport sites represent transport into the

planning area only during periods when the wind is from the direction of the external source

area toward the planning area. During other periods, the transport site may also serve the

purposes of a background site, or as a transport site for another planning area. For this

reason, transport site locations are selected to achieve multiple purposes. Meteorological data

needed to evaluate which purposes are being served should be available along with the PM2.5

measurements.

Background monitors are intended to measure PM2.5 concentrations that are not

dependent on upwind sources, although the particles they quantify will be a mixture of natural

and manmade source material. These stations should be distant from identified emitters, and

may be at higher elevations than the urban-scale community exposure monitors. Current

IMPROVE (Interagency Monitoring of Protected Visual Environments) PM2.5 monitoring in

National Parks and Wilderness Areas (Eldred et al., 1990) provides the best examples of

background monitoring sites, but there is a dearth of these sites in the non-western states.

Table 2.2.1 lists the locations of IMPROVE sites and their current measurements.

Properly sited background stations should measure PM2.5 typical of the lowest ambient

concentrations in a state or region. These sites should not be along transport pathways,

though in densely populated or industrialized regions (such as the northeast corridor) a given

sample may or may not be along such a pathway depending on which way the wind is

blowing.

Several background sites may be needed in large and geographically diverse states,

such as California and others in the west, where terrain produces major barriers to
2-15

Name Latitude Longitude Elev (m) AT EX SC A B C D RH SO2 35

Acadia NP 44.3742 68.2622 122 X X X X X X X X

Badlands NP 43.7469 101.9411 730 X X X X

43.8719 102.2308 960 X X X

Bandelier NM, Rim Fire Tower 35.7817 106.2675 1981 X X X X X X X

Big Bend NP 29.3053 103.1772 1052 X X X X X

29.3439 103.2067 1082 X X X

Boundary Waters Canoe Area 47.9467 91.4958 515 X X X X X X X

Bryce Canyon NP 37.6000 112.1667 2530 X X X X

37.4667 112.2278 2710 X

Bridger Wilderness 42.9750 109.7583 2627 X X X X

42.9281 109.7875 2390 X X X

Canyonlands NP 38.4583 109.8217 1814 X X X X X X X

Cape Romain NWR 0.0000 0.0000 34 X X X X X

Chassahowitzka NWR 28.7500 82.5667 0 X X X X X

Chiricahua NM 32.0097 109.3883 1570 X X X X X X X

Crater Lake NP 42.8958 122.1333 1981 X X X X

Craters of the Moon NM 43.4606 113.5622 1815 X

Denali NP 63.7233 148.9675 661 X X X X X

Death Valley NP 36.5086 116.8478 125 X X X

Dome Land 35.7000 118.2000 950 X X

Dolly Sods Wilderness 39.1047 79.4258 1175 X X X X

E.D. Forsythe NWR 39.4681 74.4536 5 X X X X X

Everglades NP 25.3883 0.0000 2 X X

Glacier NP 48.5103 113.9956 975 X X X X

48.5581 113.9375 968 X X X

Great Basin NP 39.0053 114.2158 2060 X X X X X X X

Grand Canyon NP 36.0392 111.8300 2290 X

(Hopi Point Fire Tower) 36.0719 112.1550 2164 X X X X X

35.9964 111.9917 2256 X X X

36.0778 112.1289 1158 X X X X X X X X

Great Sand Dunes NM 37.7083 105.5172 2487 X X X X

Great Smokey Mountains NP 35.6314 83.9422 793 X X X X X X X X

Guadalupe Mountains NP 0.0000 104.8097 1658 X X X X X X X

Haleakala NP 20.8039 156.2850 1097 X

Jarbidge Wilderness 41.9583 115.0847 2400 X

41.8925 115.4250 1889 X X X X X X X

Lassen Volcanic NP 40.5369 121.5725 1756 X X X X

Lye Brook Wilderness 43.1444 73.1289 1010 X X X X

Mammoth Cave NP 37.2178 86.0736 219 X X X X X X X X

Mesa Verde NP 37.1983 108.4903 2165 X X X X

Moosehorn NWR 0.0000 0.0000 40 X X X X

Mount Rainier NP 46.7614 122.1217 421 X X X X X X X

National Capitol Central, D.C. 38.8950 77.0367 9 X X X X

Okefenoke NWR 30.7403 82.1286 38 X X X X X X X X

Petrified Forest NP 35.0772 109.7697 1755 X X X X

34.8983 109.7958 1690 X X X

Pinnacles NM 36.4850 121.1556 335 X X X X

Point Reyes NP 38.1231 122.9083 76 X X X X

Redwood National Seashore 41.5611 124.0828 235 X X X X

Rocky Mountain NP 40.2772 105.5450 2743 X X X X

40.3606 105.5806 2536 X X X

San Gorgonio Wilderness 34.1847 116.9019 1712 X X X X X X X

Saguaro NM 32.1744 110.7364 938 X

Sequoia NP 36.4936 118.8286 521 X X X X X

Shenandoah NP, Big Meadows 38.5219 78.4361 1073 X X X X X X X X

Sipsey Wilderness 34.3431 87.3386 311 X X X X

Tonto NM 33.6339 111.1011 792 X X X X

Upper Buffalo Wilderness 35.8269 93.2056 701 X X X X X X X

Virgin Islands NP 18.3333 64.7942 46 X

Voyageurs NP 48.5878 93.1728 343 X X

Weminuche Wilderness Area 0.0000 0.0000 2758 X X X X

Yellowstone NP, Water Tank 44.5597 110.4000 2469 X X X X X

Yosemite NP, Turtleback Dome 37.7114 119.7044 1605 X X X X X X X

 KEY

AT ambient temperature (non-aspirated) C IMPROVE sampler module C

EX extinction coefficient (transmissometer) D IMPROVE sampler module D

SC scattering coefficient (nephelometer) RH relative humidity sensor

A IMPROVE sampler module A SO2 sulfur dioxide sampler

B IMPROVE sampler module B 35 35 mm camera slides

Table 2.2.1. IMPROVE measurement sites.
2-16

atmospheric flow. Regions lacking IMPROVE monitors should determine the proximity of

National Parks, Wilderness Areas, and State Preserves as candidates for background sites.

Background monitors also contribute to regional visibility goals that are part of other air

quality regulations.

2.2.5 Emissions Zone of Influence

The zone of influence of a source is the distance at which PM from that specific source

contributes no more than 10% of the measured PM concentration. The zone of influence

refers to a specific emitter, rather than to a source category. For example, though suspended

road dust may contribute 50% of PM10 over a wide region, the majority of emissions from a

specific road influence concentrations over a few tens of meters from the emissions point (see

Figure 2.1.7).

The actual size of a zone of influence varies with meteorology, being larger downwind

than upwind, and the nature of the source (point, elevated, area, line, etc.). Zones of influence

are, therefore, expressed as orders of magnitude rather than as exact distances. The concept

is useful for locating community exposure sites that are intended to represent concentrations

for sources with large rather than small zones of influence. Actual zones of influence must be

determined empirically, by spatially dense monitoring networks, or theoretically by applying

air quality and meteorological models.

2.2.6 PM2.5 Sampler Types

Measurement methods applied in PM networks are ground-based and are divided into

three categories: Federal Reference Method (FRM) samplers, Federal Equivalent Method

(FEM) samplers, and other samplers. The non-FRM samplers are distinguished by their level

of similarity in design to Federal Reference Methods (FRM). The further from the FRMs in

design, the more stringent are the requirements for designation of an instrument as an

equivalent method.

• Federal Reference Methods: Federal Reference Methods for PM2.5 are methods

that have been designated as such under CFR 40 Chapter 1 Part 53, having met

design and performance characteristics described in Part 50, Appendix L; Part 53,

Subpart E; and Part 58, Appendix A. Reference method instruments acquire

deposits over 24-hour periods on Teflon-membrane filters from air drawn at a

controlled flow rate through a tested PM2.5 inlet. The inlet and size separation

components are specified by design, with drawings and manufacturing tolerances

published in the Code of Federal Regulations. Most of the other measurement

components and procedures are specified by performance characteristics, with

specific test methods to assess that performance.

• Class I Equivalent Methods: Class I equivalent method instruments maintain the

same measurement principles as reference method instruments, but with minor

design changes. Class I instruments are intended to provide for sequential

sampling without operator intervention at measurement sites that sample every
2-17

day. Testing of design and performance characteristics for Class I instruments is

given in Part 53, Subpart E.

• Class II Equivalent Methods: Class II equivalent method instruments include all

other instruments based on a 24-hour integrated filter sample with subsequent

moisture equilibration and gravimetric mass analysis, but differ substantially in

design from the reference method instruments. More extensive performance

testing is required for a Class II equivalent instrument than for reference or Class I

equivalent instruments. Testing of design and performance characteristics for Class

II methods is given in Part 53, Subpart F.

• Class III Equivalent Methods: Class III equivalent method instruments include

any candidate instruments that cannot qualify as Class I or Class II instruments.

These may either be filter-based integrated samplers not meeting Class I or Class II

criteria, or filter or non-filter based continuous or semi-continuous samplers. Test

procedures and performance requirements for Class III candidate method

instruments will be determined on a case-by-case basis. The testing for these

instruments will be the most stringent, because equivalency to reference methods

must be demonstrated over a wide range of particle size distributions and aerosol

compositions. Other methods include all non-FRM or non-equivalent

measurement methods capable of characterizing fine particles that may not be or

have not yet been classified as an equivalent method. Existing manual and

continuous analyzers are in this category and potentially include the dichotomous

sampler, IMPROVE samplers, nephelometers, beta attenuation monitors, and

Tapered Element Oscillating Microbalances (TEOMs). Such instruments are not

precluded from becoming equivalent on a site-specific, regional or national basis.

2.3 Definitions

Several terms and abbreviations are used throughout this guidance, and in the

specification of the method for determining compliance with the revised standards. These

terms are defined for: 1) theoretical concepts; 2) monitoring boundaries; 3) monitoring

networks; and 4) site types.

2.3.1 Theoretical Concepts

As will be shown in Section 2.4, systematic sampling theory has seldom been applied

to the design of air quality measurement networks. Since monitoring resources are always

finite, trade-offs must be made off between numbers of sites, frequencies of samples, sample

durations, and the quantities measured. As more experience is gained in the design of PM2.5

monitoring networks, the theoretical and empirical basis for network design will become

better established.

• Cost Per Error (CPE) (Borgman et al., 1996): Total cost of sample collection

and analysis divided by estimated error. There is a balance between the cost

savings with fewer sites against the costs of having larger errors.
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• Error Per Cost (EPC) (Borgman et al., 1996): This is the reciprocal of CPE. It

quantifies the statistical uncertainty associated with a given amount of monitoring

resources.

2.3.2 Monitoring Boundaries

The new standards refer to several boundaries. Metropolitan Statistical Areas,

Primary Metropolitan Statistical Areas, Consolidated Metropolitan Statistical Areas, and New

England County Metropolitan Areas are defined by the U.S. Office of Management and

Budget, and these are defined in Appendix B for the 1990 census. Metropolitan Planning

Areas and Community Monitoring Zones are areas with boundaries corresponding to

subdivisions of the statistical areas that are to be defined by each state according to these

guidelines.

• Metropolitan Statistical Area (MSA): MSAs are designated by the U.S. Office

of Management and Budget (OMB) as having a large population nucleus, together

with adjacent communities having a high degree of economic and social integration

with that nucleus. MSA boundaries correspond to portions of counties, single

counties or groups of counties that often include urban and non-urban areas.

MSAs are useful for identifying which parts of a state have sufficient populations

to justify the installation of a compliance monitoring network. Their geographical

extents may be too big for defining the boundaries of Metropolitan Planning Areas

and Community Monitoring Zones.

• Primary Metropolitan Statistical Area (PMSA): PMSAs are single counties or

groups of counties that are the component metropolitan portions of a

mega-metropolitan area. PMSAs are similar to MSAs with the additional

characteristic of having a degree of integration with surrounding metropolitan

areas. A group of PMSAs having significant interaction with each other are

termed a Consolidated Metropolitan Statistical Area (CMSA).

• Consolidated Metropolitan Statistical Area (CMSA): A Consolidated

Metropolitan Statistical Area (CMSA) is a group of metropolitan areas (PMSAs)

that have significant economic and social integration.

• New England County Metropolitan Statistical Area (NECMSA): The OMB

defines NECMAs as a county-based alternative for the city- and town-based New

England MSAs and CMSAs. The NECMA defined for an MSA or CMSA

includes:

– The county containing the first-named city in that MSA/CMSA title (this

county may include the first-named cities of other MSAs/CMSAs as well), and

– Each additional county having at least half its population in the MSAs/CMSAs

whose first-named cities are in the previously identified county. NECMAs are

not identified for individual PMSAs. There are twelve NECMAs, including

one for the Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA and one for
2-19

the Connecticut portion of the New York-Northern New Jersey-Long Island,

NY-NJ-CT-PA CMSA.

• Monitoring Planning Area (MPA): MPAs are defined by the state

implementation plan as the basic planning unit for PM2.5 monitoring. A MPA is a

contiguous geographic area with established, well-defined boundaries. MPAs may

cross state lines and can be further subdivided into Community Monitoring Zones.

A MPA does not necessarily correspond to the boundaries within which pollution

control strategies will be applied. In fact, it is expected that emissions control

regions will be much larger than the MPAs, owing to the superposition of

regional-, urban-, and neighborhood-scale contributions to PM2.5. MPAs may

include aggregates of: 1) counties; 2) zip code regions; 3) census blocks and

tracts; or 4) established air quality management districts. Counties are often much

larger than the most densely populated areas they contain, and some large

metropolitan areas may extend over several counties. Census blocks are very small

and may be unwieldy to manipulate in some large areas. Zip code and census tract

boundaries may be the most manageable units for many areas. These boundaries

vary substantially in geography from one region to another. MPAs normally will

contain at least 200,000 people, though portions of a state not associated with

MSAs can be considered as a single MPA. Optional MPAs may be designated for

other areas of a state. MPAs in MSAs are completely covered by one or more

Community Monitoring Zones.

• Community Monitoring Zone (CMZ): When spatial averaging is utilized for

making comparisons to the annual PM2.5 NAAQS, Community Monitoring Zones

must be defined in the monitoring network description. Otherwise, they may be

used as a more informal manner, as a means to describe the communities

surrounding one or more core monitoring sites. CMZs have dimensions of 4 to 50

km with boundaries defined by existing political demarcations (e.g., aggregates of

zip codes, census tracts) with population attributes. They could be smaller in

densely populated areas with large pollutant gradients. Each CMZ would ideally

equal the collective zone of representation of one or more community-oriented

monitors within that zone. The CMZ, applicable only to PM2.5, is intended to

represent the spatial uniformity of PM2.5 concentrations. In practice, more than

one monitor may be needed within each CMZ to evaluate the spatial uniformity of

PM2.5 concentrations and to accurately calculate the spatial average for comparison

with the annual PM2.5 NAAQS. When spatial averaging is used, each MPA would

be completely covered by one or more contiguous CMZs.

2.3.3 Monitoring Networks

PM2.5 monitoring networks may be new networks or part of existing networks.

Additional sites may be added to existing networks according to this guidance.

• State and Local Air Monitoring Stations (SLAMS): SLAMS are designed and

operated by local air pollution control districts to determine: 1) the highest
2-20

concentrations expected to occur in each MPA; 2) representative concentrations in

areas of high population density; 3) the impact on ambient pollution levels of

significant sources or source categories; 4) general background concentration

levels; 5) the extent of regional pollutant transport among populated areas, and

6) welfare-related impacts in rural and remote areas (i.e., visibility impairment and

effects on vegetation). Only population-oriented SLAMS acquire data for

determining compliance with PM 2.5 standards, and community-oriented (core)

SLAMS acquire data for compliance with the annual PM2.5 standard.

• National Air Monitoring Stations (NAMS): NAMS are long-term monitors to

assess trends and support national assessments and decisions. The NAMS are

intended to be part of a national trends network focusing on community exposure

surveillance. NAMS is a subset of SLAMS, with the majority of sites being used

to determine compliance or non-compliance with standards. Existing PM10 NAMS

should generally be good candidates for PM2.5 monitoring.

• Photochemical Assessment Monitoring Stations (PAMS): PAMS track trends

in ozone precursor emissions, corroborate emission inventories, and support

photochemical modeling. Ozone non-attainment areas classified as serious, severe,

or extreme have PAMS sites that include enhanced monitoring of ozone, ozone

precursors, and surface and upper-air meteorology. PAMS site type 2 represent

the area of maximum O3 precursor concentration and should also represent good

locations for core PM2.5 sites. Though PAMS site type 1 and 4 are intended to be

used for regional-scale ozone assessment, their siting and measurements also apply

to secondary nitrate and organic aerosol formation and they should be considered

as potential PM2.5 monitoring sites, especially for transport and background

monitoring. MPAs with existing PAMS are to install a PM2.5 core site at a

minimum of one PAMS location.

• Interagency Monitoring of Protected Visual Environments (IMPROVE): As

noted above, the IMPROVE network provides long-term measurements of PM2.5

and other visibility-related observables in National Parks and Wildernesses

throughout the U.S. IMPROVE sites, and the data acquired at those sites, may

qualify as background and/or transport sites for PM2.5 networks.

2.3.4 Site Types

Several types of sampling sites, not all of which are designated for determining

compliance with NAAQS, will be part of the PM2.5 measurement networks.

• Community-Oriented (Core) Sites: Community-oriented sites are located where

people live, work, and play rather than at the expected maximum impact point for

specific source emissions. These sites are not located within the microscale or

middle-scale zone of influence of a specific, nearby particle emitter. Communityoriented sites may be located in industrial areas as well as and in residential,
2-21

commercial, recreational, and other areas where a substantial number of people

may spend a significant fraction of their day.

A subset of the core sites are intended to acquire PM2.5 concentrations every day.

These include core SLAMS sites and sites collocated with PAMS sites. Two or

more such core sites are to be operated in MSAs with population greater than

500,000, with at least one additional core site in each PAMS area.

Core sites are used to determine NAAQS compliance for both annual and 24-hour

PM2.5 standards. Because core sites are the only sites eligible for comparison to

both the annual and 24-hour PM2.5 NAAQS, they are the most important sites in

the new PM2.5 network. PM2.5 concentrations may be spatially averaged among

these sites within a CMZ when the annual average PM2.5 at each core site is within

±20% of the spatial average on a yearly basis. Core sites should have a zone of

representation of at least neighborhood scale (> 0.5 km). For a neighborhood

scale, this means that the 24-hour concentrations should vary by no more than ±10

percent within an area whose diameter is between 0.5 and 4 km. For urban scale,

the concentrations would be similar for distances greater than 4 km. In some

monitoring areas, a site with a smaller spatially representative scale (microscale or

middle scale) may be representative of many such small scale sites in the general

area. This site is effectively representative of a larger scale and in accordance with

Appendix D to 40 CFR 58 is also eligible to be called a core site. Sites

representing source areas with small zones of influence (e.g., less than one-tenth

the dimensions of the CMZ) do not qualify for spatial averaging.

The state can use one or more core monitoring sites to define community air

quality for purposes of making comparisons to the annual PM2.5 NAAQS.

Multiple sites would exist within community monitoring zones and must each meet

the eligibility requirements of Appendix D to 40 CFR 58. The elected community

monitoring approach and the description of the CMZs would be contained in the

state’s network description.

• Daily Compliance Sites: Daily compliance sites are used to determine NAAQS

compliance for the 24-hour (daily) PM2.5 standard, but not for the annual standard.

Because a daily compliance site does not necessarily represent community-oriented

monitoring, it may be located near an emitter with a microscale or middle-scale

zone of influence.

The PM monitoring regulations state that any population-oriented site is eligible

for comparison to the 24-hour PM2.5 standard. If the monitoring stie is also

representative of community-wide air quality, it is eligible for comparison to the

annual PM2.5 NAAQS. With a few anticipated exceptions, almost all sites in the

new network will be population-oriented. A site may be population-oriented and

at the same time be source oriented or reflective of maximum concentration. The

same is true for the existing PM10 network.
2-22

Population-oriented sites may be located in hot spot locations and other portions

of the above areas which are likely to invoke exposure to fine particles for at least

part of a 24-hour sampling period. Hot spot locations have a micro or middle

measurement scale of representativeness. Microscale means that the 24-hour

measurements should vary by no more than ±10% within a circle of diameter 100

meters. Middle scale means that the 24-hour measurements should vary no more

than ±10% within a circle of diameter 100-500 meters. These distances are the

area around the monitor which may be different than the distance to the nearest

major influencing source.

