## 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 PM 2.5 

# And PM 10

# GUIDANCE FOR NETWORK DESIGN AND

OPTIMUM SITE EXPOSURE FOR PM AND PM 2.5 10



# PREPARED BY

John G. Watson 1 

# Judith C. Chow 1 

# David DuBois 1 

# Mark Green 1 

# Neil Frank 2 

# Marc Pitchford 3



# 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 PM 2.5 

# and PM 10 

# 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. PM 2.5 

# monitoring sites are to be population-oriented, measuring exposures where people live, work, and play. For comparison to the annual PM 2.5 

# 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, PM 2.5 

# 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 Implement ation

......................... 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 PM 2.5 

# 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 Plannin g 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 PM 2.5 

# 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 Crit eria

................................ ................................ .......................... 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 Placem ent of Monitors

................................ ................................ ..... 5-5 5.5.4 Chemical Composition of PM 2.5 

# ................................ ................................ ... 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 Monito ring 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 Stati stical 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 meas urement 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 PM 10 

# in Birmingham. 4 -7

Table 4.5.1 Number of required core PM 2.5 

# 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 PM 10 

# in Las

Vegas, NV. 2

- 7

Figure 2.1.5 PM 10 

# concentrations at different nearby sites centered around Fresno,

CA. 2 -8

Figure 2.1.6 Spatial variation in 24 -hour PM 10 

# chemical compositions from the

neighborhood to regional scale. 2 -9

Figure 2.1.7 Normalized PM 10 

# 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 Pot ential 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 w inds. 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 Exa mple 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 μ m (PM 10 

# ) and 2.5 μ m (PM 2.5 

# ) and are described as follows (U.S. EPA, 1997 ):

• Twenty-four hour average PM 2.5 

# not to exceed 65 μ g/m 3 

# for a three-year average of annual 98 th 

# percentiles at any population -oriented monitoring site in a monitoring area.

• Three-year annual average PM 2.5 

# not to exceed 15 μ g/m 3 

# 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 PM 10 

# not to exceed 150 μ g/m 3 

# for a three-year average of annual 99 th 

# percentiles at any monitoring site in a monitoring area.

• Three-year average PM 10 

# not to exceed 50 μ g/m 3 

# for three annual average concentrations at any monitoring site in a monitoring area.

The PM 2.5 

# NAAQS are new. While the PM 10 

# 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 understandingsource 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 PM 10 

# network design and siting criteria are unchanged, new PM 2.5 

# monitoring networks to determine compliance or non-compliance are intended to best represent the exposure of populations that might be affected by elevated PM 2.5 

# 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 PM 2.5 

# measurements that are not eligible for comparison to one or both of the PM 2.5 

# 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 PM 10 

# network will likely decrease as new PM 2.5 

# sites are established. The PM 2.5 

# sites may or may not be collocated with PM 10 

# monitoring locations. This guidance for network design and optimum site exposure of PM 10 

# and PM 2.5 

# 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 PM 10 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# (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 NETWOR K 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 μ m 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 μ m. 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 μ m 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 (micron s )Relative Concentration



## Accumulation Coar s e NucleationCondensationDroplet 

## PM 1 0 

## PM 2.5 

## 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 μ m, the PM 10 

# fraction ranges from 0 to 10 μ m, and the PM 2.5 

# size fraction ranges from 0 to 2.5 μ m 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 (PM 2.5 

# ) 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 d ifferent

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 μ m as a “condensation” mode containing gas-phase reaction products. John et al. (1990) interpreted the ~0.7 μ m 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 μ m. A small shift in the 50% cut-point of a PM 10 

# sampler has a large influence on the mass collected because the coarse mode usually peaks near 10 μ m. On the other hand, a similar shift in cut-point near 2.5 μ m has a small effect on the mass collected owing to the low quantities of particles in the 1 to 3 μ m 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 PM 10 

# 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 PM 10 

# and PM 2.5 

# 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 PM 10 

# is often composed of geological material. Geological material often constitutes less than ~10% of the PM 2.5 

# 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 PM 2.5 

# 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 SO 4 = , Na + , K + , P, S, Cl, Mn, Zn, Ba, Ti Elemental Carbon (EC), Al, K, Ca, Fe Organic Carbon(OC), Si

Unpaved Road Dust Coarse NO 3 - , NH 4 + , P, Zn, Sr, Ba SO 4 = , Na + , K + , P, S, Cl, Mn, Ba,Ti OC, Al, K, Ca, Fe Si

Construction Coarse Cr, Mn, Zn, Sr, Ba SO 4 = , K + , S, Ti OC, Al, K, Ca, Fe Si

Agricultural Soil Coarse NO 3 , NH 4 + , Cr, Zn, Sr SO 4 = , 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 SO 4 = , Na + , OC, Al, S, Cl, K, Ca, Fe Si

Motor Vehicle Fine Cr, Ni, Y NH 4 + , Si, Cl, Al, Si, P, Ca, Mn, Fe, Zn, Br, Pb Cl - , NO 3 - , SO 4 = , NH 4 + , S OC, EC