Limitations in resources dictate some tradeoffs in the selection of hot spot

locations. Every potential hot spot may not be covered. In general, those

maximum concentration locations most reflective of larger population impact

should be given higher priority in the placement of permanent monitoring stations.

Restrictions in the ability to site permanent monitors is also and important

consideration. It may not be feasible to always establish stations adjacent to

occupied buildings and within recreational settings, because we cannot obtain

permission to use the property or the buildings obstruct the air flow. In such

cases, alternate locations which are representative of population-oriented sites

should be considered.

• Special Purpose Monitors (SPM): SPMs may or may not be used to determine

compliance. Their purpose is to understand the nature and causes of excessive

concentrations measured at compliance monitoring sites. SPMs do not necessarily

use FRMs or FEM methods, and they may be operated over short periods of time

at different locations. SPMs may be discontinued within their first two years of

operation without prejudice when their purpose has been achieved. Typical SPMs

might include: 1) portable saturation monitors operated at many locations around

core sites to determine zones of representation, zones of influence, and spatial

uniformity; 2) sequential samplers with Teflon and quartz filters or absorbing

substrates to determine diurnal distributions of PM chemical components and

precursor gases; and 3) short-time-resolution continuous monitors to determine

diurnal mass concentration changes in response to changes in emission rates and

meteorology. When SPMs use FRM or FEM samplers and satisfy other

requirements of section 58.14a, then they may be used to judge compliance.

However, non-attainment designations will not be based upon the SPM data for

the first two years of their operation.

• Transport Sites: Transport sites are intended assess the effects of emissions

within one MPA or isolated emission sources on other MPAs. To do this, they are

typically located between MPAs, or between non-urban source areas and MPAs.

Meteorological measurements will usually be associated with transport sites.

• NAMS Sites: Subsets of core and transport sites will be selected for long-term

monitoring and will be designated as PM2.5 NAMS for assessing trends and for

performing future epidemiological studies.
2-23

• Background Sites: Background sites are intended to represent regional-scale

PM2.5 concentrations that may be a combination of contributions from several

MPAs and non-urban source areas, as well as natural emissions. These are usually

located in pristine areas, such as National Parks and Wilderness areas, and possibly

at elevations higher than MPAs, but still within the typical mixed layer of the

atmosphere.

2.4 Network Design Philosophies

The design of environmental sampling networks has been studied in hydrology

(Andricevic, 1990; Kassim and Kottegoda, 1991; Woldt and Bogardi, 1992; Meyer et al.,

1994), meteorology (Gandin, 1970), and the geological sciences (Camisani-Calzolari, 1984;

de Marsily et al., 1984; Russo, 1984). Only a few of these concepts have been adapted to air

quality networks. Some of the earliest work done in network design focused on

meteorological observations (Gandin, 1970).

2.4.1 Network Design Objectives

Networks are designed to attain specific objectives. Objectives of the SLAMS PM2.5

monitoring network are (U.S. EPA, 1997b):

• To determine representative concentrations in areas of high population density.

• To determine the impact on ambient pollution levels of significant sources or

source categories.

• To determine general background concentration levels.

• To determine the extent of regional pollutant transport among populated areas;

and in support of secondary standards.

• To determine the highest concentrations expected to occur in the area covered by

the network.

• To determine the welfare-related impacts in more rural and remote areas such as

visibility impairment and effects on vegetation.

Munn (1981) defines two basic methods of network design: 1) the statistical method,

and 2) the modeling method. The statistical method assumes that existing data is available to

extract meaningful statistical information for network design.

The statistical approach is based on the lognormal distribution followed by most air

quality data (Larsen, 1969; Noll and Miller, 1977). Statistical methods take advantage of the

fact that most air quality measurements are correlated either in time at the same location or in

space with other monitors in a network. Networks are optimized by examining time series

correlations from long measurement records or spatial correlations among measurements from
2-24

many nearby monitors (Munn, 1975; Elsom, 1978; Handscombe and Elsom, 1982). Munn

(1981) identifies four types of correlation analysis: 1) time correlation (autocorrelation) at

one site; 2) cross-correlation of several pollutant concentrations at one site; 3) spatial

correlations among simultaneous measurements at different sites; and 4) spatial correlations

among different sites with time lags.

2.4.2 Random Sampling

Random sampling locates sites by chance, without taking into consideration the

sources of pollutants (Nesbitt and Carter, 1996). Random placement is accomplished by

specifying boundaries of a rectangular domain, generating x and y coordinates from a uniformdistribution random number generator truncated at the domain boundaries, and placing

samplers as close to these coordinates as practical.

The advantages of random sampling designs are: 1) measurement bias is minimized;

2) implementation simplicity, with no knowledge assumed about the spatial and temporal

distribution of concentrations; and 3) sampling locations are objectively chosen. The

disadvantages are that: 1) many sampling locations must be allocated for an acceptable

sampling error; 2) there is large potential for redundancy in a network with many locations;

and 3) there is a large risk of poorly representing exposures in a network with few locations.

Borgman et al. (1996) cites an example of how many samplers are required for a

certain confidence interval. If the 95% confidence interval is 1 μg/m3 with a variance, σ

2

, of

6.5 (μg/m3)

2

 the estimated number of samples is found to be

196

1

. s

n

=

and solving for n yields 25 samples. This large number of PM sampling sites would only be

applicable to a very large urban area, or for a short-term special-study.

From a practical standpoint, random network siting is not a useful model for air quality

monitoring. Prior knowledge, though sometimes incomplete, is always available concerning

the sources and meteorology that affect PM concentrations in an area. Sampler siting

constraints of power, security, and minimum separations from nearby emitters and

obstructions impose logistical constraints that prevent a purely “random” selection of

measurement locations. The community exposure monitoring philosophy of the new

standards is not served by a random-sampling network design.

2.4.3 Systematic Sampling

Systematic sampling locates samplers on a grid system, with one sampler assigned to

each grid cell. Noll and Miller (1977) call this type of sampling the “area method”. This

method is most applicable in flat terrain with a few large point sources. Samplers are placed

as close to the center of the cell as practical. This method minimizes sampling bias because of

its regular spacing of sensor locations. However, systematic sampling requires a substantial
2-25

number of samplers depending on the size of the MPA, and most of these samplers supply

redundant information where PM2.5 concentrations are spatially uniform.

Systematic sampling costs may be prohibitively high, even for small areas, except for

short periods during which spatial uniformity is being evaluated. The positive characteristic of

systematic sampling is that the network completely covers the planning area.

2.4.4 Judgmental Sampling

Judgmental sampling (Nesbitt and Carter, 1996) uses knowledge of source emissions

and sensitive receptor locations, coupled with mechanisms for pollutant transport, to locate

measurement sites. Noll and Miller (1977) call this the “source orientation method” and deem

it most appropriate for monitoring point sources in uneven terrain. Air pollution models can

be used to assist in this judgment, but this requires exceptional accuracy of the model

formulation and the model input data. Few areas in the U.S. have good estimates of particle

and precursor gas emissions, especially from mobile and area sources. Complex terrain and

meteorology, as well as simulating secondary aerosol formation, also present challenges to

currently available models for suspended particles.

Judgmental sampler locations may be determined by data from an existing monitoring

network or by identifying the locations of pollutant sources and inferring pollutant transport

from data analysis of emissions and wind measurements. Short-term experiments involving

spatially dense measurements and modeling may assist in making or verifying judgments.

Monitoring networks for criteria pollutants always use judgmental sampling strategies

that consider where source emissions are in relation to populations and which way the wind

blows.

2.4.5 Heterogeneous Siting Strategies

Nesbitt and Carter (1996) combine judgmental and systematic sampling by applying

the following steps: 1) identify potential sources of contamination or “hot spots” using

existing measurements or models; 2) place a grid system over these areas; 3) perform

sampling at these grid points; 4) define a systematic grid at points which yield positive

contamination; 5) use the systematic grid to assess the remainder of the study area.

Figure 2.4.1 shows how a judgmental strategy compares with a combined judgmental

and systematic strategy. The concentration isopleths can be interpolated from spatially dense

measurements or produced by an air quality model. The judgmental strategy, by itself, missed

areas of significant concentrations, while the combined judgmental and systematic strategy

covered the areas of significant concentration that had not previously been monitored.

Another hybrid method for locating potential particulate matter samplers is based on

geostatistical sampling (Journel, 1980; Russo, 1984; Kassim and Kottegoda, 1991;

Trujillo-Ventura, 1991; Rouhani et al., 1992; Borgman et al., 1996). Kriging is a common

method for interpolation to predict unknown values from existing spatial data (Volpi and

Gambolati, 1978; Lefohn et al., 1987; Venkatram, 1988). Kriging uses the correlation
2-26

Figure 2.4.1. Examples of judgmental and hybrid sampling strategies.

structure to produce an estimator with the smallest possible mean square error and results in

reduced sample size compared to other methods.

Most of this guidance is based on judgmental network design, though it is expected

that networks will involve more of the hybrid approach as they are evaluated as future PM2.5

measurements and improved aerosol modeling techniques are developed.

2.4.6 Other Siting Strategies

Other statistical tools to design air quality networks include: 1) the coefficient of

geographic variation (Stalker and Dickerson, 1962; Stalker et al., 1962); 2) structure

functions (Goldstein et al., 1974; Goldstein and Landovitz, 1977); 3) cluster analysis

(Sabaton, 1976); 4) principal component analysis (Peterson, 1970; Sabaton, 1976); 5) the

variational principle (Wilkins, 1971); and 6) linear programming (Darby et al., 1974;

Hougland, 1977).

Modeling relies on a numerical or analytical model to estimate particulate

concentrations in space and time. Because of its nature and sources, PM2.5 is difficult to

model over neighborhood- and urban-scales. As noted above, modeling requires a detailed

emissions inventory over the entire domain. Efforts are being made to archive emissions data

in geographical information systems (GIS).
2-27

Numerical source-oriented models are designed to simulate atmospheric diffusion or

dispersion and estimate concentrations at defined receptors. Numerical source models can be

grouped as kinematic, first-order closure, or second-order closure models (Bowne and

Lundergan, 1983). Kinematic models are the simplest both mathematically and conceptually.

These models simplify the non-linear equations of turbulent motion, thereby permitting a

closed analytical approximation to describe pollutant concentration (Green et al., 1980).

First-order closure models are based on the assumption of an isotropic pollutant concentration

field. Consequently, turbulent eddy fluxes are estimated as being proportional to the local

spatial gradient of the transport quantities. The Eulerian grid models, Lagrangian particle

models, and trajectory puff/plume models are included in this category. Second-order closure

models involve a series of algorithm transformations of the equations of state, mass continuity,

momentum, and energy by using the Boussinesque approximation and Reynold’s

decomposition theory (Holton, 1992; Stull, 1988).

For estimating PM2.5 levels, Eulerian models that include aerosol modules simulating

the physical and chemical processes governing particulate concentrations in the atmosphere

are more suitable than Lagrangian models such as plume trajectory models. Eulerian

three-dimensional models may use either a simplified treatment of atmospheric chemistry

(usually used to address long-term particulate concentrations at urban sites) or include a more

detailed atmospheric chemistry treatment (usually used to simulate only a few days of

episodes due to their compositional cost).

Commonly used long-term Eulerian models with simplified atmospheric processes

include (Seigneur et al., 1997):

• Urban Airshed Model Version V with Linear Chemistry (UAM-V).

• Regulatory Modeling System for Aerosol and Deposition (REMSAD).

• Visibility and Haze in the Western Atmosphere Model (VISHWA).

Commonly used short-term Eulerian models with complex atmospheric processes

include:

• Urban Airshed Model Version V with Aerosols (UAM-AERO),

• Urban Airshed Model with Aerosol Inorganic Module (UAM-AIM).

• SARMAP Air Quality Model with Aerosols (SAQM-AERO).

• California Institute of Technology Model (CIT).

• Gas, Aerosol, Transport, Radiation Model (GATOR).

• Denver Air Quality Model (DAQM).

• Regional Particulate Model (RPM).
2-28

All of the above mentioned Eulerian models have been developed by various scientists

from universities, federal and state agencies, and the private sector. These particulate air

quality models provide a three-dimensional treatment to simulate the fate and transport of

atmospheric contaminants. All of these Eulerian models include gas phase chemistry and

aerosol dynamics and simulate atmospheric inorganics (such as sulfate, nitrate, and

ammonium), but some of these models do not include the treatment of organics

(i.e., REMSAD and UAM-LC).

In cases where secondary aerosols may not be a significant fraction of the PM2.5 mass,

the applicability of these Eulerian models needs to be investigated further. Less complex

Gaussian plume dispersion models such as the Industrial Source Complex Model Version 3

(ISC3) and the Fugitive Dust Model (FDM) will continue to be useful in estimating impacts

from particulate sources.
3-1

3.0 DEFINING STATE PLANNING AREAS

This section specifies the steps to define the boundaries of Monitoring Planning Areas

(MPAs) for determining compliance with PM10 and PM2.5 standards. This procedure requires

the spatial examination of population statistics, topography, existing PM networks, past

measurements, emissions densities, pollution transport patterns, and existing planning areas.

The procedure gives preference to maintaining existing planning areas as MPAs for PM2.5

and for adapting existing sites to PM2.5 compliance monitoring. It also provides an objective

means for identifying PM10 measurement locations that can be discontinued as PM10

compliance monitors.

Two examples, from Birmingham and Jefferson County, AL, and from California's

San Joaquin Valley, are used to illustrate the application of the approach for selecting MPAs,

optional CMZs, and sampling sites. These eastern and western areas show several examples

of complications and solutions that might be encountered in following these guidelines.

These examples are given for illustrative purposes only, using data from the public domain

obtained from the sources identified in Appendix A. It is not intended that these examples

should be used as the basis for re-design of existing PM networks in either of these areas.

The following steps define the MPAs:

1. Identify Political Boundaries of Populated Areas: Plot populated entities

(MSAs, PMSAs, counties, zip code areas, census tracts, or census blocks).

Identify where the majority of the people live. Identify a grouping of populated

entities that define a contiguous area and designate this as an initial MPA.

According to the new regulations, MPAs are required to correspond to all

metropolitan statistical areas with populations greater than 200,000. The

regulations also state that the MSA boundaries do not necessarily have to

correspond to the proposed MPA, and that air planning district boundaries may be

used.

2. Identify Natural Air Basins: Compare outer boundaries of the initial MPA on a

topographic map showing terrain that might engender trapping, channeling, or

separation of source emissions from populated areas. When terrain features are

near the initial MPA boundary, add or subtract population entities to correspond

as closely as possible to the terrain features. When terrain features are significant

within the MPA boundary, identify potential Community Monitoring Zones

(CMZ) that are separated by ridges, lakes, or valleys, or that are bounded on one

edge by a seacoast.

3. Locate Existing Air Quality Monitoring Sites: Plot the locations of existing

PM monitoring sites from NAMS, SLAMS, PAMS, IMPROVE, and special

monitoring networks. Examine the extent to which these correspond to populated

areas. Identify large distances between existing sites, and identify sites that

appear to represent the same sizes of populated areas. Evaluate the justification

for excluding existing sites outside of the initial MPA boundaries. If these are

community oriented sites, extend the initial MPA boundaries with populated
3-2

entities to include these sites. Alternatively, evaluate these sites for potential as

special monitoring, transport or background sites. If existing sites outside of the

MPA do not qualify as any of these, designate these for potential discontinuation

in favor of sites that better attain one of the monitoring objectives.

4. Reconcile Boundaries with Existing Planning Areas: Plot boundaries of

existing planning areas, such as air quality management districts, urban master

plan boundaries, and/or transportation planning regions. Make initial MPA

boundaries correspond to existing planning boundaries. Add or subtract

populated entities to define the MPA as closely as possible to the existing

boundary. Where major adjustments are needed to accommodate existing

planning boundaries, define initial CMZs or general areas for locating core sites

within those boundaries according to the procedure in Section 4.

3.1 Identify Political Boundaries of Populated Areas

Appendix B lists Metropolitan Statistical Areas (MSA) and Primary Metropolitan

Statistical Areas (PMSA) in the United States. Figures 3.1.1 and 3.1.2 show these statistical

areas for the continental U.S. with shading for their populations in 1990 and 1995,

respectively. The 1990 census values are to be used to determine population cut-offs, and in

most cases these do not differ by more than ±10% from the 1995 estimates. Tables 3.1.1 and

3.1.2 are extracts from Appendix B for the states of Alabama and California, respectively.

The MSAs and PMSAs are named after the most populated cities or counties and are

intended to include the economic influence of a population center. Their boundaries may

correspond to county or municipal borders.

In Alabama, the MSAs range from ~1,500 km2 to 8,000 km2, with population

densities of ~40 to 100 people/km2. This is typical of many eastern states, where the counties

are relatively small compared to those of the west. In California, on the other hand, the

MSAs range from ~1,000 km2 to >20,000 km2, with 1990 population densities from 25

people/km2 to >1,100 people/km2. The most extreme cases in Appendix B are: 1) the Las

Vegas MSA that covers more than 100,000 km2 and includes Nye, Clark, and Mohave

Counties, among the largest counties in the U.S.; and 2) the Jersey City PMSA that includes

only 120 km2 of Hudson County with one of the highest U.S. population densities (>4,500

people/km2

). More than 95% of the population in the Las Vegas MSA lives in the southern

portion of Clark County, occupying less than 5% of the MSA land area, while the Jersey City

PSMA has high population density throughout. While the majority of the MSAs remained in

the same categories from 1990 to 1995, there are several that exceeded 200,000 in population

by the year 1995. The Las Vegas MSA continued to grow and changed from a >500,000

category to a >1 million category by 1995.

Countywide population maps and MSA designations are most useful for identifying

those parts of a state that are not required to perform community exposure monitoring.

MSAs are not useful for defining the boundaries of MPAs in most cases. Figure 3.1.1 and

Appendix B show a wide variation in populations among the MSAs. A large number of

these had less than 500,000 people in them during 1990, and these are mostly in the

non-coastal western states. There are many small but highly populated MSAs along the east
3-3

Figure 3.1.1. Metropolitan Statistical Areas and Primary Metropolitan Statistical Areas in the continental U.S. with 1990 populations.
3-4

Figure 3.1.2. Metropolitan Statistical Areas and Primary Metropolitan Statistical Areas in the continental U.S. with 1995 populations.
3-5

State Metropolitan Area TYPE Counties

1990 

Population

1995 Est. 

Population

1995 pop 

density 

(km-2) Area (km2)

AL Anniston, AL MSA Calhoun County 116,034 117,263 74.4 1576.0

AL Birmingham, AL MSA Blount County 840,140 881,761 106.8 8,255.0

Jefferson County

St. Clair County

Shelby County

AL Decatur, AL MSA Lawrence County 131,556 139,837 42.3 3304.0

Morgan County

AL Dothan, AL MSA Dale County 130,964 134,368 45.4 2956.6

Houston County

AL Florence, AL MSA Colbert County 131,327 136,184 41.6 3274.0

Lauderdale County

AL Gadsden, AL MSA Etowah County 99,840 100,259 72.4 1385.2

AL Huntsville, AL MSA Limestone County 293,047 317,684 89.3 3556.2

Madison County

AL Mobile, AL MSA Baldwin County 476,923 517,611 70.6 7329.4

Mobile County

AL Montgomery, AL MSA Autauga County 292,517 315,332 60.6 5199.3

Elmore County

Montgomery County

AL Tuscaloosa, AL MSA Tuscaloosa County 150,522 158,732 46.2 3432.4

Table 3.1.1. Alabama Metropolitan Statistical Areas and Primary Metropolitan Statistical Areas.
3-6

State Metropolitan Area TYPE Counties

1990 

Population

1995 Est. 

Population

1995 pop 

density 

(km-2) Area (km2)

CA Bakersfield, CA MSA Kern County 543,477 617,528 29.3 21086.7

CA Chico-Paradise, CA MSA Butte County 182,120 192,880 45.4 4246.6

CA Fresno, CA MSA Fresno County 755,580 844,293 40.2 20983.3

Madera County

CA Los Angeles-Long Beach, CA PMSA Los Angeles County 8,863,164 9,138,789 869.1 10515.3

CA Los Angeles-Riverside-Orange County, CA CMSA Los Angeles County 14,531,529 15,362,165 174.4 88080.4

Orange County

Riverside County

San Bernardino County

Ventura County

CA Orange County, CA PMSA Orange County 2,410,556 2,563,971 1253.6 2045.3

CA Riverside-San Bernardino, CA PMSA Riverside County 2,588,793 2,949,387 41.8 70629.2

San Bernardino County

CA Ventura, CA PMSA Ventura County 669,016 710,018 148.5 4781.0

CA Merced, CA MSA Merced County 178,403 194,407 38.9 4995.8

CA Modesto, CA MSA Stanislaus County 370,522 410,870 106.1 3870.9

CA Redding, CA MSA Shasta County 147,036 160,940 16.4 9804.8

CA Sacramento, CA PMSA El Dorado County 1,340,010 1,456,955 137.8 10571.3

Placer County

Sacramento County

CA Yolo, CA PMSA Yolo County 141,092 147,769 56.4 2622.2

CA Salinas, CA MSA Monterey County 355,660 348,841 40.5 8603.8

CA San Diego, CA MSA San Diego County 2,498,016 2,644,132 242.8 10889.6

CA Oakland, CA PMSA Alameda County 2,082,914 2,195,411 581.5 3775.7

Contra Costa County

CA Sacramento-Yolo, CA CMSA El Dorado County 1,481,220 1,604,724 121.1 13250.4

Placer County

Sacramento County

Yolo County

Table 3.1.2. California Metropolitan Statistical Areas and Primary Metropolitan Statistical Areas.
3-7

State Metropolitan Area TYPE Counties

1990 

Population

1995 Est. 

Population

1995 pop 

density 

(km-2) Area (km2)

CA San Francisco, CA PMSA Marin County 1,603,678 1,645,815 625.7 2630.4

San Francisco County

San Mateo County

CA San Francisco-Oakland-San Jose, CA CMSA Alameda County 6,249,881 6,539,602 341.1 19173.7

Contra Costa County

Marin County

San Francisco County

San Mateo County

Santa Clara County

Santa Cruz County

Sonoma County

Napa County

Solano County

CA San Jose, CA PMSA Santa Clara County 1,497,577 1,565,253 468.0 3344.3

CA Santa Cruz-Watsonville, CA PMSA Santa Cruz County 229,734 236,669 205.0 1154.6

CA Santa Rosa, CA PMSA Sonoma County 388,222 414,569 101.6 4082.4

CA Vallejo-Fairfield-Napa, CA PMSA Napa County 451,186 481,885 117.6 4097.5

Solano County

CA San Luis Obispo-Atascadero-Paso Robles, CA MSA San Luis Obispo County 217,162 226,071 26.4 8558.6

CA Santa Barbara-Santa Maria-Lompoc, CA MSA Santa Barbara County 369,608 381,401 53.8 7092.6

CA Stockton-Lodi, CA MSA San Joaquin County 480,628 523,969 144.6 3624.5

CA Visalia-Tulare-Porterville, CA MSA Tulare County 311,921 346,843 27.8 12495.0

CA Yuba City, CA MSA Sutter County 122,643 136,104 42.6 3193.9

Yuba County

Table 3.1.2 (continued). California Metropolitan Statistical Areas and Primary Metropolitan Statistical Areas.
3-8

coast, in the upper midwest, and along the gulf coast. California dominates the west coast

with the largest number of and most populated MSAs.

Figure 3.1.3 shows a continental U.S. map of federal lands that are generally low in

population. While these are not of interest for community-oriented monitoring, many of

them are good candidates for background monitoring sites. Currently operating stations from

the IMPROVE network are plotted on this map, and these provide the first preference for

background sites. While the western states have an abundance of these pristine areas, and a

long history of IMPROVE background monitoring, the coverage in midwestern, eastern, and

southern states is sparse.

Counties, zip code areas, census tracts, and census blocks have population attributes

that qualify them as populated entities. These boundaries are available from the 1990 U.S.

census that also contains 1990 and 1995/1996 population estimates associated with each

entity. Population estimates and 1990 census data are available from the U.S. Census Bureau

in electronic and paper formats. See Appendix A for sources of population data. Figure

3.1.4 shows these populated entities in the Birmingham, AL MSA. This MSA consists of

four counties, but Blount and St. Clair counties in the upper right of the MSA have no

principal cities and small populations. More than 80% of the people in the MSA live in

Jefferson County, in and around the principal cities noted in Figure 3.1.4a. The largest and

most central of these cities is Birmingham, the largest city in Alabama.

Figure 3.1.4b shows zip code boundaries in Jefferson and Shelby counties; these are

more dense and of smaller size in and around the city of Birmingham. Five-digit zip codes

may be associated with a few hundred people in rural areas, or with tens of thousands of

people in urban areas. Figure 3.1.4c shows census tracts, each containing from 1,000 to

8,000 people, for both counties. These are very small, and often highly populated, in the

urban area of south-central Jefferson County, but they become larger and less densely

populated toward the north, east, and west edges of the county. Finally, Figure 3.1.4d shows

the boundaries for census blocks. Census blocks are subsets of the census tracts, and may

contain from 500 to 5,000 people. Their small sizes in the populated area, and their

comparable sizes to the census tracts in the less populated periphery of Jefferson County,

makes census blocks less desirable than census tracts for defining MPAs, CMZs or general

areas for community-oriented monitoring in this MSA.

From these figures, it appears that census blocks provide more population detail than

is needed for defining an MPA. Zip code boundaries provide reasonable distributions except

at the edges of a potential MPA. Census tracts are probably the most practical units of

population to define political boundaries for the Birmingham MPA. In Birmingham, AL, the

Jefferson County boundaries provide the first estimate of the MPA, with some of northern

parts of Shelby County that abut the Birmingham metropolitan area. As will be seen below,

county boundaries are not good starting points for California’s San Joaquin Valley.

3.2 Identify Natural Air Basins

In many states, including Alabama and California, political boundaries do not

necessarily correspond to terrain features that may trap or channel source emissions or
3-9

Figure 3.1.3. National parks and monuments, national wildlife refuges, national forests, Indian reservations, and IMPROVE

background monitoring sites.
3-10

Figure 3.1.4. Populated entities in the Birmingham MSA: a) counties, b) zip codes, c) census tracts, and d) census blocks.
3-11

separate emissions from populations. These terrain features may be larger than the single

populated area that represents an MPA, or there may be several terrain features that affect

concentrations within an MPA. USGS maps with scales of 1:250000, 1:50000 and 1:24000

are useful defining these boundaries. Smaller scale (1:250,000) maps are readily available in

electronic format. See Appendix A for sources. This scale is marginally adequate for

identifying Monitoring Planning Areas.

Figure 3.2.1 shows the Birmingham MSA in relation to the terrain of the state of

Alabama. Alabama is relatively flat toward the south, with the southwestern end of the

Appalachian mountain range penetrating into its northeast corner as far as Jefferson County.

Birmingham and its neighboring cities are situated along the narrow valleys that constitute

the end of this range. These northwest to southeast valleys are separated by ridges that

barely attain 300 m in height above the valley floors, and people live and work both within

the valleys, on the hillsides, and on the ridges. The populated entities in Figure 3.1.3 can be

seen to follow this terrain, as do the major transportation corridors.

The Opossum Valley, just to the north of downtown Birmingham, contains a large

industrial complex that extends nearly 40 km to the northeast and southwest from the most

densely populated entities. These industries are interspersed with residences in the Opossum

Valley, and lie just north of low ridges that separate Opossum from the valleys to the south.

The hills are low enough that they probably do not channel local flows, except possibly

during night or morning when temperature inversions might induce shallow mixed layers.

Table 3.1.1 shows few other highly populated areas in Alabama. Mobile, AL, is on the gulf

coast and it is unlikely to have a major influence on pollution in Birmingham. Huntsville to

the north and Montgomery to the south have ~300,000 people in their MSAs and little heavy

industry. Much of the area between cities is forested or occupied by small farms.

Precipitation is abundant, and there is little bare land within the state. The Birmingham MSA

may be affected by a superposition of contributions from regional-scale emitters in the

southeastern U.S. and urban-scale and neighborhood-scale sources within the MPA.

The San Joaquin Valley (SJV) in central California, shown in Figure 3.2.2, is a

significant contrast to Birmingham, AL. This is a complex region, from an air quality and

meteorological perspective, owing to its proximity to the Pacific Ocean, its surrounding

terrain that affects air flows, its diversity of climates, and its large population centers

separated by vast areas of intensively cultivated farmland. Central California contains nearly

half of the state’s 32 million people.

The SJV encompasses nearly 64,000 square kilometers and contains a population in

excess of 3 million people. The majority of this population is centered in the large urban

areas of Bakersfield, Fresno, Modesto, and Stockton, though there are nearly 100 smaller

communities in the region. The San Francisco Bay area, with more than 6 million people, and

a much higher population density than that of the SJV, is generally upwind during non-winter

months.

The SJV is bordered on the west by the Coast Mountain range, rising to 1,530 meters

(m) above sea level (ASL), and on the east by the Sierra Nevada range with peaks exceeding
3-12

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ÑÑ Ñ Ñ

Ñ

Ñ

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Ñ Ñ Ñ Ñ

Ñ

Ñ

Ñ

Ñ

Ñ

Ñ

ÑÑ

Ñ Ñ Ñ

30 0 30 60 Kilometers

Figure 3.2.1. The Birmingham MSA in relation to counties, principal cities (+), and terrain

in Alabama.
3-13

Ñ

Ñ Ñ

Ñ

Ñ

Ñ Ñ

Ñ Ñ

Ñ Ñ Ñ Ñ

Ñ

Ñ Ñ

ÑÑ

Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ

Ñ ÑÑÑÑ

Ñ Ñ

Ñ Ñ

Ñ

Ñ

Fresno, CA

MSA boundaries

Ñ cities

county

80 0 80 160 Kilometers

N

W E

S

Figure 3.2.2. Central California MSAs in relation to counties, principal cities (+), and

terrain.
3-14

4,300 m ASL. These ranges converge at the Tehachapi Mountains in the southernmost end

of the valley with mountain passes to the Los Angeles basin (Tejon Pass, 1,256 m ASL) and

to the Mojave Desert (Tehachapi Pass, 1,225 m ASL). These are significant orographic

barriers that can channel flow. There is little heavy industry in the SJV. Agriculture of all

types is the major industry, with oil and gas production and refining, waste incineration,

electrical co-generation, transportation, commerce, and light manufacturing constituting the

remainder of the economy. The climate is arid, with precipitation only in the winter. Bare

land is prevalent throughout the region, especially after harvests and prior to re-planting.

There is much potential for transport between populated areas within the SJV, from outside

of the SJV into the Valley, and from the rural areas to the populated areas.

The populated entities in the SJV are large and extend into the coastal mountains and

the Sierra Nevadas. The most populated areas are on the flat terrain between the two ranges,

and these are in a line following SR 99 on the eastern side of the Valley.

3.3 Identify Existing Air Quality Monitoring Sites

Figures 3.3.1 shows particle monitoring sites that are currently operated, or were

operated in the past, by the Jefferson County Department of Health. Some of these have

been discontinued, but their data should still be evaluated along with the cause for their

termination. The Jefferson County network corresponds well to the populated entities. Sites

are located both within the Opossum Valley, as well as in the southern valleys. The

measurements at the Inglenook site, which is furthest north, and the Leeds Elementary

School site, which is furthest east, are in areas with lower population, and they might be

evaluated as potential background or transport locations, or as monitors in a separate CMZ.

Figure 3.3.2 shows census tracts with past and current PM monitoring sites in the San

Joaquin Valley. The areas with the densest concentrations of tracts have one to three

monitors apiece. There are also several monitoring sites along the southwestern side of the

Valley, in the Sierra Nevadas to the east, and in the Mojave Desert (eastern Kern County).

Several of these sites may be appropriate as source-oriented SPMs, background sites, or

transport sites.

Figures 3.3.3 and 3.3.4 show potential MPAs determine by census tracts for Jefferson

County and the San Joaquin Valley, respectively. Notice that the MPA for Jefferson County

also includes a few of the more densely populated tracts in Shelby County, as this appears to

be an area of growth in residential housing. Notice that three separate MPAs are identified

for the San Joaquin Valley, each corresponding to the most highly populated portions of an

MSA and including existing community-oriented monitoring sites.

3.4 Reconcile Boundaries with Existing Planning Areas

Population entities can be added or subtracted at the edges of initial MPAs to

correspond to existing boundaries, but the MPAs should still correspond to populated areas.

Air pollution control agencies such as the San Joaquin Valley Unified Air Pollution Control

District (SJVUAPCD) are responsible for large geographic areas, several MSAs and several

initial MPAs. These areas have two options for reconciling the MPAs with their boundaries:
3-15

Figure 3.3.1. Populations in Jefferson and Shelby county census tracts. Jefferson County Health Department PM monitoring sites

are shown.
3-16

#S

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BESS

INGL

NOSC FAIR

LESC

TASC

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DOLO

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TARR

NOBS

Figure 3.3.2. Potential Monitoring Planning Area for the Birmingham MSA. Dots represent monitoring sites.
3-17

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TAN TAC

COV

HAN

KCC

LOS

MAS

MLB

MER

SOH

MIS

CLD

MOK

WES

PTV

VCS

Figure 3.3.3. Census tract boundaries and past and present PM monitoring sites in

California’s San Joaquin Valley.
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#

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MIS MOK

WES

SOH

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BAC BAK

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BKA

OLD

a) Stockton/Modesto b) Fresno/Madera/Clovis/Selma

c) Bakersfield

Figure 3.3.4. Potential San Joaquin Valley MPAs: a) Stockton/Modesto,

b) Fresno/Madera/Clovis/Selma, and c) Bakersfield.
3-19

• Several MPAs can be designated within the existing jurisdiction, as shown in

Figure 3.3.4. Areas between or along the edges of these MPAs become target

areas for transport and background monitors, or as SPMs if they are intended to

determine specific source influences.

• The MPA can be defined as identical to the existing jurisdictional boundaries.

The initial MPAs, such as those in Figure 3.3.4, can be designated as one or more

CMZ within the MPA.

In the first option, compliance is determined from SLAMS and other compliance sites

within the MPA portions of the jurisdiction, where the most people are exposed to PM2.5.

Special purpose monitoring between and around these MPAs may be used for source

assessment, and may result in emission reduction requirements outside of the MPAs.

Alternatively, special purpose transport monitors between the MPAs might be appropriate.

In both instances, the data from these SPMs are not necessarily needed for compliance

assessments. In the second option, all areas within the jurisdiction are part of an MPA, and

measurements from any part may be used for determining compliance.

In other cases, the MPA may extend outside of the current boundaries of the air

quality control agency, as for the Birmingham metropolitan area that extends south into

Shelby County, AL. There are two options in this case:

• Designate two adjacent MPAs, with the dividing line at jurisdictional lines. This

has the advantage of making a clean break between the two administrative

agencies, but the disadvantage of complicated coordinated emissions reduction

strategies should the PM2.5 standards be exceeded.

• Designate one MPA, but with separate CMZs (or core monitoring sites) divided

by the jurisdictional line. This has the advantage of allowing monitoring

networks to be administered by the existing air pollution control agencies, while

allowing for more coordinated planning with respect to needed emissions

reductions should the standards be exceeded within different jurisdictions.

The Jefferson County Department of Health has jurisdiction over all air quality

monitoring in the county, but none in Shelby County. Shelby County conducts no PM

monitoring, and it does not maintain an infrastructure for air quality monitoring and

emissions control. These functions are handled by the state for most of the lightly populated

counties in Alabama. In this case, the few northern Shelby tracts in Figure 3.3.2 might be

eliminated from the MPA to keep the MPA entirely within the jurisdiction of Jefferson

County. It is possible that measurements entirely within Jefferson County adequately

represent population exposures just south of the Jefferson County border. This hypothesis

could be tested by short-term SPMs in Shelby County.
3-20

3.5 Summary

Figures 3.3.3 and 3.3.4 show potential MPAs for Birmingham, AL, and for portions

of California’s San Joaquin Valley that can be used as examples for other areas. In the

Birmingham case, the potential MPA is smaller than the entire county and corresponds to the

~100 km long by ~20 km wide swath that cuts through Jefferson County, and extends

partially into Shelby County to the south. It corresponds on its edges to terrain features, but

it also includes several valleys.

In the San Joaquin Valley portion of Central California, three MPAs are defined

within the existing boundaries of the SJVUAPCD for Stockton/Modesto,

Fresno/Madera/Clovis/Selma, and Bakersfield, the most highly populated regions of the

Valley. The detailed population maps of these areas show that there is substantial difference

in population density within the Valley, and even within the proposed MPAs.
4-1

4.0 DEFINING PM2.5 COMMUNITY MONITORING ZONES

Community-oriented monitors and optional Community Monitoring Zones (CMZ)

within MPAs are intended to quantify neighborhood-scale exposures that are added to

underlying urban and regional PM contributions. In this discussion, the term CMZ is used to

represent the specifically defined area required by the regulations when spatial averaging is

intended for making comparisons to the annual PM2.5 NAAQS or a more general area only

used for description of the communities represented by one or more core sites. CMZs are

defined based on terrain, sources, and prior monitoring within and upwind of an MPA. Core

sites and optional CMZs should be reviewed annually to determine whether or not additional

core sites or CMZs are needed or changes to CMZ boundaries are appropriate. General

locations for core sites and CMZs are defined by the following steps:

1. Locate Emissions Sources and Population: Plot major land use within the

populated entities within the categories of commercial, residential, industrial, or

agricultural and the major roadways. Plot emissions from major point sources for

primary PM, sulfur dioxide, and oxides of nitrogen. Use a gridded emissions

inventory or maps of source type and density, if available. Each monitoring site in

the CMZ will principally be affected by similar emission sources. Determine which

populated areas coincide with or are in close proximity to areas of high source

density and which are in areas of low source density. When evaluating community

exposures to emissions, consider populations at work and leisure activities, as well

as at home. Population density is important both for determining exposure and for

estimating emissions from vehicles, cooking, woodburning, etc. Modify initial

CMZ boundaries identified when defining MPAs to better represent exposure to

nearby source emissions from commercial, residential, industrial, and agricultural

emissions.

2. Identify Meteorological Patterns: Plot wind directions and speeds, vertical

temperature structure, and frequencies of fogs by season. Determine how these

vary within and around the initial MPA and CMZs. Extend the dimensions of

CMZs that include large source emissions in the downwind direction, using terrain

as a guide for potential channeling.

3. Compare PM concentrations: Determine the spatial homogeneity of average

and maximum concentrations from previous measurements or model calculations

within the potential CMZ for annual, seasonal, and maximum PM concentrations.

Use measurements of PM2.5 or visibility if available; if not, use PM10 or other air

pollutant measurements. Combine potential CMZs where these concentrations are

similar. When existing PM2.5 measurements are available, the CMZ should be

chosen such that the average concentrations at individual sites does not exceed the

spatial average by more than ±20 percent on a year-by-year basis. Lastly, the

CMZ is defined such that each site is generally well correlated with other sites in

the CMZ on a day-to-day basis (r>0.6).
4-2

4. Adjust CMZs to jurisdictional boundaries: Where air quality management

jurisdictional boundaries are within a natural CMZ, divide the CMZ along these

lines so that a separate CMZ resides within each jurisdiction.

5. Locate Sites: Where existing sites are within each CMZ, give them first priority

of PM2.5 monitoring when they meet the siting criteria in Section 5. Where CMZs

do not contain existing sites, apply the criteria of Section 5 to select new sites.

4.1 Locate Emissions Sources

As noted in Section 3, Jefferson County is highly industrialized in the Opossum Valley,

but contains less industry in the other, adjacent valleys. Several different types of heavy

industries are located in various clusters in the Opossum Valley, so two potential CMZs might

be defined for each end of the MPA in Figure 3.3.2. The commercial central city also

indicates another source area, but it is so close to the Opossum Valley that emissions are very

likely to mix over the low ridges separating them. A third CMZ might be considered for the

downtown area.