Vegetative Burning Fine Ca, Mn, Fe, Zn, Br, Rb, Pb NO 3 - , SO 4 = , NH 4 + , Na + , S Cl - , K + , Cl, K OC, EC

Residual Oil Combustion Fine K + , OC, Cl, Ti, Cr, Co, Ga, Se NH 4 + , Na + , Zn, Fe, Si V, OC, EC, Ni S, SO 4 =

Incinerator Fine V, Mn, Cu, Ag, Sn K + , Al, Ti, Zn, Hg NO 3 - , Na + , EC, Si, S, Ca, Fe, Br, La, Pb SO 4 = , NH 4 + , OC, Cl

Coal-Fired Boiler Fine Cl, Cr, Mn, Ga, As, Se, Br, Rb, Zr NH 4 + , P, K, Ti, V, Ni, Zn, Sr, Ba, Pb SO 4 = , OC, EC, Al, S, Ca, Fe Si

Oil-Fired Power Plant Fine V, Ni, Se, As, Br, Ba Al, Si, P, K, Zn NH 4 + , OC, EC, Na, Ca, Pb S, SO 4 =

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 SO 4 = , 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 NO 3 - , SO 4 = , 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 μ m, 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 PM 2.5 

# 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
, PM 2.5 

# 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 S tudy (Chow and Egami, 1997 ). This plot shows a distinct diurnal pattern for the 50 th 

# and 80 th 

# 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 PM 2.5 

# in

Bakersfield, CA (Chow and Egami , 1997 ).

midnight. In some communities where fugitive dust is a major emitter, peak PM 10 

# 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 PM 10 

# mass in a saturation monitoring network in California’s San Joaquin Valley (Chow and Egami, 1997 ). During this study, most of the PM 10 

# mass was in the PM 2.5 

# 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 PM 10 

# 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. PM 10 

# concentrations at different nearby sites centered around Fresno, CA.

# 2-9

Figure 2.1.6. Spatial variation in 24 -hour PM 10 

# 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 PM 10 

# concentrations at increasing distances from an unpaved road

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

PM 2.5 

# 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 PM 10 

# at the residential site, but to contribute less than 10% of PM 10 

# 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 PM 10 

# 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 PM 2.5 

# . 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 PM 2.5 

# , 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 PM 2.5 

# 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/m 3



# , and maximum deviations of no more than 3 μg/m 3 

# , at concentrations near the annual PM 2.5 

# standard of 15 μg/m 3



# .
2.2.2 Receptor Site Zone of Representation

PM 10 

# and PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# measurements.
Background monitors are intended to measure PM 2.5 

# 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) PM 2.5 

# 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 PM 2.5 

# 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 PM 10 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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.

# 2-18 • 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 -metr opolitan 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 PM 2.5 

# 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 PM 2.5 

# . 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 PM 2.5 

# 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 PM 2.5 

# , is intended to represent the spatial uniformity of PM 2.5 

# concentrations. In practice, more than one monitor may be needed within each CMZ to evaluate the spatial uniformity of PM 2.5 

# concentrations and to accurately calculate the spatial average for comparison with the annual PM 2.5 

# NAAQS. When spatial averaging is used, each MPA would be completely covered by one or more contiguous CMZs.

2.3.3 Monitoring Netwo rks

PM 2.5 

# 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 PM 2.5 

# 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 PM 10 

# NAMS should generally be good candidates for PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# monitoring sites, especially for transport and background monitoring. MPAs with existing PAMS are to install a PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# measurement networks.

• Community -Oriente d (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 ormiddle-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 PM 2.5 

# 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 PM 2.5 

# standards. Because core sites are the only sites eligible for comparison to both the annual and 24-hour PM 2.5 

# NAAQS, they are the most important sites in the new PM 2.5 

# network. PM 2.5 

# concentrations may be spatially averaged among these sites within a CMZ when the annual average PM 2.5 

# 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 PM 2.5 

# 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) PM 2.5 

# 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 PM 2.5 

# standard. If the monitoring stie is also representative of community-wide air quality, it is eligible for comparison to the annual PM 2.5 

# 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 PM 10 

# 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 de termine 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 de signations 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 PM 2.5 

# NAMS for assessing trends and for performing future epidemiological studies.

# 2-23 • Background Sites : Background sites are intended to represent regional -scale PM 2.5 

# 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 Net work 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 PM 2.5 

# 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 s everal 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 byspecifying 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 assu med 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/m 3 

# with a variance, σ 2 

# , of 6.5 (μg/m 3 

# ) 2 

# the estimated number of samples is found to be
1 96 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 PM 2.5 

# 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 PM 2.5 

# 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, PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PLA NNING AREAS

This section specifies the steps to define the boundaries of Monitoring Planning Areas ( MPAs) for determining compliance with PM 10 