In contrast, California’s San Joaquin Valley has little heavy industry. While crude oil

combustion in Kern County to the south was associated with elevated sulfate levels in the

past, this fuel source has been replaced with natural gas that brings countywide sulfur dioxide

emissions down to levels comparable with those of other parts of the Valley. The initial

CMZs are set equal to the MPAs illustrated in Figure 3.3.4, since each consists of mostly

urban source emissions such as road dust, vehicle exhaust, residential wood burning, and

oxides of nitrogen and sulfur dioxide from gasoline and diesel fuel combustion.

The AIRS-AFS database is a useful source for locating local emissions sources. A

downloaded AFS database is usable with a GIS. There may be special cases where the TRIS

inventory may provide species information. The local transit authority may be consulted for

data on diesel fuel usage and bus routing. State Department of Transportation data on heavy

truck registration (especially short haul bulk haulers) can be consulted.

4.2 Identify Meteorological Patterns

Figures 4.2.1 and 4.2.2 show examples of wind transport directions and distances for

different seasons and different times of the day for National Weather Service wind data from

the Birmingham, AL, and Fresno, CA, airports. The vertical axes of these plots represent

distance in the north/south direction while the horizontal axes represent distances in the

east/west direction. The plotted points are the distances and directions that emitted particles

or precursors would travel if they were transported by the measured surface winds.

In Figure 4.2.1, the denser concentration of points in the southwest corner of the

morning and nighttime plots indicates some, but not dominant, channeling through the valleys.

Transport sites should definitely be located to the northeast. The afternoon plots in all

seasons show a greater frequency of large transport distances and no special preference for

transport direction. Wind speeds and transport distances are lowest at night during the
4-3

Winter Morning Afternoon Night

Spring Morning Afternoon Night

Summer Morning Afternoon Night

Fall Morning Afternoon Night

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Figure 4.2.1. Hourly wind transport directions (from N) and distances (km). 1988-92

Birmingham airport winds for winter (Dec-Feb), spring (Mar-May), summer

(Jun-Aug), and fall (Sep-Nov) during morning (0700-1000 CST), afternoon

(1200-1600 CST), and night (2200-0500 CST).
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Winter Morning Afternoon Night

Spring Morning Afternoon Night

Summer Morning Afternoon Night

Fall Morning Afternoon Night

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Figure 4.2.2. Hourly wind transport directions (from N) and distances (km). 1988-92

Fresno airport winds for winter (Dec-Feb), spring (Mar-May), summer

(Jun-Aug), and fall (Sep-Nov) during morning (0700-1000 PST), afternoon

(1200-1600 PST), and night (2200-0500 PST).
4-5

summer in Jefferson County. The implication of this brief meteorological analysis is that

emissions can be transported in many directions, with a slight tendency toward the southwest.

There is no reason to change the dimensions or orientations of the initial CMZs owing to

transport.

Figure 4.2.2 from the San Joaquin Valley shows substantial channeling along the

northwest to southeast axis of the Valley. The frequency and magnitude of transport is

definitely from the northwest to the southeast, except possibly during winter when there are

nearly equal densities of northwest and southeast transport.

These plots show that CMZs might be longer in the southeastern direction, downwind

of source areas such as population centers, than in the northwestern direction.

Other useful displays of meteorological variables relevant to PM transport and

formation are:

• Annual and Seasonal Wind Roses: Wind roses are compass-type plots of the

frequencies of wind speeds and directions over a specified period. They are

another method of representing the transport patterns shown in Figures 4.2.1 and

4.2.2. Wind roses show the dominant direction of near-surface transport. The

directions often correspond to terrain-channeling in mountainous or hilly areas.

These vary with season and time of day.

• Time Series of Hourly Wind Directions and Speeds Corresponding to High

Concentrations: These plots show the magnitudes of hourly wind speeds and

directions as a function of time throughout a day. Since there are many hourly

wind measurements, these are only practical for selected 24-hour periods, usually

those corresponding to high PM concentrations. Very low wind speeds with

variable directions might correspond to a multi-day pollutant build-up in stagnant

air. PM levels under these conditions are often dominated by neighborhood- and

urban-scale emitters. Moderately high wind speeds that only correspond to a high

PM level at one site may indicate contributions from a nearby upwind source.

High wind speeds often dilute pollutant concentrations, but may engender

suspension of fine particle fugitive dust. This dust may remain suspended for a

long time and result in regional scale contributions.

• Vertical Temperature Plots Corresponding to High Concentrations: Where

upper air soundings are available, temperatures as a function of height may be

examined to estimate the depth of the mixed layer. During the winter, especially

when snow is on the ground, intense temperature inversions may persist for several

days in areas that are surrounded by elevated terrain. This allows the accumulation

of urban and neighborhood scale emissions.

• Frequencies of Fogs: Plots of the number of hours during which fog is observed

during the day, which are available from many National Weather Service

summaries, indicate the potential for aqueous-phase conversion of sulfur dioxide to
4-6

sulfate. Reactions in fogs are the only mechanisms by which nearby sulfur dioxide

emissions can transform into significant quantities of sulfate. Much of the sulfate

observed in most locations without frequent fogs results from regional-scale

transport during which slower non-aqueous reactions or reactions in elevated

clouds occur.

4.3 Compare PM Concentrations

Few areas possess sufficient PM2.5 measurements to permit comparisons for the first

selection of CMZs. PM10 measurements are often available, and where these show acceptable

spatial uniformity, it is likely that the PM2.5 would also show homogeneity if it had been

measured at the same locations. When the PM10 measurements are non-uniform among

different sites, however, it may be the case that PM2.5 concentrations are still spatially

homogeneous, owing to the substantial differences in atmospheric residence times and zones

of influence of emissions sources discussed in Section 2.

Several MPAs may have undergone an air quality modeling exercise to estimate PM10

and possibly PM2.5 concentrations for a year or for high PM episodes. These modeled

estimates can also be used in place of or in addition to measurements to further refine CMZs.

As shown in Section 2, PM2.5 is a complex combination of chemical compounds that is

difficult to accurately represent in mathematical models. Emissions rates from area and

mobile sources are often inaccurate, as these often are episodic and based on unknown fuels

and operating conditions. Secondary particle formation depends on many factors that are

often unknown. Transport under low-wind-speed conditions, that often accompany high PM

levels, is not well measured or modeled. Modeling results need to be extensively evaluated

against chemical- and size-specific PM2.5 measurements to establish confidence that they

accurately represent the applicable emissions, meteorological, and transformation processes.

Once the validity of the modeling results has been established, PM2.5 concentration isopleths

can be compared with the initial CMZ boundaries to further improve the homogeneity of the

CMZ.

Table 4.3.1 shows several uniformity measures from the seven PM10 measurement

sites in Jefferson County: 1) Bessemer (BESS) in the southwest corner; 2) North Birmingham

(NOBI) in the Opossum Valley ~0.5 km southwest of a steel-pipe forming plant; 3) Inglenook

(INGL) in the northeast portion of the county; 4) Northside School (NOSC) in downtown

Birmingham; 5) Leeds Elementary School (LESC) in the eastern-most corner of the county;

6) Wyland (WYLA) just northeast of Northside School; and 7) Tarrant Elementary School

which is a few kilometers northeast of the North Birmingham site, but >1 km distant from a

large industrial source complex. These seven sites, for which data are listed in EPA’s AIRS

data base, are fewer than the number of sites listed in the AIRS site log. Several sourceoriented SPMs have been operated over several years in Jefferson County, and these data

should be included in this type of analysis.
4-7

Annual Averages (μg/m3)

Year BESS NOBI INGL NOSC LESC WYLA TASC

1990 33.5 47.6 34.6 40.3 30.7 38.1 37.3

1991 31.9 41.0 30.6 36.7 30.6 33.1 32.0

1992 28.4 38.6 28.5 31.2 27.7 31.4 30.1

1993 28.4 32.7 26.5 29.3 25.3 29.6 27.0

1994 24.8 24.7 27.2 23.7 25.6

1995 27.2 27.6 24.6 27.7

98th Percentile 24-Hour Averages (μg/m3)

Year BESS NOBI INGL NOSC LESC WYLA TASC

1990 62 111 72 77 61 85 76

1991 79 100 75 80 70 78 76

1992 52 91 52 66 52 70 55

1993 58 81 62 69 61 64 58

1994 50 47 58 48 50

1995 56 52 50 57

Spatial Average Statistics (μg/m3)

Spatial Spatial Spatial Max Min Average Average Average Average

Year Average Std COV Average Average +20% –20% +10% –10%

1990 37.4 5.1 13.6 47.6 30.7 44.9 30.0 41.2 33.7

1991 33.7 3.5 10.5 41.0 30.6 40.4 26.9 37.0 30.3

1992 30.9 3.4 11.1 38.6 27.7 37.0 24.7 33.9 27.8

1993 28.4 2.2 7.9 32.7 25.3 34.1 22.7 31.2 25.6

1994 25.2 1.2 4.7 27.2 23.7 30.2 20.2 27.7 22.7

1995 26.8 1.2 4.7 27.7 24.6 32.1 21.4 29.4 24.1

Intersite PM10 Correlation Coefficients (1990-1993, n=226)

BESS NOBI INGL NOSC LESC WYLA TASC

BESS 1.000

NOBI 0.848 1.000

INGL 0.872 0.786 1.000

NOSC 0.916 0.909 0.855 1.000

LESC 0.873 0.809 0.885 0.856 1.000

WYLA 0.811 0.879 0.822 0.834 0.846 1.000

TASC 0.844 0.794 0.933 0.837 0.833 0.799 1.000

Table 4.3.1. Uniformity measures for PM10 in Birmingham.
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The first two sub-sections of Table 4.3.1 show the annual arithmetic averages and 98th

percentile (second highest 24-hour maximum with sixth-day sampling) for these sites. Note

that the North Birmingham, Inglenook, and Wyland sites have no data after 1993. The North

Birmingham hivol size-selective inlet (SSI) monitor was replaced by a continuous TEOM

monitor that acquires hourly PM10 concentrations daily, but this appears under a different

AIRS code and was not extracted with this data set. There are known differences between

TEOM and SSI PM monitors in areas with volatizable aerosol (Chow, 1995). Sudden

changes in year-to-year concentrations might be due to changes in measurement method

rather than as a result of emissions reductions. Only data from the same type of PM10

samplers should be used in the analysis of prior data to select CMZs.

There are also changes in past data owing to emissions reductions. The Jefferson

County data in Table 4.3.1 clearly shows the effects of stringent regulations on industrial

emissions since 1990. The NOBI source-oriented site PM10 concentrations were very

different from the annual average and 98th percentile concentrations at other sites during 1990,

but by 1993 they were much more similar to those at the other sites. In 1994, the INGL site

in the northeast corner of Jefferson County, and the LESC site in eastern Jefferson County

had similar average and 98th percentile PM10 levels. In 1995, the BESS, NOSC, and TASC

stations near the center of the MPA show almost identical annual averages, and 98th percentile

PM10 concentrations that differ by no more than 6 μg/m3. The LESC site shows ~3 μg/m3

lower annual PM10 average, and a separate CMZ could be defined around this site.

Alternatively, the MPA might be defined to be smaller than that represented in Section 3 for

the Birmingham MSA, and the LESC site might be considered as a background or transport

site.

The third segment of Table 4.3.1 shows how spatial averages of annual averages at the

different Jefferson County sites vary from year to year. Notice that the spatial standard

deviation decreased from 5.1 μg/m3 in 1990 to 1.2 μg/m3 in 1994. This resulted from the

decrease in concentrations at the NOBI source-oriented site, and its elimination after 1993.

Even in 1993, however, the spatial coefficient of variation (COV) was less than 10% when the

NOBI site was included in the average.

The final panel of Table 4.3.1 shows the spatial correlation coefficients among the

different sites for the 1990 through 1993 periods when data were available from each one.

Each of these exceeds 0.8, with the exception of the NOBI site. This shows that the

information content of the different monitoring locations is similar, and that some PM10 sites

can be sacrificed in favor of collocated PM2.5 sites at most of the Jefferson County sites.

Other analyses of historical PM10 and PM2.5 that provide a basis for selecting CMZs,

and also serve as a justification for de-commissioning PM10 sites in favor of PM2.5 sites are:

• Spatial Plots of Maximum, Annual and Seasonal Average PM: These consist

of pies or bars with areas or heights corresponding to PM concentration on a map.

They can be displayed on the maps of source emissions in conjunction with the

meteorological plots to gain a better understanding of source Zones of Influence

and receptor Zones of Representation.
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• Time Series Plots of PM Mass and Selected Chemical Concentrations: These

consist of single or stacked bars of concentrations for each day. The chemical

concentrations provide an indication of the types of regional, urban, or local

sources that might be contributing.

• Pollution Roses for Hourly PM Concentrations: Pollution roses show the

average concentration associated with a specific wind direction. These are only

practical and useful when hourly data are available from an hourly PM monitor.

Bias toward a specific direction may indicate an overwhelming influence from a

nearby source. The sampling site may be judged as unrepresentative of the CMZ.

The CMZ boundaries are adjusted to include locations that show PM10 concentrations

varying together. Sampling sites that show substantial deviations from other sites in the area

are identified and reasons for their deviation is sought. These sites are excluded from

consideration as core sites if they do not have neighborhood- or urban-scale zones of

representation.

CMZ boundaries are adjusted to include contiguous groups of measurements that

show a reasonable degree of spatial homogeneity, as indicated by the various homogeneity

measures in the analyses above.

4.4 Adjust CMZs to Jurisdictional Boundaries

Just as the MPAs give preference to existing jurisdictional boundaries, the CMZ

definitions may also conform to these boundaries as long as they consist of defined populated

entities. These may include municipal borders or planning districts. An example has already

been given in Section 3. A single MPA might include portions of Jefferson and Shelby

Counties with two CMZs. The Jefferson County CMZ would be monitored by the Jefferson

County Health Department. The Shelby County CMZ would be monitored by the State of

Alabama. On the other hand, a special monitoring study might show that measurements in

Jefferson County also apply to population exposures in the more densely populated portion of

Shelby County, thereby eliminating the need for an additional CMZ.

4.5 Locate Sites

There are two options for the community-oriented monitoring approach for making

comparisons to the annual PM2.5 NAAQS. The network can either be constructed in terms of

using: 1) individual community-oriented core sites; or 2) taking the spatial average of two or

more eligible core sites in a well defined community monitoring zone. Existing sites within a

CMZ are evaluated against the PM siting criteria in Section 5. Sites that do not meet those

criteria for neighborhood or urban zones of representation are eliminated as potential

compliance monitoring sites for comparison to annual standards, though they may be

designated as daily compliance sites or SPM sites. Core PM 2.5 sites should include: 1) a

population-oriented site with the highest expected community-oriented concentrations; 2) a

site with high population density with poor air quality (high population exposure); and 3) a

site collocated at a PAMS site, if the MPA is a PAMS area.
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MSA Population

Number of Core

PM2.5 SLAMS

200,000 to 500,000 1

500,000 to 1 million 2

1 million to 2 million 3

2 million to 4 million 4

4 million to 6 million 6

6 million to 8 million 8

> 8 million 10

Table 4.5.1. Number of required core PM2.5 SLAMS monitors per MSA.

If a PAMS station is located in an CMZ and attains the neighborhood or urban criteria,

this is selected as the first monitor in the CMZ. The preference is a PAMS site type #2 which

is representative of the area of maximum ozone precursor concentrations. If there is no

PAMS site within the CMZ, the existing site with the highest PM measurements or modeled

concentrations that is determined to have a neighborhood or urban zone of representation in a

populated area is selected.

The next site to be added is one in an area with high population and poor air quality.

For these selections, existing PM10 NAMS sites should be given prime consideration. In

addition, each state should have at least one core site for regional transport monitoring and

one site for regional background monitoring. For each MSA or PMSA with a population over

1 million, a continuous fine particle analyzer (e.g., beta attenuation analyzer, nephelometer,

transmissometer, or inertial microbalance TEOM) must be located at a core PM2.5 site. With

these criteria, neither the Jefferson County or San Joaquin Valley examples cited here would

require continuous monitors, since neither one contains an MSA with more than 1 million

inhabitants. However, continuous monitors should be considered in all areas with population

greater than 200,000 to assist with public reporting of real-time data.

The selection of the known or anticipated community-oriented monitoring site with the

highest concentration as the first site in the CMZ serves two purposes. First, it allows the site

to be used for determination of the 24-hour and annual PM2.5 standards. Second, it

encourages the location of other sites within the CMZ boundaries to give a more

representative PM2.5 spatial average.

The number of required monitors in an MPA is a function of the MPA’s population.

Table 4.5.1 shows the minimum number of these core monitors for a given MSA population.
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5.0 MONITOR SITING

PM2.5 monitors are situated to meet requirements as core sites, community averaging

sites, or daily compliance sites. Internal requirements are those for operating the needed

instruments, while external criteria address site surroundings to achieve specific monitoring

purposes.

5.1 Internal Siting Criteria

Internal criteria refer to the logistics of locating and service instruments for multi-year

monitoring. These include:

• Long-term Site Commitment: NAMS sites are meant to measure trends as well

as compliance, and a long-term commitment from the property owner for

continued monitoring is required. Public buildings such as schools, fire stations,

police stations, recreation halls, and hospitals often have more stability and a

motive for public service than do private or commercial buildings.

• Sufficient Operating Space: A large, flat space, elevated at least 1 m but no

more than 14 m above ground level, is needed to place monitors and monitoring

probes. The space available for samplers should be at least 5 m distant and upwind

(most common wind direction) from building exhausts and intakes and at least 2 m

from walls, parapets, or penthouses that might influence air flow. Buildings

housing large emitters, such as coal-, waste-, or oil-burning boilers, furnaces or

incinerators, should be avoided.

• Access and Security: Access to the sampling platform should be controlled by

fencing or elevation above ground level. Sampler inlets should be sufficiently

distant (>10 m) from public access to preclude purposeful contamination from

reaching them in sufficient quantities to bias samples. Access should be controlled

by a locked door, gate, or ladder with documentation of site visitations and the

purposes of those visits.

• Safety: Wiring, access steps, sampler spacing, and platform railings should

comply with all relevant codes and workplace regulations, as well as common

sense, to minimize potential for injury to personnel or equipment.

• Power: Power should be sufficient for the samplers to be operated on a long-term

basis, as well as for special study and audit samplers to be located at a site. Where

possible, a separate circuit breaker should be provided for each instrument to

prevent an electrical malfunction in one monitor from shutting off power to the

other monitors at the site.

• Environmental Control: Environments surrounding monitoring instruments

should be maintained within the manufacturers specifications for proper instrument

function. Most FRM filter-based samplers are designed to operate under a wide
5-2

range of environmental conditions and can be located outdoors in most types of

weather. Several continuous monitoring methods may require environmental

shelters with temperature and humidity controls to protect their electronic sensing

and data acquisition mechanisms.

These criteria may be tightened or relaxed for special purpose, transport, and

background monitors. For example, battery-powered saturation monitors may be located on

utility poles at various elevations to assess the zone of influence and zones of representation

for sources and receptors.

5.2 External Siting Criteria

External siting criteria refer to the environs surrounding a measurement location, and

these differ depending on the zone of representation intended for a specific monitoring site.

• Exposure: Large nearby buildings and trees extending above the height of the

monitor may present barriers or deposition surfaces for PM. Certain trees may

also be sources of PM in the form of detritus, pollen, or insect parts. These can be

avoided by locating samplers by placing them >20 m from nearby trees, and twice

the difference in elevation difference from nearby buildings or other obstacles.

• Distance from Nearby Emitters: The monitor should be outside the zone of

influence of sources located within the designated zone of representation for the

monitoring site. Neighborhood and urban zones of representation are needed for

community-oriented compliance monitors. These should generally be at least 1 km

from very large, visibly identifiable source areas occupied by major industries such

as cement and steel production or ore processing. Regarding exhaust and road

dust emissions from paved roads, Figure 5.2.1 provides guidance on the

recommended monitoring distances from paved roads with different levels of

average daily traffic for neighborhood- and urban-scale sites. A minimum distance

of ~50 m from busy paved highways is usually outside the road’s immediate zone

of influence for a rooftop monitor. These siting criteria were established for PM10

monitoring siting (U.S. EPA, 1987), and they have proven their validity in PM10

network design. For larger than middle-scale monitoring, no unpaved roads with

significant traffic or residential wood-burning appliances should be located within

100 m of the monitoring location. Background monitoring sites should be located

>100 km from large population centers, and >100 m from roads and wood burning

(burning is common, though often intermittent, in camping, forested, and

agricultural areas).

• Proximity to Other Measurements: Other air quality and meteorological

measurements can aid in the interpretation of high PM levels, and with all other

considerations being equal, PM2.5 sites should give preference to existing sites that

make other measurements. For example, high local wind gusts may
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Figure 5.2.1. Recommended distances and elevations of PM sampler inlets from heavily

traveled roadways.

explain high PM readings as caused by wind blown dust. These gusts are often

localized, and would not be detected on a more distant monitor. Similarly, a

strong correspondence between hourly CO and PM readings would indicate that

locally emitted vehicle exhaust is a large contributor at that site. This conclusion

would be more tenuous if the CO measurements were not collocated. In

particular, collocating PM10 and PM2.5 monitors will provide information on the

size distribution of suspended particles.

5.3 Evaluating Zones of Representation

A site originally selected to represent community exposure (generally on a

neighborhood or urban scale) may have its zone of representation change owing to long-term

changes in land use or short term events that affect that particular site.

• Annual Site Surveys: The land use and sources around a monitoring site may

change from year to year, especially in high growth areas. Maps should be

updated as part of the annual measurement network summary, and the setbacks
5-4

from emissions sources and obstructions should be re-evaluated to ascertain that

they are still met.

• Records of Intermittent Events: High PM2.5 or PM10 concentrations may have

corresponded to a specific event, such as construction or a fire, occurring near the

measurement location. Visual events should be recorded as part of the

measurement network maintenance, and these should be summarized at the end of

each year for inclusion in the annual network evaluation report.

• Saturation Monitoring Studies: To evaluate the zone of representation of sites,

many monitors may be located at different distances around and between monitors

in a CMZ. Spatial uniformity measures can be determined for these temporary

locations and compared to those from the sites within the CMZ to evaluate how

well the long-term sites represent population exposures to PM.

5.4 Evaluating Siting Redundancy

The spatial uniformity measures specified in Section 4 to can be applied to PM2.5

concentrations at the sites within a CMZ to determine the extent to which each one supplies

additional information concerning exposure. When information content is redundant,

recommendations can be justified for the transfer of sites within a CMZ to other parts of the

CMZ or to other CMZs.

5.5 Evaluating Network Adequacy for Spatial Averaging

Appendix D of 40 CFR 58 lays out several specific requirements for PM2.5 monitoring

sites to be eligible for spatial averaging: (1) they each represent a neighborhood or larger

spatial measurement scale and (2) their community monitoring zone represents homogeneous

air quality. To satisfy the latter, the sites’ annual averages must be within ±20% of the CMZwide average on an annual basis, be influenced by similar sources and be reasonably correlated

on a daily basis (r > 0.6). This assessment can be made in a preliminary manner with one year

of data, but will require three years of PM2.5 air quality data before final evaluation of site

eligibility and comparisons to the NAAQS can be made.

EPA recommends that the State follow additional design criteria to ensure that the air

quality in a community monitoring zone is spatially homogeneous. This should include an

evaluation of several factors including the year-to-year variability of each individual site, the

effect of changing emissions over time, the relative distribution of concentrations among the

sites, the spatial patterns within the CMZ, similarities and differences in speciated PM2.5, and

the relationship between population density and air quality patterns.

5.5.1 Temporal Behavior

The evaluation of ±20% year-to-year variability should support spatially homogeneous

air quality during 3-year period.
5-5

One location should not be consistently and substantially higher (e.g., 30% higher)

than all the other sites. The variation among annual means should reflect sampling and

meteorological variation and should not characterize consistent differences in air quality

within the community monitoring zone. The left hand graph in Figure 5.5.1 supports

homogeneity while the right hand diagram does not. Accordingly, it may not be sufficient that

individual means be ±20%.

Figure 5.5.1. Example of spatial homogeneity over a three-year period.

5.5.2 Consistent trends

The variation among the eligible core monitors should be stable over time. Changes in

emissions can differently affect individual monitoring locations. This can cause site eligibility

to change over time. Although the sites can be within ±20 % during the first year, they can be

±30% or more after changes in emissions have occurred. This is depicted in Figure 5.5.2.

Therefore the initial set of annual means should be well within ±20 % (e.g. 10-15%) to allow

for potential changes over time.

Figure 5.5.2. Example of temporal trends.

5.5.3 Spatial Placement of Monitors

The core monitoring sites should adequately reflect area-wide average air quality.

With the help from modeling or spatial interpolation, the site average can be compared to

other estimates of the area-wide average. An example of this is shown in Figure 5.5.3. If

there are significant discrepancies between the different area-wide estimates, the placement of

the monitors within the zone can be questioned.
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Figure 5.5.3. Example of spatial averaging.

5.5.4 Chemical Composition of PM2.5

To satisfy the requirement that the sites are influenced by similar sources, the aerosol

for all eligible sites in the CMZ should have similar chemical composition on an annual basis.

Moreover, for more sensitive evaluation, this should also be true on seasonal basis. Although

chemical analysis to determine eligibility for spatial averaging is not required, the use of

chemical speciation data, when available, can provide important insights into the relative

differences among monitoring sites. Appropriate chemical species can be used (e.g., carbon,

metals) for making this assessment.

6

8 3

3

8

2

Difference between sites is the 

change in one compo nent 

Site A, 13 ug/ m 3 Site B, 17 ug/m3

Figure 5.5.4. Example of differences in chemical composition between two sites.

5.5.5 Population Density and Air Quality Patterns (additional supporting evidence)

To further justify the placement of monitoring sites within the community monitoring

zones, the estimated air quality pattern and population density should support the placement

of core monitors. This is illustrated in the following figure which presents a case where

average air quality is or is not representative of potential population exposure.
5-7

Figure 5.5.5. Example of using population density for monitor placement.
6-1

6.0 STATE PM MONITORING NETWORK DESCRIPTIONS,

ANNUAL REPORTS, AND NETWORK EVALUATIONS

As implied by this guidance, monitoring network configurations are likely to change

from year to year. NAMS sites will remain fixed to the extent allowed by such considerations

as leasing arrangements, urban renewal projects, and loss of monitoring locations due to other

construction, fires, or natural disasters. NAMS will include some of the current NAMS

network for PM10 and the current PAMS network for ozone to maintain continuity with longterm data sets acquired at these sites. PAMS with PM2.5 monitors will be a subset of the core

SLAMS as well as selected regional transport sites.

Non-NAMS SLAMS sites may change their locations from year to year, however, if

they are shown to provide the same information for determining attainment as NAMS sites.

SPMs will surely change location from year to year, and may even be discontinued without

major review once they have provided the data for their intended purposes. This section

outlines the elements of state network descriptions, annual reports, and network evaluations

that will document and justify changes in the monitoring network.

6.1 State PM Monitoring Network Descriptions

The State PM Monitoring Network Description should describe the PM monitoring

strategy based on the use of SLAMS (including NAMS and PAMS) and SPMs for PM10 and

PM2.5. The phase-in of PM2.5 monitors and changes in the existing PM10 and TSP network

should be specified and justified. These descriptions should document the application of this

guidance to the selection of MPAs within each state, the definition of MPA and CMZ

boundaries, and transport and background sites within each state. Specific definitions for

CMZs are only needed for spatial averaging, otherwise more general descriptions of

community monitoring areas are appropriate. The description should be a single summary

report that documents monitoring for the entire state, with a separate appendix for each of the

designated MPAs. The network description should consist of the following sections.

1. Introduction: Describe the state’s physical setting, major metropolitan areas,

economic activity and industry. Show those MSAs within the state having

populations levels requiring MPAs. Identify other areas with lower populations

that the state chooses to define as MPAs and specify the reasons for these

additional MPAs.

2. Monitoring Planning Areas: Show the MPAs that have been defined for the

state and summarize the justification for these MPAs, based on the steps in Section

3. USGS (1:125000) maps and census data are available in electronic formats (see

Appendix A).

3. Community Monitoring Approach: Indicate the extent to which spatial

averaging is intended and provide maps showing boundaries for the CMZs. These

maps need not be as detailed as those shown in the appendices to justify the

CMZs.
6-2

4. Transport and Background Areas: Show the areas where transport between or

upwind of MPAs is expected to occur and explain why that transport is expected.

Locate monitors distant from source areas and explain why these should represent

background levels within the state or selected portions of the state.

5. Schedule and Responsibilities for Network Change: Show the planned start-up

date for each new PM2.5 monitoring site, with its MPA and CMZ (where

appropriate) designation, its type (community-exposure, daily compliance, SPM,

transport, or background), its anticipated monitoring methods (e.g., FRM, Class I,

II, and/or III equivalent or other non-reference/equivalent methods), and its

measurement frequency. Show which PM10 monitoring sites will be modified by

discontinuation or reduction in measurement frequency. Network changes are to

be phased in between 1998 and 1999 for PM2.5 and between 1998 and 2000 for

PM10.

A separate appendix for each MPA should present the following detailed information:

1. Introduction: Describe the physical setting of the MPA, population

characteristics, climate and weather, dominant economic activities, and emissions

sources. Much of this information can be concisely and efficiently summarized on

maps such as USGS (1:50000 or 1:2400), aerial photographic, or commercial

maps available on CD-ROM (Appendix A identifies several options).

2. Community Monitoring Areas or Zones: Show maps of the selected

community monitoring areas or community monitoring zones, if appropriate, and

justify them based on the procedures in Section 4. Document modeling and data

analysis activities that were conducted to determine these zones. These maps

should include, where available and appropriate: 1) populated entity boundaries

(e.g. census tracts or blocks), 2) relevant jurisdictional boundaries; 3) commercial,

residential, agricultural, and industrial land uses; 4) suspected area source

emissions hot spots (e.g. wood burning communities, diesel emissions hot spots

identified from bus transfer locations, railyards, marine terminals, waste recycling,

land preparation, unpaved roads, etc.). Include tables of spatial uniformity

measures (spatial averages, spatial coefficients of variation, 98th percentile

concentrations, and spatial correlations) using existing data to determine the zone

of representation of existing monitors. Use wind roses and trajectories to identify

potential transport pathways and terrain maps to identify potential barriers to

transport.

3. Sampling Site Descriptions: Provide site descriptions, including maps showing

surrounding sources as well as verbal descriptions of activities surrounding the

site. Define the variables measured at each site in terms of observables measured

(e.g., PM10 mass, PM2.5 mass, chemical composition), sample duration, frequency,

and measurement method.
6-3

4. Sites Intended for Comparison with NAAQS: Specify those sites that acquire

measurements to be compared with the NAAQS, with their designation as

community-oriented, daily compliance, or other sites. Identify sampling methods

that acquire compliance data at these sites.

5. Special Purpose Monitoring Projects: Where there is doubt concerning the

validity of the CMZs, the zone of representation of a sampling site, or the influence

on PM from different sources, define special purpose monitoring projects to

resolve these concerns.

6. Phase-In and Responsibilities: Estimate costs of hardware, operation, and

maintenance for the number of stations and measurement frequencies required, and

reconcile these costs with current resources. Justify trade-offs between additions

of PM2.5 monitors at the expense of PM10 monitors. Specify responsibilities of

local and state monitoring authorities, and determine a schedule for changes.

6.2 Annual Measurement Reports

The annual measurement report should include the following information for each

MPA:

1. Annual Site Data Summaries: Include tables of PM10 and PM2.5 annual

averages, maximum concentrations, and 98th and 99th percentile concentrations for

each monitoring site for each year. For most situations these data can be obtained

from EPA’s AIRS database. This will include data from non-compliance monitors,

including SPMs, as well as for compliance monitors.

2. Spatial Averages: When spatial averaging is utilized, include tables of spatially

averaged annual-average PM2.5 concentrations for each CMZ for each year.

Identify sites with annual averages differing by more than ±20% from the spatial

average, and remove them from the average.

3. Compliance Statistics: Include three-year-average annual averages for each core

site (and when appropriate for each CMZ) and three-year-average 98th and 99th

percentile averages for each eligible site.

4. Compliance Determination: Compare the compliance statistics with standards,

and discuss the compliance or non-compliance of each site and/or CMZ in the

MPA. Compare measurements at background and transport sites with those at

core sites to estimate the extent to which urban-scale (within the MPA) or

regional-scale (within and outside of the MPA) sources contribute to the excess

concentrations. Discuss the comparison of concentrations at background and

transport sites with the core sites.
6-4

6.3 Annual Network Evaluation

The annual network evaluation should include the following information for each

MPA:

1. Changes in Site Characteristics: Document changes in site exposure owing to

construction or demolition of nearby buildings or the growth of foliage, the

presence of temporary (e.g., building construction, road repair) or permanent (e.g.,

an industrial facility or a new highway) emitters within 1 km of the site, or special

events (e.g., accidental fires, major wind storms). Record the magnitude, location,

and duration of these changes. Examine PM measurements in conjunction with

these changes to evaluate the continued population-orientation of the site.

2. Concentration Uniformity Measures: Evaluate spatial time-series plots, spatial

correlation coefficients, differences between site-specific averages and 98th

percentile values, and spatial coefficients of variation within and between CMZs

within an MPA. These measures are especially important for evaluating the spatial

average used to determine compliance with the annual PM2.5 standard. The annual

average at each site within a CMZ should not differ by more than ±20% from the

CMZ average. If one or more of these site-specific averages exceeds this

tolerance, it may be necessary to re-define the CMZs to better represent

community exposure, or to re-evaluate the site exposure. On the other hand, high

correlations among measurements, low spatial coefficients of variation, and annual

averages and 98th and 99th percentiles that do not differ by more than ±5% within a

CMZ indicate that some stations are redundant. Justification can be made for

moving one or more of the non-NAMS trend stations to another location where it

might better represent population exposure. Similar results for measurements in

different CMZs within the MPA indicate that the adjacent CMZs with similar

concentrations might be combined into a larger CMZ, with a consequent reduction

in the number of monitors needed to represent the spatial average.

3. Monitoring Site Additions and Deletions: When sites are determined to no

longer represent population exposure, or when spatial uniformity measures show

that they provide consistently redundant information, recommendations and

justifications for deletion may be submitted to the EPA regional office for

approval. When spatial uniformity measures show high variability within a CMZ,

when a populated area expands beyond its original boundaries, or when special

monitoring sites are deemed necessary, measurement locations may need to be

added. These, too, should be justified. The intent of this annual evaluation is to

continually re-define the network, within available monitoring resource constraints,

to best represent population exposures. This section of the evaluation allows

substantial flexibility for networks to evolve to attain this end.

4. Changes to CMZ and MPA Boundaries and Site Designations: SLAMS with

PM2.5 standard exceedances should be considered for re-designation as core sites.

SPM sites showing NAAQS violations should be considered for designation as
6-5

SLAMS sites. Changes in population or emissions may require changes in MPA or

CMZ boundaries, including the creation of additional MPAs or CMZs. The

evaluation of spatial uniformity measures may also justify recommendations for

changes in MPA or CMZ boundaries. In particular, the 2000 census will provide

more current information on population distributions, and when these data become

available in 2001 or 2002, the MPA and CMZ boundaries will need to be

re-assessed. Locally generated land-use patterns in rapidly growing areas can also

be used to determine the extent to which the boundaries of planning areas and

averaging zones should be expanded.
7-1

7.0 REFERENCES

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Chemical and Size Characterization of Particulate Matter Emissions from Selected

Sources in California. In Transactions: Receptor Models in Air Resources

Management, J.G. Watson, Ed. Air & Waste Management Assoc., Pittsburgh, PA,

pp. 145-158.

Andricevic, R. (1990). Cost-Effective Network Design for Groundwater Flow Monitoring.

Stochastic Hydrol. Hydraul., pp. 27-41.

Borgman, L.E., K. Gerow, and G.T. Flatman. (1996). Cost-Effective Sampling for Spatially

Distributed Phenomena. In Principles of Environmental Sampling, 2nd ed., L.H.

Keith, Ed. Lewis Publishers, pp. 753-778.

Bowne, N.E., and R.J. Lundergan (1983). Overview, Results, and Conclusions for the EPRI

Plume Model Validation and Development Project: Plains Site. EPRI Report No. EA3074, Project 1616-1, Final Report. Electric Power Research Institute, Palo Alto, CA.

Braaten, D.A., and T.A. Cahill (1986). Size and Composition of Asian Dust Transported to

Hawaii. Atmos. Environ., 20:1105-1109.

Camisani-Calzolari, F.A.G.M. (1984). Geostatistical Appraisal of a Tabular Uranium Deposit

in South Africa. In Geostatistics for Natural Resources Characterization, Part 2, G.

Verley et al., eds. D. Reidel Publishing Co., pp. 935-949.

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APPENDIX A

Sources of Data Used for Network Design and Evaluation
Appendix A (continued)

Sources of Data Used for Network Design and Evaluation

Database Description Access Format(s) Relevant Data Fields Reference or Internet Address

U.S. Geological Service Digital Elevation 

Model database

This site contains 1 minute or 1:250,000-scale 

Digital Elevation Models of the US arranged by 

USGS quadrants.

WWW and ftp USGS DEM format 

(ASCII)

Digital elevation data are 

read into ARC/INFO, 

ArcView, other GIS 

packages as data layers. They 

are also used in dispersion 

models.

http://edcftp.cr.usgs.gov:80/pub/data/DEM/250/

U.S. Geological Service North American 

Digital Elevation Models

30 arc second (approximately 1 km resolution) 

DCW Digital Elevation Models of North America 

are provided here. These maps are supplied in four 

equal pieces over the US.

WWW and ftp compressed BIL 

(band interleaved by 

line) format that can 

be read into GIS 

software

Digital elevation data are 

read into ARC/INFO, 

ArcView, other GIS 

packages as data layers. They 

are also used in dispersion 

models.

http://edcwww.cr.usgs.gov/landdaac/gtopo30/gtopo30.html

U.S. Geological Survey Topographic, Image 

and Related Maps

The USGS offers standard topographic quadrangle 

maps. Scales are 7.5 minute (1:24,000, 1:25,000 

and 1:20,000), 7.5x15 minute (1:25,000) and 15 

minute (1:50,000 and 1:63,360). County maps are 

available in 1:50,000 and 1:100,000.

These maps can 

be obtained at 

local map 

dealers as well 

as from the 

USGS

paper maps Topographic maps may be 

useful in defining MPAs, 

CMZs and general mapping 

at the microscale to urban 

scale.

Other maps are available from the USGS at 

http://mapping.usgs.gov/esic/mapprice.html. Commercial dealers may be located 

on the web at http://mapping.usgs.gov/esic/usimage/dealers.html or the local 

phone book. Maps may be ordered from the USGS directly by calling (800) USAMAPS or by mail at: USGS Information Services, Box 25286, Denver, CO 80225.

The USGS Geographic Names Information 

System (GNIS)

The GNIS contains name and location information 

for approximately 2 million physical and cultural 

features located throughout the U.S. and it's 

territories.

FTP Formatted ASCII 

text file

This database provides 

location (latitude and 

longitude) for populated 

entities as well as geographic 

features.

http://mapping.usgs.gov/www/gnis/gnisftp.html

Johns Hopkins Univ/Applied Physics 

Laboratory Digital Elevation Model images

This site provides excellent 5 x 5 degree color 

shaded relief maps of the lower 48 states.

WWW Digital GIF format These images can be easily 

printed out or imported into 

ArcView as a terrain layer.

http://fermi.jhuapl.edu/states/states.html

U.S. EPA Forest Land Distribution Data of 

the United States

Full U.S. forest GIS coverages and GIF images are 

available for forest type and forest density

WWW Digital GIF, 

ARC/INFO grid 

formats

This data set may be useful 

in defining natural air basins 

by showing forest cover & 

tree type coverages as landuse.

http://www.epa.gov/docs/grd/forest_inventory/

U.S. Bureau of the Census TIGER Mapping 

Service

In this web site one can create maps on-line using 

several of the attributes in the TIGER/Line database 

such as MSA, census tracts, citiesm streets, etc.

WWW Digital GIF format This web site produces quick 

turn-around, user-defined 

US maps zoomed in on any 

region of the U.S.

http://tiger.census.gov/

U.S. Bureau of Census U.S. Gazetteer This gazetteer is used to identify places to view with 

the Tiger Map Server and obtain census data from 

the 1990 Census Lookup server. You can search for 

places, counties or MCDs by entering the name and 

state abbreviation (optional), or 5-digit zip code.

WWW Census data is 

provided in HTML, 

tab delimited or 

CODATA formats. 

The map images are 

in the digital GIF 

format.

This web site allows custom 

map creation which can be 

used to identify planning 

areas and population. 

Census tracts, MSA, PMSA 

are options in the map 

making process.

http://www.census.gov/cgi-bin/gazetteer

Temporal Urban Mapping at USGS This site includes several digital satellite and 

geographic images of the SF Bay area (data 

limited to Bay area & Baltimore/Washington area)

WWW Digital GIF format population growth maps, 

satellite orthophoto images 

and Lansat of San Francisco

http://edcwww2.cr.usgs.gov/umap/umap.html

USGS Geologic Information This site mainly contains digital geologic maps for 

the central and western US, national geologic map 

database but has some useful base maps.

WWW MapInfo, 

ARC/INFO format

geologic GIS and US base 

maps

http://geology.usgs.gov/maps.html
Appendix A (continued)

Sources of Data Used for Network Design and Evaluation

Bay Area Digital Geo Reource (BADGER) The Bay Area Shared Information Consortium 

makes available maps, images and data on the San 

Francisco Bay area.

WWW Several digital 

formats including 

GIF satellite images

10 meter satellite imagery 

over SF Bay area

http://www.svi.org/badger.html

National Park Service boundary database National Park Service boundaries and regions are 

distributed in ARC/INFO GIS export format at this 

site. An ARC/INFO coverage of U.S. States is also 

available at this site.

WWW ARC/INFO export 

format

park boundaries in GIS ftp://ftp.its.nps.gov/pub/park_boundaries/

US TIGER/Line databases These disks contain a multitude of geographic 

information such as state,county,census block , 

census tract, etc. boundary databases.

CD-ROM ASCII (in 

TIGER/Line 

database format)

US boundary databases 

which includes some landuse information

Customer Services, Bureau of Census, 301-457-4100

U.S. Census Summary Tape Files (STF) These disks contain US population & 

socioeconomic data taken during the 1990 census

CD-ROM ASCII US population databases Customer Services, Bureau of Census, 301-457-4100

U.S. Census metropolitan area population 

estimates

1990 to 1995 MSA/PMSA/CMSA/NECMA 

population estimates by the U.S. Bureau of Census

WWW ASCII text files that 

are easily 

importable into 

spreadsheet 

programs

US MSA, CMSA, PMSA, 

NECMA population 

estimates

http://www.census.gov/population/www/estimates/metropop.html

Landview II software Landview is a display software for TIGER/Line, 

STF data. Currently a DOS application with limited 

exportability.

CD-ROM Reads ASCII 

TIGER and census 

data

DOS based text and 

graphical display and 

provides quick hard copies

Customer Services, Bureau of Census, 301-457-4100

Voyager and Voyager Viewer mapping 

software

Voyager is a multidimensional data browser for 

browsing Voyager data files.

WWW Reads Voyager data 

files.

This software package is 

useful as a visualization tool 

for temporal and spatial air 

quality databases.

http://capita.wustl.edu/CAPITA/utilities/utilities.html

ESRI ARC/INFO and ArcView GIS software ArcView is a commercially available desktop GIS 

software for PC, Mac and UNIX systems. 

ARC/INFO is also a commercial GIS package with 

extensive spatal analysis tools for network design.

Through ESRI CD-ROM These software tools can be 

used to simply display 

cartographic data or provide 

spatial data analysis of the 

data sets in this appendix.

The software is available from Envrionmental Systems Research Institute, Inc. 

(ESRI). They may be contacted by e-mail at info@esri.com or on the WWW at 

http://www.esri.com or (800) 447-9778.

Western Regional Climate Center This climate center serves CA, NV, OR, WA, ID, 

MT, UT, AZ, NM; meteorological data

WWW various formats climate data http://wrcc.sage.dri.edu

High Plains Climate Center This climate center serves ND, SD, NE, KS, CO, 

WY data; meteorological data

WWW various formats climate data http://hpccsun.unl.edu

Midwestern Climate Center This climate center serves MN, IA, MO, WI, IL, IN, 

KY, MI, OH; meteorological data

WWW various formats climate data http://mcc.wsw.uiuc.edu

Northeast Regional Climate Center This climate center serves ME, NH, VT, NY, MA, 

RI, CT, PA, NJ, DE, MD, WV; meteorological data

WWW various formats climate data http://met-www.cit.cornell.edu/

Southern Regional Climate Center This climate center serves LA, TX, OK, AR, MS, 

TN; meteorological data

WWW various formats climate data http://www.srcc.lsu.edu/srcc.html

Southeastern Climate Center This climate center serves SC, NC, VA, AL, FL, 

GA, Puerto Rico, Virgin Islands; meteorological 

WWW various formats climate data http://water.dnr.state.sc.us/climate/sercc

National Climatic Data Center The NCDC provides a wealth of climate data, 

publications, databases, images related to climate in 

the US and world-wide. Some data is available online.

WWW and ftp various formats climate data world-wide but 

mainly for the United States

http://www.ncdc.noaa.gov/

IMPROVE network data Data is available for the IMPROVE network 

(visibility in National Parks & wilderness areas).

listserve via email to 

subscribe

ASCII text files aerosol and visibility data LISTSERVE@caesar.ucdavis.edu; "SUBSCRIBE IMPROVE-DATA-USERS"

U.S. EPA SCRAM Meteorological Data Airport Surface & Upper Air Meteorological data 

for slected US airports

internet (web, 

telnet, ftp), 

phone dial-up

ASCII text files surface meteorology and 

mixing height

http://www.epa.gov/scram001/t25.htm
Appendix A (continued)

Sources of Data Used for Network Design and Evaluation

U.S. EPA Aerometric Information Retrieval 

System (AIRS)

Aerometric Information Retrieval System -- primary 

data source for air quality and meteorological data 

from the EPA

phone dial-up ASCII reports air quality and emissions 

data such as TSP, PM10, 

PM2.5,O3, NO, CO data

http://www.epa.gov/docs/airs/airs.html or US EPA OAQPS at (919) 541-5454

U.S. EPA Region 3 GIS data The region 3 web site gives access to the EPA 

Region III Land Cover Data Set and GIS coverages 

of watersheds, forests, ecoregions, TRI sites, 

CERCLIS, hydrology, roads, railroads, NPL and 

USGS DEM.

WWW GIS coverages are in 

ARC/INFO export 

format with GIF 

preview images. 

The land cover data 

set an ERDAS 

image readable in 

ARC/INFO.

The data provided at this 

web site may be useful in 

determining state planning 

areas and CMZs by showing 

land use and possible 

biogenic emission sources.

http://www.epa.gov/reg3giss/libraryp.htm

U.S. EPA Region 7 GIS data The Quad100 GIS data library contains 1:100,000-

scale base data for Region 7 and is tiled by United 

States Geological Survey (USGS) 1:100,000-scale 

(30 X 60 arc minutes) quadrangle boundaries.

WWW This site provides 

ARC/INFO GIS 

coverages.

The data provided at this 

web site may be useful in 

determining state planning 

areas and CMZs by showing 

streams, lakes, transportation 

routes, wetlands and county 

boundaries.

http://www.epa.gov/region07/envdata/gis/q100lib.html

U.S. EPA Region 8 GIS data This site contains Region VIII Environmental 

ARC/INFO GIS Data covering natural and manmade boundaries.

WWW The data files are in 

Unix GZIPed 

Arc/Info 7.02 

export format.

Country Boundaries,Census 

Tract 

Boundaries,Dams,Ecoregion

s,Federal Land 

Boundary,Hydrologic Unit 

Codes,Linear Hydrology 

(streams, rivers, 

etc),Polygonal 

Hydrology,Mines,National 

Pollution Discharge,Historic 

& Current Place 

Names,Railroads,Seismic 

Activity,State 

Boundary,Tribal Land,Toxic 

Release 

Inventory,Treatment,Storage 

& Disposal Facilities,Zip 

Codes 

http://www.epa.gov/region08/data.html

U.S. EPA Region 9 Nonattainment Maps This site provides O3, CO, NO2,PM10,class 1, tribal 

land maps in EPA's region 9

WWW Digital GIF format nonattainment maps http://www.epa.gov/region09/air/maps/maps_top.html

STATSGO data (U.S. Department of 

Agriculture Soil Conservation Service 

Geographic Database)

This US soil geographic database is arranged 

according to state.

WWW ARC/INFO, DLG-3 GIS soil coverages for 

ARC/INFO and ArcView

ftp://ftp.ftw.nrcs.usda.gov/pub/statsgo/

Oregon digital map library This site contains Oregon 1:2,000,000 to 1:24,000 

digital maps of agriculture land, cities, geology, 

highways, population, soils, etc.

WWW ARC/INFO many GIS useful coverages 

for land-use but only for 

Oregon

http://www.sscgis.state.or.us/data/data.html

U.S. GeoData DEM and DLG files from the 

EROS Data Center

The EROS data center offers U.S. DEM, Land Use 

Land Cover (LULC) and Digital Line Graph (DLG) 

databases in scales from 1:100,000 to 1:2,000,000.

WWW These digital files 

can be imported 

into ARC/INFO and 

other GIS packages.

DEM provides elevation & 

boundary data for use with a 

GIS. The DLG provide 

boundary, streets and 

topographic data for a GIS.

http://edcwww.cr.usgs.gov/doc/edchome/ndcdb/ndcdb.html
APPENDIX B

Metropolitan Statistical Areas,

Primary Metropolitan Statistical Areas,

Consolidated Metropolitan Statistical Areas, and

New England County Metropolitan Statistical Areas

in the United States
Appendix B (continued)

Metropolitan Statistical Areas, Primary Metropolitan Statistical Areas, Consolidated Metropolitan 

Statistical Areas, and New England County Metropolitan Statistical Areas in the United States

State Metropolitan Area TYPE Counties

1990 

Population

1995 Est. 

Population

1995 pop 

density 

(km-2) Area (km2)

AK Anchorage, AK MSA Anchorage Borough 226,338 251,335 57.2 4396.9

AL Anniston, AL MSA Calhoun County 116,034 117,263 74.4 1576.0

AL Birmingham, AL MSA Blount County 840,140 881,761 106.8 8,255.0

Jefferson County

St. Clair County

Shelby County

AL Decatur, AL MSA Lawrence County 131,556 139,837 42.3 3304.0

Morgan County

AL Dothan, AL MSA Dale County 130,964 134,368 45.4 2956.6

Houston County

AL Florence, AL MSA Colbert County 131,327 136,184 41.6 3274.0

Lauderdale County

AL Gadsden, AL MSA Etowah County 99,840 100,259 72.4 1385.2

AL Huntsville, AL MSA Limestone County 293,047 317,684 89.3 3556.2

Madison County

AL Mobile, AL MSA Baldwin County 476,923 517,611 70.6 7329.4

Mobile County

AL Montgomery, AL MSA Autauga County 292,517 315,332 60.6 5199.3

Elmore County

Montgomery County

AL Tuscaloosa, AL MSA Tuscaloosa County 150,522 158,732 46.2 3432.4

AR Fayetteville-Springdale-Rogers, AR MSA Benton County 210,908 252,640 54.4 4645.3

Washington County

AR Jonesboro, AR MSA

AR Little Rock-North Little Rock, AR MSA Faulkner County 513,117 543,568 72.2 7533.2

Lonoke County

Pulaski County

Saline County

AR Pine Bluff, AR MSA Jefferson County 85,487 84,042 36.7 2291.6

AR-OK Fort Smith, AR-OK MSA Crawford County 175,911 188,572 40.3 4676.9

Sebastian County

Sequoyah County

AZ Phoenix-Mesa, AZ MSA Maricopa County 2,238,480 2,563,582 67.9 37746.7

Pinal County

AZ Tucson, AZ MSA Pima County 666,880 752,428 31.6 23794.4

AZ Yuma, AZ MSA Yuma County 106,895 132,869 9.3 14282.4

AZ-UT Flagstaff, AZ-UT MSA Coconino County 101,760 116,498

Kane County

CA Bakersfield, CA MSA Kern County 543,477 617,528 29.3 21086.7

CA Chico-Paradise, CA MSA Butte County 182,120 192,880 45.4 4246.6

CA Fresno, CA MSA Fresno County 755,580 844,293 40.2 20983.3

Madera County

CA Los Angeles-Long Beach, CA PMSA Los Angeles County 8,863,164 9,138,789 869.1 10515.3

CA Los Angeles-Riverside-Orange County, CA CMSA Los Angeles County 14,531,529 15,362,165 174.4 88080.4

Orange County

Riverside County

San Bernardino County

Ventura County

CA Orange County, CA PMSA Orange County 2,410,556 2,563,971 1253.6 2045.3

CA Riverside-San Bernardino, CA PMSA Riverside County 2,588,793 2,949,387 41.8 70629.2

San Bernardino County

CA Ventura, CA PMSA Ventura County 669,016 710,018 148.5 4781.0

CA Merced, CA MSA Merced County 178,403 194,407 38.9 4995.8

CA Modesto, CA MSA Stanislaus County 370,522 410,870 106.1 3870.9

CA Redding, CA MSA Shasta County 147,036 160,940 16.4 9804.8

CA Sacramento, CA PMSA El Dorado County 1,340,010 1,456,955 137.8 10571.3

Placer County

Sacramento County

CA Yolo, CA PMSA Yolo County 141,092 147,769 56.4 2622.2

CA Salinas, CA MSA Monterey County 355,660 348,841 40.5 8603.8

CA San Diego, CA MSA San Diego County 2,498,016 2,644,132 242.8 10889.6

CA Oakland, CA PMSA Alameda County 2,082,914 2,195,411 581.5 3775.7

Contra Costa County

CA Sacramento-Yolo, CA CMSA El Dorado County 1,481,220 1,604,724 121.1 13250.4

Placer County

Sacramento County

Yolo County

CA San Francisco, CA PMSA Marin County 1,603,678 1,645,815 625.7 2630.4

San Francisco County

B-1
Appendix B (continued)

Metropolitan Statistical Areas, Primary Metropolitan Statistical Areas, Consolidated Metropolitan 

Statistical Areas, and New England County Metropolitan Statistical Areas in the United States

State Metropolitan Area TYPE Counties

1990 

Population

1995 Est. 

Population

1995 pop 

density 

(km-2) Area (km2)

San Mateo County

CA San Francisco-Oakland-San Jose, CA CMSA Alameda County 6,249,881 6,539,602 341.1 19173.7

Contra Costa County

Marin County

San Francisco County

San Mateo County

Santa Clara County

Santa Cruz County

Sonoma County

Napa County

Solano County

CA San Jose, CA PMSA Santa Clara County 1,497,577 1,565,253 468.0 3344.3

CA Santa Cruz-Watsonville, CA PMSA Santa Cruz County 229,734 236,669 205.0 1154.6

CA Santa Rosa, CA PMSA Sonoma County 388,222 414,569 101.6 4082.4

CA Vallejo-Fairfield-Napa, CA PMSA Napa County 451,186 481,885 117.6 4097.5

Solano County

CA San Luis Obispo-Atascadero-Paso Robles, CA MSA San Luis Obispo County 217,162 226,071 26.4 8558.6

CA Santa Barbara-Santa Maria-Lompoc, CA MSA Santa Barbara County 369,608 381,401 53.8 7092.6

CA Stockton-Lodi, CA MSA San Joaquin County 480,628 523,969 144.6 3624.5

CA Visalia-Tulare-Porterville, CA MSA Tulare County 311,921 346,843 27.8 12495.0

CA Yuba City, CA MSA Sutter County 122,643 136,104 42.6 3193.9

Yuba County

CO Colorado Springs, CO MSA El Paso County 397,014 465,800 84.6 5508.1

CO Boulder-Longmont, CO PMSA Boulder County 225,339 253,850 132.0 1923.0

CO Denver, CO PMSA Adams County 1,622,980 1,831,308 188.0 9740.6

Arapahoe County

Denver County

Douglas County

Jefferson County

CO Denver-Boulder-Greeley, CO CMSA Boulder County 1,980,140 2,233,172 101.6 21981.2

Adams County

Arapahoe County

Denver County

Douglas County

Jefferson County

Weld County

CO Greeley, CO PMSA Weld County 131,821 148,014 14.3 10341.3

CO Fort Collins-Loveland, CO MSA Larimer County 186,136 217,215 32.2 6737.7

CO Grand Junction, CO MSA Mesa County 93,145 106,548

CO Pueblo, CO MSA Pueblo County 123,051 129,759 21.0 6187.0

CT Hartford, CT NECMA Hartford County (pt.) 1,123,678 1,115,223 282.5 3947.1

Litchfield County (pt.)

Middlesex County (pt.)

Tolland County (pt.)

CT Bridgeport, CT PMSA Fairfield County (pt.) 1,631,864 3190.0

New Haven County (pt.)

CT Danbury, CT PMSA Fairfield County (pt.) 1,001,737 4003.8

Litchfield County (pt.)

CT New Haven-Bridgeport-Stamford-Waterbury-Danbury, CT NECMA Middlesex County (pt.) 1,631,864 1,625,513 505.3 3216.8

New Haven County (pt.)

Fairfield County (pt.)

CT Stamford-Norwalk, CT PMSA Fairfield County (pt.) 827,645 1621.0

CT Waterbury, CT PMSA Litchfield County (pt.) 978,311 3951.8

New Haven County (pt.)

CT-RI New London-Norwich, CT-RI NECMA New London County, CT (pt.) 254,957 250,404

DC-MD-VA-WV Washington, DC-MD-VA-WV PMSA District of Columbia 4,223,485 4,509,932 267.5 16862.7

Calvert County, MD

Charles County, MD

Frederick County, MD

Montgomery County, MD

Prince George's County, MD

Arlington County, VA

Clarke County, VA

Culpeper County, VA

Fairfax County, VA

Fauquier County, VA

King George County, VA

Loudoun County, VA

Prince William County, VA

B-2
Appendix B (continued)

Metropolitan Statistical Areas, Primary Metropolitan Statistical Areas, Consolidated Metropolitan 

Statistical Areas, and New England County Metropolitan Statistical Areas in the United States

State Metropolitan Area TYPE Counties

1990 

Population

1995 Est. 

Population

1995 pop 

density 

(km-2) Area (km2)

Spotsylvania County, VA

Stafford County, VA

Warren County, VA

Berkeley County, WV

Jefferson County, WV

DC-MD-VA-WV Washington-Baltimore, DC-MD-VA-WV CMSA Anne Arundel County 6,726,395 7,107,116 286.5 24809.5

Baltimore County

Carroll County

Harford County

Howard County

Queen Anne's County

Washington County

District of Columbia

Calvert County

Charles County

Frederick County

Montgomery County

Prince George's County

Arlington County

Clarke County

Culpeper County

Fairfax County

Fauquier County

King George County

Loudoun County

Prince William County

Spotsylvania County

Stafford County

Warren County

Berkeley County

Jefferson County

DE Dover, DE MSA Kent County 110,993 121,725 79.6 1529.8

DE-MD Wilmington-Newark, DE-MD PMSA New Castle County 513,293 546,063 272.2 2005.9

Cecil County

FL Daytona Beach, FL MSA Flagler County 399,413 448,904 108.9 4120.4

Volusia County

FL Fort Myers-Cape Coral, FL MSA Lee County 335,113 375,381 180.4 2081.3

FL Fort Pierce-Port St. Lucie, FL MSA Martin County 251,071 283,552 97.0 2921.9

St. Lucie County

FL Fort Walton Beach, FL MSA Okaloosa County 143,776 163,707 67.5 2423.7

FL Gainesville, FL MSA Alachua County 181,596 196,106 86.6 2264.4

FL Jacksonville, FL MSA Clay County 906,727 979,045 143.4 6826.3

Duval County

Nassau County

St. Johns County

FL Lakeland-Winter Haven, FL MSA Polk County 405,382 436,701 89.9 4856.1

FL Melbourne-Titusville-Palm Bay, FL MSA Brevard County 398,978 450,646 170.8 2637.9

FL Fort Lauderdale, FL PMSA Broward County 1,255,488 1,412,165 451.0 3131.0

FL Miami, FL PMSA Dade County 1,937,094 2,031,336 403.4 5036.2

FL Miami-Fort Lauderdale, FL CMSA Broward County 3,192,725 3,443,501

Dade County

FL Naples, FL MSA Collier County 152,099 181,381 34.6 5245.9

FL Ocala, FL MSA Marion County 194,833 226,678 55.4 4089.6

FL Orlando, FL MSA Lake County 1,224,852 1,390,574 153.8 9041.6

Orange County

Osceola County

Seminole County

FL Panama City, FL MSA Bay County 126,994 142,690 72.1 1978.1

FL Pensacola, FL MSA Escambia County 344,406 377,914 86.9 4349.8

Santa Rosa County

FL Punta Gorda, FL MSA Charlotte County 110,975 129,381 72.0 1796.6

FL Sarasota-Bradenton, FL MSA Manatee County 489,483 525,806 154.6 3400.6

Sarasota County

FL Tallahassee, FL MSA Gadsden County 233,598 257,295 84.0 3063.8

Leon County

FL Tampa-St. Petersburg-Clearwater, FL MSA Hernando County 2,067,959 2,180,484 329.6 6616.1

Hillsborough County

Pasco County

Pinellas County

B-3
Appendix B (continued)

Metropolitan Statistical Areas, Primary Metropolitan Statistical Areas, Consolidated Metropolitan 

Statistical Areas, and New England County Metropolitan Statistical Areas in the United States

State Metropolitan Area TYPE Counties

1990 

Population

1995 Est. 

Population

1995 pop 

density 

(km-2) Area (km2)

FL West Palm Beach-Boca Raton, FL MSA Palm Beach County 863,518 972,093 184.5 5268.9

GA Albany, GA MSA Dougherty County 112,571 117,433 66.1 1775.4

Lee County

GA Athens, GA MSA Clarke County 126,262 134,793 88.1 1530.8

Madison County

Oconee County

GA Atlanta, GA MSA Barrow County 2,959,950 3,431,983 216.3 15866.8

Bartow County

Carroll County

Cherokee County

Clayton County

Cobb County

Coweta County

DeKalb County

Douglas County

Fayette County

Forsyth County

Fulton County

Gwinnett County

Henry County

Newton County

Paulding County

Pickens County

Rockdale County

Spalding County

Walton County

GA Macon, GA MSA Bibb County 290,909 309,756 78.1 3967.9

Houston County

Jones County

Peach County

Twiggs County

GA Savannah, GA MSA Bryan County 258,060 279,468 79.2 3526.6

Chatham County

Effingham County

GA-AL Columbus, GA-AL MSA Russell County, AL 260,860 272,380 67.0 4066.3

Chattahoochee County, GA

Harris County, GA

Muscogee County, GA

GA-SC Augusta-Aiken, GA-SC MSA Columbia County, GA 415,220 453,209 52.4 8643.7

McDuffie County, GA

Richmond County, GA

Aiken County, SC

Edgefield County, SC

HI Honolulu, HI MSA Honolulu County 836,231 877,198 564.3 1554.5

IA Cedar Rapids, IA MSA Linn County 168,767 178,559 96.1 1858.4

IA Des Moines, IA MSA Dallas County 392,928 421,447 94.2 4474.7

Polk County

Warren County

IA Dubuque, IA MSA Dubuque County 86,403 88,566 56.2 1575.3

IA Iowa City, IA MSA Johnson County 96,119 101,291 63.6 1591.7

IA Waterloo-Cedar Falls, IA MSA Black Hawk County 123,798 123,077 83.8 1469.5

IA-IL Davenport-Moline-Rock Island, IA-IL MSA Henry County, IL 350,861 358,243 81.0 4423.6

Rock Island County, IL

Scott County, IA

IA-NE Sioux City, IA-NE MSA Woodbury County, IA 115,018 120,033 40.8 2943.8

Dakota County, NE

ID Boise City, ID MSA Ada County 295,851 360,341 84.6 4260.0

Canyon County

IL Bloomington-Normal, IL MSA McLean County 129,180 139,274 45.4 3065.6

IL Champaign-Urbana, IL MSA Champaign County 173,025 169,096 65.5 2582.7

IL Chicago, IL PMSA Cook County 7,410,858 7,724,770 588.9 13118.3

DeKalb County

DuPage County

Grundy County

Kane County

Kendall County

Lake County

McHenry County

Will County

B-4
Appendix B (continued)

Metropolitan Statistical Areas, Primary Metropolitan Statistical Areas, Consolidated Metropolitan 

Statistical Areas, and New England County Metropolitan Statistical Areas in the United States

State Metropolitan Area TYPE Counties

1990 

Population

1995 Est. 

Population

1995 pop 

density 

(km-2) Area (km2)

IL-IN-WI Chicago-Gary-Kenosha, IL-IN-WI CMSA Cook County 8,239,820 8,589,913 475.6 18060.0

DeKalb County

DuPage County

Grundy County

Kane County

Kendall County

Lake County

McHenry County

Will County

Lake County

Porter County

Kankakee County

Kenosha County

IL Kankakee, IL PMSA Kankakee County 96,255 102,046 58.2 1754.7

IL Decatur, IL MSA Macon County 117,206 116,414 77.4 1503.6

IL Peoria-Pekin, IL MSA Peoria County 339,172 345,555 74.3 4653.0

Tazewell County

Woodford County

IL Rockford, IL MSA Boone County 329,676 350,538 87.1 4025.2

Ogle County

Winnebago County

IL Springfield, IL MSA Menard County 189,550 197,015 64.3 3062.8

Sangamon County

IN Bloomington, IN MSA Monroe County 108,978 115,208 112.8 1021.4

IN Gary, IN PMSA Lake County 604,526 623,159 262.9 2370.5

Porter County

IN Elkhart-Goshen, IN MSA Elkhart County 156,198 166,994 139.0 1201.3

IN Fort Wayne, IN MSA Adams County 456,281 471,508 74.4 6339.5

Allen County

DeKalb County

Huntington County

Wells County

Whitley County

IN Indianapolis, IN MSA Boone County 1,380,491 1,476,865 161.8 9125.4

Hamilton County

Hancock County

Hendricks County

Johnson County

Madison County

Marion County

Morgan County

Shelby County

IN Kokomo, IN MSA Howard County 96,946 100,226 69.9 1433.5

Tipton County

IN Lafayette, IN MSA Clinton County 161,572 167,879 71.6 2343.9

Tippecanoe County

IN Muncie, IN MSA Delaware County 119,659 118,577 116.4 1018.7

IN South Bend, IN MSA St. Joseph County 247,052 258,083 217.9 1184.5

IN Terre Haute, IN MSA Clay County 147,585 149,769 56.8 2636.3

Vermillion County

Vigo County

IN-KY Evansville-Henderson, IN-KY MSA Posey County, IN 278,990 288,369 75.9 3800.4

Vanderburgh County, IN

Warrick County, IN

Henderson County, KY

KS Lawrence, KS MSA Douglas County 81,798 88,206 74.5 1183.5

KS Topeka, KS MSA Shawnee County 160,976 165,062 115.9 1424.1

KS Wichita, KS MSA Butler County 485,270 508,224 66.1 7686.7

Harvey County

Sedgwick County

KY Lexington, KY MSA Bourbon County 405,936 435,736 87.6 4973.0

Clark County

Fayette County

Jessamine County

Madison County

Scott County

Woodford County

KY Owensboro, KY MSA Daviess County 87,189 90,662 75.7 1197.7

KY-IN Louisville, KY-IN MSA Clark County, IN 948,829 987,102 183.9 5367.2

B-5
Appendix B (continued)

Metropolitan Statistical Areas, Primary Metropolitan Statistical Areas, Consolidated Metropolitan 

Statistical Areas, and New England County Metropolitan Statistical Areas in the United States

State Metropolitan Area TYPE Counties

1990 

Population

1995 Est. 

Population

1995 pop 

density 

(km-2) Area (km2)

Floyd County, IN

Harrison County, IN

Scott County, IN

Bullitt County, KY

Jefferson County, KY

Oldham County, KY

LA Alexandria, LA MSA Rapides Parish 131,556 127,167 37.1 3425.7

LA Baton Rouge, LA MSA Ascension Parish 528,264 563,994 137.3 4109.1

East Baton Rouge Parish

Livingston Parish

West Baton Rouge Parish

LA Houma, LA MSA Lafourche Parish 182,842 188,757 31.1 6060.3

Terrebonne Parish

LA Lafayette, LA MSA Acadia Parish 344,953 365,857 54.5 6717.9

Lafayette Parish

St. Landry Parish

St. Martin Parish

LA Lake Charles, LA MSA Calcasieu Parish 168,134 175,868 63.4 2774.4

LA Monroe, LA MSA Ouachita Parish 142,191 146,826 92.8 1582.5

LA New Orleans, LA MSA Jefferson Parish 1,285,270 1,315,294 149.4 8804.8

Orleans Parish

Plaquemines Parish

St. Bernard Parish

St. Charles Parish

St. James Parish

St. John the Baptist Parish

St. Tammany Parish

LA Shreveport-Bossier City, LA MSA Bossier Parish 376,330 379,778 63.3 5999.7

Caddo Parish

Webster Parish

MA Barnstable-Yarmouth, MA NECMA Barnstable County (pt.) 186,605 199,804 194.9 1025.0

MA Brockton, MA PMSA Bristol County (pt.) 1,557,688 4186.0

Norfolk County (pt.)

Plymouth County (pt.)

MA Fitchburg-Leominster, MA PMSA Middlesex County (pt.) 2,108,173 6052.1

Worcester County (pt.)

MA New Bedford, MA PMSA Bristol County (pt.) 941,601 3151.1

Plymouth County (pt.)

MA Pittsfield, MA NECMA Berkshire County (pt.) 139,352 135,743 56.3 2412.3

MA Springfield, MA NECMA Hampden County (pt.) 602,878 592,587 199.6 2968.1

Hampshire County (pt.)

MA-CT Worcester, MA-CT PMSA Windham County, CT (pt.) 1,268,540 6849.2

Hampden County, MA (pt.)

Worcester County, MA (pt.)

MA-NH Boston-Worcester-Lawrence-Lowell-Brockton, MA-NH NECMA Bristol County, MA (pt.) 5,685,763 5,768,968 345.7 16689.9

Essex County, MA (pt.)

Middlesex County, MA (pt.)

Norfolk County, MA (pt.)

Plymouth County, MA (pt.)

Suffolk County, MA

Worcester County, MA (pt.)

Rockingham County, NH (pt.)

Hillsborough County, NH

Strafford County, NH

MA-NH Lawrence, MA-NH PMSA Essex County, MA (pt.) 915,925 3090.7

Rockingham County, NH (pt.)

MA-NH Lowell, MA-NH PMSA Middlesex County, MA (pt.) 1,734,541 4403.1

Hillsborough County, NH (pt.)

MA-WI Duluth-Superior, MN-WI MSA St. Louis County, MN 239,971 19515.4

Douglas County, WI

MD Baltimore, MD PMSA Anne Arundel County 2,382,172 2,469,985 365.5 6758.1

Baltimore County

Carroll County

Harford County

Howard County

Queen Anne's County

MD Hagerstown, MD PMSA Washington County 121,393 127,199 107.2 1186.6

MD-WV Cumberland, MD-WV MSA Allegany County, MD 101,643 101,275 51.9 1950.6

B-6
Appendix B (continued)

Metropolitan Statistical Areas, Primary Metropolitan Statistical Areas, Consolidated Metropolitan 

Statistical Areas, and New England County Metropolitan Statistical Areas in the United States

State Metropolitan Area TYPE Counties

1990 

Population

1995 Est. 

Population

1995 pop 

density 

(km-2) Area (km2)

Mineral County, WV

ME Bangor, ME NECMA Penobscot County (pt.) 146,601 145,905 13.7 10685.9

ME Lewiston-Auburn, ME NECMA Androscoggin County (pt.) 105,259 103,751 85.2 1218.1

ME Portland, ME NECMA Cumberland County (pt.) 243,135 248,526 52.5 4730.8

MI Benton Harbor, MI MSA Berrien County 161,378 162,623 110.0 1479.0

MI Ann Arbor, MI PMSA Lenawee County 490,058 522,916 99.5 5255.2

Livingston County

Washtenaw County

MI Detroit, MI PMSA Lapeer County 4,266,654 4,320,203 428.0 10093.7

Macomb County

Monroe County

Oakland County

St. Clair County

Wayne County

MI Detroit-Ann Arbor-Flint, MI CMSA Lenawee County 5,187,171 5,279,500 308.9 17093.9

Livingston County

Washtenaw County

LaPeer County

Macomb County

Monroe County

Oakland County

St. Clair County

Wayne County

Genesee County

MI Flint, MI PMSA Genesee County 430,459 436,381 263.4 1656.7

MI Grand Rapids-Muskegon-Holland, MI MSA Allegan County 937,891 997,895 139.7 7145.0

Kent County

Muskegon County

Ottawa County

MI Jackson, MI MSA Jackson County 149,756 154,010 84.1 1830.2

MI Kalamazoo-Battle Creek, MI MSA Calhoun County 429,453 443,253 90.9 4873.8

Kalamazoo County

Van Buren County

MI Lansing-East Lansing, MI MSA Clinton County 432,674 437,633 99.0 4421.8

Eaton County

Ingham County

MI Saginaw-Bay City-Midland, MI MSA Bay County 399,320 403,572 87.8 4595.8

Midland County

Saginaw County

MN-WI Duluth-Superior, MN-WI MSA St. Louis County 239,971 239,921

Douglas County

MN Rochester, MN MSA Olmsted County 106,470 112,619 66.6 1691.4

MN St. Cloud, MN MSA Benton County 148,976 158,802 35.0 4540.0

Stearns County

MN-WI Minneapolis-St. Paul, MN-WI MSA Anoka County, MN 2,538,834 2,723,137 173.4 15706.9

Carver County, MN

Chisago County, MN

Dakota County, MN

Hennepin County, MN

Isanti County, MN

Ramsey County, MN

Scott County, MN

Sherburne County, MN

Washington County, MN

Wright County, MN

Pierce County, WI

St. Croix County, WI

MO Columbia, MO MSA Boone County 112,379 123,742 69.7 1775.1

MO Joplin, MO MSA Jasper County 134,910 143,804 43.8 3279.7

Newton County

MO St. Joseph, MO MSA Andrew County 97,715 97,679 44.6 2188.5

Buchanan County

MO Springfield, MO MSA Christian County 264,346 294,526 62.1 4743.9

Greene County

Webster County

MO-IL St. Louis, MO-IL MSA Clinton County, IL 2,511,698 2,547,686 137.9 18481.2

Jersey County, IL

Madison County, IL

Monroe County, IL

B-7
Appendix B (continued)

Metropolitan Statistical Areas, Primary Metropolitan Statistical Areas, Consolidated Metropolitan 

Statistical Areas, and New England County Metropolitan Statistical Areas in the United States

State Metropolitan Area TYPE Counties

1990 

Population

1995 Est. 

Population

1995 pop 

density 

(km-2) Area (km2)

St. Clair County, IL

Franklin County, MO

Jefferson County, MO

Lincoln County, MO

St. Charles County, MO

St. Louis County, MO

Warren County, MO

MO-KS Kansas City, MO-KS MSA Johnson County, KS 1,582,875 1,663,453 118.8 14003.3

Leavenworth County, KS

Miami County, KS

Wyandotte County, KS

Cass County, MO

Clay County, MO

Clinton County, MO

Jackson County, MO

Lafayette County, MO

Platte County, MO

Ray County, MO

MS Biloxi-Gulfport-Pascagoula, MS MSA Hancock County 312,368 341,548 73.9 4622.0

Harrison County

Jackson County

MS Hattiesburg, MS MSA Forrest County 98,738 106,195

Lamar County

MS Jackson, MS MSA Hinds County 395,396 416,297 68.0 6120.2

Madison County

Rankin County

MT Billings, MT MSA Yellowstone County 113,419 124,655 18.3 6825.2

MT Great Falls, MT MSA Cascade County 77,691 81,091 11.6 6987.9

MY Casper, WY MSA Natrona County 61,226 64,025 4.6 13830.8

NC Asheville, NC MSA Buncombe County 191,774 207,448 72.4 2863.8

Madison County

NC Fayetteville, NC MSA Cumberland County 274,566 285,869 169.0 1691.6

NC Goldsboro, NC MSA Wayne County 104,666 110,174 77.0 1431.2

NC Greensboro--Winston-Salem--High Point, NC MSA Alamance County 1,050,304 1,123,840 111.8 10056.6

Davidson County

Davie County

Forsyth County

Guilford County

Randolph County

Stokes County

Yadkin County

NC Greenville, NC MSA Pitt County 107,924 117,740 69.8 1687.7

NC Hickory-Morganton-Lenoir, NC MSA Alexander County 292,409 310,236 73.1 4244.3

Burke County

Caldwell County

Catawba County

NC Jacksonville, NC MSA Onslow County 149,838 143,324 72.2 1986.2

NC Raleigh-Durham-Chapel Hill, NC MSA Chatham County 855,545 995,256 110.1 9041.7

Durham County

Franklin County

Johnston County

Orange County

Wake County

NC Rocky Mount, NC MSA Edgecombe County 133,235 141,932 52.4 2707.6

Nash County

NC Wilmington, NC MSA Brunswick County 171,269 200,610 389.3 515.3

New Hanover County

NC-SC Charlotte-Gastonia-Rock Hill, NC-SC MSA Cabarrus County, NC 1,162,140 1,289,177 147.3 8750.5

Gaston County, NC

Lincoln County, NC

Mecklenburg County, NC

Rowan County, NC

Union County, NC

York County, SC

ND Bismarck, ND MSA Burleigh County 83,831 89,440 9.7 9219.4

Morton County

ND-MN Fargo-Moorhead, ND-MN MSA Clay County 153,296 163,618 22.5 7280.6

Cass County

ND-MN Grand Forks, ND-MN MSA Polk County 103,181 104,571 11.8 8827.7

B-8
Appendix B (continued)

Metropolitan Statistical Areas, Primary Metropolitan Statistical Areas, Consolidated Metropolitan 

Statistical Areas, and New England County Metropolitan Statistical Areas in the United States

State Metropolitan Area TYPE Counties

1990 

Population

1995 Est. 

Population

1995 pop 

density 

(km-2) Area (km2)

Grand Forks County

NE Lincoln, NE MSA Lancaster County 213,641 228,638 105.2 2172.7

NE-IA Omaha, NE-IA MSA Pottawattamie County, IA 639,580 670,322 104.5 6412.4

Cass County, NE

Douglas County, NE

Sarpy County, NE

Washington County, NE

NH Manchester, NH PMSA Hillsborough County (pt.) 701,923 6491.2

Merrimack County (pt.)

Rockingham County (pt.)

NH Nashua, NH PMSA Hillsborough County 581,918 4070.7

NH-ME Portsmouth-Rochester, NH-ME PMSA York County, ME (pt.) 514,665 5322.6

Rockingham County, NH (pt.)

Strafford County, NH (pt.)

NJ Bergen-Passaic, NJ PMSA Bergen County 1,278,440 1,308,655 1205.2 1085.9

Passaic County

NJ Jersey City, NJ PMSA Hudson County 553,099 550,183 4553.2 120.8

NJ Middlesex-Somerset-Hunterdon, NJ PMSA Hunterdon County 1,019,835 1,080,450 399.0 2707.7

Middlesex County

Somerset County

NJ Monmouth-Ocean, NJ PMSA Monmouth County 986,327 1,050,052 365.8 2870.3

Ocean County

NJ Newark, NJ PMSA Essex County 1,915,928 1,936,096 473.8 4086.5

Morris County

Sussex County

Union County

Warren County

NJ Trenton, NJ PMSA Mercer County 325,824 330,305 564.4 585.2

NJ Atlantic-Cape May, NJ PMSA Atlantic County 319,416 332,336 157.2 2114.4

Cape May County

NJ Vineland-Millville-Bridgeton, NJ PMSA Cumberland County 138,053 138,058 108.9 1267.3

NM Albuquerque, NM MSA Bernalillo County 589,131 659,855 42.9 15393.6

Sandoval County

Valencia County

NM Las Cruces, NM MSA Dona Ana County 135,510 158,849 16.1 9861.3

NM Santa Fe, NM MSA Los Alamos County 117,043 135,018 25.8 5228.5

Santa Fe County

NV Reno, NV MSA Washoe County 254,667 290,833 17.7 16426.9

NV-AZ Las Vegas, NV-AZ MSA Mohave County 852,737 1,138,758 11.2 101969.1

Clark County

Nye County

NY Albany-Schenectady-Troy, NY MSA Albany County 861,623 873,361 104.6 8346.3

Montgomery County

Rensselaer County

Saratoga County

Schenectady County

Schoharie County

NY Binghamton, NY MSA Broome County 264,497 257,403 81.1 3174.3

Tioga County

NY Buffalo-Niagara Falls, NY MSA Erie County 1,189,288 1,184,052 291.6 4060.2

Niagara County

NY Elmira, NY MSA Chemung County 95,195 94,082 89.0 1057.2

NY Glens Falls, NY MSA Warren County 118,539 122,559 27.7 4416.6

Washington County

NY Jamestown, NY MSA Chautauqua County 141,895 141,677 51.5 2751.0

NY Dutchess County, NY PMSA Dutchess County 259,462 262,062 126.2 2076.3

NY Nassau-Suffolk, NY PMSA Nassau County 1,321,864 2,659,476 1126.9 2360.1

Suffolk County

NY New York, NY PMSA Bronx County 8,546,846 8,570,212 2883.4 2972.3

Kings County

New York County

Putnam County

Queens County

Richmond County

Rockland County

Westchester County

NY-NJ-CT-PA New York-No. New Jersey-Long Island, NY-NJ-CT-PA CMSA Bergen County 17,830,586 18,107,235 779.7 23221.9

Passaic County

Fairfield County

B-9
Appendix B (continued)

Metropolitan Statistical Areas, Primary Metropolitan Statistical Areas, Consolidated Metropolitan 

Statistical Areas, and New England County Metropolitan Statistical Areas in the United States

State Metropolitan Area TYPE Counties

1990 

Population

1995 Est. 

Population

1995 pop 

density 

(km-2) Area (km2)

New Haven County

Litchfield County

Dutchess County

Hudson County

Hunterdon County

Middlesex County

Somerset County

Monmouth County

Ocean County

Nassau County

Suffolk County

Middlesex County

Bronx County

Kings County

New York County

Putnam County

Queens County

Richmond County

Rockland County

Westchester County

Essex County

Morris County

Sussex County

Union County

Warren County

Orange County

Pike County

Mercer County

NY Rochester, NY MSA Genesee County 1,062,470 1,088,516 122.7 8872.5

Livingston County

Monroe County

Ontario County

Orleans County

Wayne County

NY Syracuse, NY MSA Cayuga County 742,177 750,090 93.9 7984.5

Madison County

Onondaga County

Oswego County

NY Utica-Rome, NY MSA Herkimer County 316,633 308,562 45.4 6797.7

Oneida County

NY-PA Newburgh, NY-PA PMSA Orange County, NY 335,613 359,744 101.9 3531.3

Pike County, PA

OH Canton-Massillon, OH MSA Carroll County 394,106 403,695 160.5 2514.5

Stark County

OH Hamilton-Middletown, OH PMSA Butler County 291,479 315,601 260.8 1210.3

OH Akron, OH PMSA Portage County 657,575 678,834 289.5 2344.5

Summit County

OH-KY-IN Cincinnati-Hamilton, OH-KY-IN CMSA Dearborn County 1,817,569 1,907,438 192.4 9914.5

Ohio County

Boone County

Campbell County

Gallatin County

Grant County

Kenton County

Pendleton County

Brown County

Clermont County

Hamilton County

Warren County

Butler County

OH Cleveland-Akron, OH CMSA Portage County 2,859,644 2,903,808 309.5 9383.5

Summit County

Ashtabula Caounty

Cuyahoga County

Geauga County

Lake County

Lorain County

Medina County

OH Cleveland-Lorain-Elyria, OH PMSA Ashtabula County 2,202,069 2,224,974 317.3 7012.4

B-10
Appendix B (continued)

Metropolitan Statistical Areas, Primary Metropolitan Statistical Areas, Consolidated Metropolitan 

Statistical Areas, and New England County Metropolitan Statistical Areas in the United States

State Metropolitan Area TYPE Counties

1990 

Population

1995 Est. 

Population

1995 pop 

density 

(km-2) Area (km2)

Cuyahoga County

Geauga County

Lake County

Lorain County

Medina County

OH Columbus, OH MSA Delaware County 1,345,450 1,437,512 176.6 8138.2

Fairfield County

Franklin County

Licking County

Madison County

Pickaway County

OH Dayton-Springfield, OH MSA Clark County 951,270 956,412 219.3 4360.7

Greene County

Miami County

Montgomery County

OH Lima, OH MSA Allen County 154,340 156,276 74.9 2086.8

Auglaize County

OH Mansfield, OH MSA Crawford County 174,007 176,154 75.6 2329.4

Richland County

OH Toledo, OH MSA Fulton County 614,128 612,798 173.4 3534.3

Lucas County

Wood County

OH Youngstown-Warren, OH MSA Columbiana County 600,895 602,608 148.8 4049.8

Mahoning County

Trumbull County

OH-KY-IN Cincinnati, OH-KY-IN PMSA Dearborn County, IN 1,526,092 1,591,837 183.9 8656.5

Ohio County, IN

Boone County, KY

Campbell County, KY

Gallatin County, KY

Grant County, KY

Kenton County, KY

Pendleton County, KY

Brown County, OH

Clermont County, OH

Hamilton County, OH

Warren County, OH

OH-WV Steubenville-Weirton, OH-WV MSA Jefferson County, OH 142,523 139,862 92.9 1506.2

Brooke County, WV

Hancock County, WV

OK Enid, OK MSA Garfield County 56,735 57,330 20.9 2741.5

OK Lawton, OK MSA Comanche County 111,486 115,672 41.8 2769.8

OK Oklahoma City, OK MSA Canadian County 958,839 1,015,174 92.3 11000.8

Cleveland County

Logan County

McClain County

Oklahoma County

Pottawatomie County

OK Tulsa, OK MSA Creek County 708,954 746,500 57.5 12988.7

Osage County

Rogers County

Tulsa County

Wagoner County

OR Eugene-Springfield, OR MSA Lane County 282,912 303,426 25.7 11795.3

OR Medford-Ashland, OR MSA Jackson County 146,389 166,060 23.0 7214.1

OR Salem, OR PMSA Marion County 278,024 311,722 62.5 4988.5

Polk County

OR-WA Portland-Salem, OR-WA CMSA Clackamas County 1,793,476 2,021,982 112.4 17984.9

Columbia County

Multnomah County

Washington County

Yamhill County

Clark County

Marion County

Polk County

OR-WA Portland-Vancouver, OR-WA PMSA Clackamas County 1,515,452 1,710,260 131.3 13021.6

Columbia County

Multnomah County

Washington County

B-11
Appendix B (continued)

Metropolitan Statistical Areas, Primary Metropolitan Statistical Areas, Consolidated Metropolitan 

Statistical Areas, and New England County Metropolitan Statistical Areas in the United States

State Metropolitan Area TYPE Counties

1990 

Population

1995 Est. 

Population

1995 pop 

density 

(km-2) Area (km2)

Yamhill County

Clark County

PA Allentown-Bethlehem-Easton, PA MSA Carbon County 595,208 613,466 214.7 2857.0

Lehigh County

Northampton County

PA Altoona, PA MSA Blair County 130,542 131,647 96.7 1362.0

PA Erie, PA MSA Erie County 275,572 280,460 135.0 2077.2

PA Harrisburg-Lebanon-Carlisle, PA MSA Cumberland County 587,986 612,617 118.8 5156.4

Dauphin County

Lebanon County

Perry County

PA Johnstown, PA MSA Cambria County 241,247 240,644 52.7 4565.8

Somerset County

PA Lancaster, PA MSA Lancaster County 422,822 447,521 182.0 2458.2

PA-NJ-DE-MD Philadelphia-Wilmington-Atlantic City, PA-NJ-DE-MD CMSA Atlantic County 5,893,019 5,967,323 386.9 15423.4

Cape May County

Burlington County

Camden County

Gloucester County

Salem County

Bucks County

Chester County

Delaware County

Montgomery County

Philadelphia County

Cumberland County

New Castle County

Cecil County

PA Pittsburgh, PA MSA Allegheny County 2,394,811 2,394,702 200.0 11975.9

Beaver County

Butler County

Fayette County

Washington County

Westmoreland County

PA Reading, PA MSA Berks County 336,523 349,583 157.1 2225.4

PA Scranton--Wilkes-Barre--Hazleton, PA MSA Columbia County 638,466 635,559 109.9 5782.3

Lackawanna County

Luzerne County

Wyoming County

PA Sharon, PA MSA Mercer County 121,003 122,254 70.3 1740.1

PA State College, PA MSA Centre County 123,786 131,968 46.0 2868.7

PA Williamsport, PA MSA Lycoming County 118,710 120,194 37.6 3198.5

PA York, PA MSA York County 339,574 362,793 154.8 2343.0

PA-NJ Philadelphia, PA-NJ PMSA Burlington County, NJ 4,922,257 4,950,866

Camden County, NJ

Gloucester County, NJ

Salem County, NJ

Bucks County, PA

Chester County, PA

Delaware County, PA

Montgomery County, PA

Philadelphia County, PA

RI-MA Providence-Warwick-Pawtucket, RI-MA NECMA Bristol County, MA (pt.) 916,270 907,801 370.1 2452.7

Bristol County, RI

Kent County, RI

Newport County, RI (pt.)

Providence County, RI

Washington County, RI (pt.)

SC Charleston-North Charleston, SC MSA Berkeley County 506,875 506,420 75.4 6712.7

Charleston County

Dorchester County

SC Columbia, SC MSA Lexington County 453,331 481,718 127.6 3774.5

Richland County

SC Florence, SC MSA Florence County 114,344 122,769 59.3 2070.0

SC Greenville-Spartanburg-Anderson, SC MSA Anderson County 830,563 884,306 106.3 8315.8

Cherokee County

Greenville County

Pickens County

Spartanburg County

B-12
Appendix B (continued)

Metropolitan Statistical Areas, Primary Metropolitan Statistical Areas, Consolidated Metropolitan 

Statistical Areas, and New England County Metropolitan Statistical Areas in the United States

State Metropolitan Area TYPE Counties

1990 

Population

1995 Est. 

Population

1995 pop 

density 

(km-2) Area (km2)

SC Myrtle Beach, SC MSA Horry County 144,053 157,902 53.8 2936.3

SC Sumter, SC MSA Sumter County 102,637 106,823 62.0 1723.5

SD Rapid City, SD MSA Pennington County 81,343 87,304 12.1 7190.8

SD Sioux Falls, SD MSA Lincoln County 139,236 153,307 42.7 3593.2

Minnehaha County

TN Jackson, TN MSA Chester County 77,982 83,715 58.0 1442.9

Madison County

TN Knoxville, TN MSA Anderson County 585,960 640,700 101.0 6343.2

Blount County

Knox County

Loudon County

Sevier County

Union County

TN Nashville, TN MSA Cheatham County 985,026 1,093,836 103.7 10549.2

Davidson County

Dickson County

Robertson County

Rutherford County

Sumner County

Williamson County

Wilson County

TN-AR-MS Memphis, TN-AR-MS MSA Crittenden County, AR 1,007,306 1,068,891 137.2 7789.6

DeSoto County, MS

Fayette County, TN

Shelby County, TN

Tipton County, TN

TN-GA Chattanooga, TN-GA MSA Catoosa County, GA 424,347 443,060 93.7 4726.2

Dade County, GA

Walker County, GA

Hamilton County, TN

Marion County, TN

TN-KY Clarksville-Hopkinsville, TN-KY MSA Christian County, KY 169,439 189,477 58.0 3264.8

Montgomery County, TN

TN-VA Johnson City-Kingsport-Bristol, TN-VA MSA Carter County, TN 436,047 454,056 61.2 7421.8

Hawkins County, TN

Sullivan County, TN

Unicoi County, TN

Washington County, TN

Scott County, VA

Washington County, VA

TX Abilene, TX MSA Taylor County 119,655 122,791 51.8 2371.7

TX Amarillo, TX MSA Potter County 187,514 201,012 42.6 4723.9

Randall County

TX Austin-San Marcos, TX MSA Bastrop County 807,964 999,936 115.7 8644.1

Caldwell County

Hays County

Travis County

Williamson County

TX Beaumont-Port Arthur, TX MSA Hardin County 361,226 374,637 67.1 5579.9

Jefferson County

Orange County

TX Brownsville-Harlingen-San Benito, TX MSA Cameron County 260,120 309,578 132.0 2345.4

TX Bryan-College Station, TX MSA Brazos County 121,862 130,486 86.0 1517.3

TX Corpus Christi, TX MSA Nueces County 349,894 378,936 95.8 3956.6

San Patricio County

TX Dallas, TX PMSA Collin County 2,676,248 2,957,910 184.6 16023.3

Dallas County

Denton County

Ellis County

Henderson County

Hunt County

Kaufman County

Rockwall County

TX Dallas-Fort Worth, TX CMSA Collin County 4,037,282 4,449,875 277.7 16023.3

Dallas County

Denton County

Ellis County

Henderson County

Hunt County

B-13
Appendix B (continued)

Metropolitan Statistical Areas, Primary Metropolitan Statistical Areas, Consolidated Metropolitan 

Statistical Areas, and New England County Metropolitan Statistical Areas in the United States

State Metropolitan Area TYPE Counties

1990 

Population

1995 Est. 

Population

1995 pop 

density 

(km-2) Area (km2)

Kaufman County

Rockwall County

TX Fort Worth-Arlington, TX PMSA Hood County 1,361,034 1,491,965 197.4 7557.9

Johnson County

Parker County

Tarrant County

TX El Paso, TX MSA El Paso County 591,610 678,313 258.5 2623.9

TX Brazoria, TX PMSA Brazoria County 191,707 216,016 60.1 3592.0

TX Galveston-Texas City, TX PMSA Galveston County 217,399 237,533 230.0 1032.6

TX Houston, TX PMSA Chambers County 3,322,025 3,710,844 242.0 15335.8

Fort Bend County

Harris County

Liberty County

Montgomery County

Waller County

TX Houston-Galveston-Brazoria, TX CMSA Brazoria County 3,731,029 4,164,393 207.0 20116.5

Galveston County

Chambers County

Fort Bend County

Harris County

Liberty County

Montgomery County

Waller County

TX Killeen-Temple, TX MSA Bell County 255,301 289,903 53.0 5467.1

Coryell County

TX Laredo, TX MSA Webb County 133,239 170,863 19.7 8694.6

TX Longview-Marshall, TX MSA Gregg County 193,801 203,949 44.7 4560.0

Harrison County

Upshur County

TX Lubbock, TX MSA Lubbock County 222,636 232,276 99.7 2330.0

TX McAllen-Edinburg-Mission, TX MSA Hidalgo County 383,545 479,783 118.1 4063.9

TX Odessa-Midland, TX MSA Ector County 225,545 239,245 51.3 4665.8

Midland County

TX San Angelo, TX MSA Tom Green County 98,458 101,555 25.8 3942.5

TX San Antonio, TX MSA Bexar County 1,324,749 1,460,809 169.5 8616.4

Comal County

Guadalupe County

Wilson County

TX Sherman-Denison, TX MSA Grayson County 95,021 98,336 40.7 2418.2

TX Tyler, TX MSA Smith County 151,309 161,986 67.4 2404.8

TX Victoria, TX MSA Victoria County 74,361 79,992 35.0 2285.9

TX Waco, TX MSA McLennan County 189,123 200,111 74.2 2698.6

TX Wichita Falls, TX MSA Archer County 130,351 133,386 33.5 3982.0

Wichita County

TX-AR Texarkana, TX-Texarkana, AR MSA Miller County, AR 120,132 122,991 31.4 3916.1

Bowie County, TX

UT Provo-Orem, UT MSA Utah County 263,590 298,789 57.7 5175.9

UT Salt Lake City-Ogden, UT MSA Davis County 1,072,227 1,199,323 286.3 4189.3

Salt Lake County

Weber County

VA Charlottesville, VA MSA Albemarle County 131,107 142,148 46.6 3048.6

Fluvanna County

Greene County

VA Danville, VA MSA Pittsylvania County 108,711 109,890 41.8 2626.0

VA Lynchburg, VA MSA Amherst County 193,928 204,212 44.0 4638.3

Bedford County

Campbell County

VA Richmond-Petersburg, VA MSA Charles City County 865,640 927,435 121.6 7626.9

Chesterfield County

Dinwiddie County

Goochland County

Hanover County

Henrico County

New Kent County

Powhatan County

Prince George County

VA Roanoke, VA MSA Botetourt County 224,477 228,895 103.9 2203.6

Roanoke County

VA-NC Norfolk-Virginia Beach-Newport News, VA-NC MSA Currituck County, NC 1,443,244 1,540,446 253.2 6083.1

B-14
Appendix B (continued)

Metropolitan Statistical Areas, Primary Metropolitan Statistical Areas, Consolidated Metropolitan 

Statistical Areas, and New England County Metropolitan Statistical Areas in the United States

State Metropolitan Area TYPE Counties

1990 

Population

1995 Est. 

Population

1995 pop 

density 

(km-2) Area (km2)

Gloucester County, VA

Isle of Wight County, VA

James City County, VA

Mathews County, VA

York County, VA

VT Burlington, VT NECMA Chittenden County (pt.) 177,059 188,175 57.7 3259.9

Franklin County (pt.)

Grand Isle County (pt.)

WA Bellingham, WA MSA Whatcom County 127,780 148,929 27.1 5490.9

WA Richland-Kennewick-Pasco, WA MSA Benton County 150,033 177,529 23.3 7628.3

Franklin County

WA Bremerton, WA PMSA Kitsap County 189,731 226,720 221.1 1025.6

WA Olympia, WA PMSA Thurston County 161,238 191,974 101.9 1883.1

WA Seattle-Bellevue-Everett, WA PMSA Island County 2,033,156 2,197,451 191.7 11460.5

King County

Snohomish County

WA Seattle-Tacoma-Bremerton, WA CMSA Kitsap County 2,970,300 3,265,139 174.3 18733.4

Thurston County

Island County

King County

Snohomish County

Pierce County

WA Tacoma, WA PMSA Pierce County 586,203 648,994 149.5 4339.7

WA Spokane, WA MSA Spokane County 361,364 401,205 87.8 4568.3

WA Yakima, WA MSA Yakima County 188,823 212,035 19.1 11126.9

WI Appleton-Oshkosh-Neenah, WI MSA Calumet County 336,073 336,073 92.8 3623.1

Outagamie County

Winnebago County

WI Kenosha, WI PMSA Kenosha County 128,181 139,938 198.0 706.6

WI Eau Claire, WI MSA Chippewa County 137,543 142,663 33.4 4268.7

Eau Claire County

WI Green Bay, WI MSA Brown County 194,594 210,303 153.6 1369.4

WI Janesville-Beloit, WI MSA Rock County 139,510 148,349 79.5 1866.2

WI Madison, WI MSA Dane County 367,085 393,296 126.3 3113.6

WI Milwaukee-Waukesha, WI PMSA Milwaukee County 1,432,149 1,457,939 385.6 3781.3

Ozaukee County

Washington County

Waukesha County

WI Milwaukee-Racine, WI CMSA Milwaukee County 1,607,183 1,640,831 352.9 4649.0

Ozaukee County

Washington County

Waukesha County

Racine County

WI Racine, WI PMSA Racine County 175,034 182,892 212.0 862.8

WI Sheboygan, WI MSA Sheboygan County 103,877 108,326 81.4 1330.4

WI Wausau, WI MSA Marathon County 115,400 120,776 30.2 4001.7

WI-MN La Crosse, WI-MN MSA Houston County, MN 116,401 121,005 46.2 2619.1

La Crosse County, WI

WV Charleston, WV MSA Kanawha County 250,454 255,139 78.8 3236.0

Putnam County

WV-KY-OH Huntington-Ashland, WV-KY-OH MSA Boyd County, KY 312,529 317,489 56.8 5594.1

Carter County, KY

Greenup County, KY

Lawrence County, OH

Cabell County, WV

Wayne County, WV

WV-OH Parkersburg-Marietta, WV-OH MSA Washington County, OH 149,169 152,131 58.6 2596.7

Wood County, WV

WV-OH Wheeling, WV-OH MSA Belmont County, OH 159,301 157,349 63.9 2461.8

Marshall County, WV

Ohio County, WV

WY Cheyenne, WY MSA Laramie County 73,142 78,444 11.3 6957.4

B-15