# and PM 2.5 

# 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 PM 2.5 

# and for adapting existing sites to PM 2.5 

# compliance monitoring. It also provides an objective means for identifying PM 10 

# measurement locations that can be discontinued as PM 10 

# 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 km 2 

# to 8,000 km 2 

# , with population densities of ~40 to 100 people/km 2 

# . 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 km 2 

# to >20,000 km 2 

# , with 1990 population densities from 25 people/km 2 

# to >1,100 people/km 2 

# . The most extreme cases in Appendix B are : 1) the Las Vegas MSA that covers more than 100,000 km 2 

# 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 km 2 

# of Hudson County with one of the highest U.S. population densities (>4,500 people/km 2 

# ). 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 east3-3



# 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 (km 2 

### ) 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 (km 2 

## ) 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 (km 2 

## ) 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 or3-9



# 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 Ñ Ñ Ñ Ñ

Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ

Ñ

Ñ Ñ Ñ Ñ Ñ Ñ Ñ

Ñ

Ñ

Ñ Ñ Ñ

Ñ Ñ Ñ Ñ Ñ



# 3 0 0 3 0 6 0 K i l o m e t e r s

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 Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ

Ñ Ñ Ñ

Ñ



# F r e s n o , C A

M S A b o u n d a r i e s Ñ 

# c i t i e s c o u n t y 

# 8 0 0 8 0 1 6 0 K i l o m e t e r s

N

E W

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 #

S

# S

# S # S # S # S # S

# S #

S # S



# B **E S**  **S I**  **N G**  **L**

**N O**  **S C**  **F A**  **I R**  **L E**  **S C** 

**T A**  **S C**  **N O**  **B I** 

**D O**  **L O**  **W Y**  **L A** 

**T A**  **R R**  **N O**  **B S** 

Figure 3.3.2. Potential Monitoring Planning Area for the Birmingham MSA. Dots represent monitoring sites.

# 3-17 # # # # # # #

#

# #

#

# #

#

#

# # #

# # # #

# # #

# # #

# F O V

F P T F S F C L O F C S

F S D H I R

B A C B A K B G S K R F

M O A M O P

O L D T R O

B K A

M O J T A C T A N C O V H A N

K C C L O S M A S

M L B M E R S O H

M I S

C L D M O K W E S

P T V V C S



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

California’s San Joaquin Valley.

# 3-18 #

# # #
#

**C L**  **D M**  **I S**  **M O**  **K**

**W E**  **S S**  **O H** 



## #

# #

#

# #

#

# # #

#

**C L**  **O**

**C O**  **V**

**F O**  **V**

**F P**  **T F**  **S D** 

**F S**  **F**

**H A**  **N**

**H I**  **R M**  **A S** 

**M L**  **B**

**F C**  **S**



## # # # # #

**B A**  **C B**  **A K** 

**B G**  **S**

**B K**  **A O**  **L D**  

# a ) S t o c k t o n /

M o d e s t o b ) F r e s n o / M a d e r a / C l o v i s / S e l m a

c ) B a k e r s f i e l d

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 PM 2.5 

# . 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# or visibility if available; if not, use PM 10 

# or other air pollutant measurements. Combine potential CMZs where these concentrations are similar. When existing PM 2.5 

# 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 PM 2.5 

# 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 -30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30 

# 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 mo rning (0700 -1000 CST), afternoon (1200 -1600 CST), and night (2200 -0500 CST).

# 4-4 Winter Morning Afternoon Night

Spring Morning Afternoon Night

Summer Morning Afternoon Night

Fall Morning Afternoon Night -30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30

-30 -20 -10 0 10 20 30 

# 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 (Se p -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 Comp are PM Concentrations
Few areas possess sufficient PM 2.5 

# measurements to permit comparisons for the first selection of CMZs. PM 10 

# measurements are often available, and where these show acceptable spatial uniformity, it is likely that the PM 2.5 

# would also show homogeneity if it had been measured at the same locations. When the PM 10 

# measurements are non -uniform among different sites, however, it may be the case that PM 2.5 

# 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 PM 10 

# and possibly PM 2.5 

# 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, PM 2.5 

# 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 condi tions, that often accompany high PM levels, is not well measured or modeled. Modeling results need to be extensively evaluated against chemical- and size-specific PM 2.5 

# 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 , PM 2.5 

# 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 PM 10 

# 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 AIRSdata 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/m 3 

# ) 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/m 3 

# ) 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/m 3 

# ) 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 PM 10 

# 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 PM 10 

# in Birmingham.

# 4-8 The first two sub-sections of Table 4.3.1 show the annual arithmetic averages and 98 th 

# 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 PM 10 

# 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 PM 10 

# 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 PM 10 

# concentrations were very different from the annual average and 98 th 

# 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 98 th 

# percentile PM 10 

# levels. In 1995, the BESS, NOSC, and TASC stations near the center of the MPA show almost identical annual averages, and 98 th 

# percentile PM 10 

# concentrations that differ by no more than 6 μg/m 3 

# . The LESC site shows ~3 μg/m 3 

# lower annual PM 10 

# 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/m 3 

# in 1990 to 1.2 μg/m 3 

# 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 PM 10 

# sites can be sacrificed in favor of collocated PM 2.5 

# sites at most of the Jefferson County sites.
Other analyses of historical PM 10 

# and PM 2.5 

# that provide a basis for selecting CMZs, and also serve as a justification for de-commissioning PM 10 

# sites in favor of PM 2.5 

# 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.

# 4-9 • 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 PM 10 

# 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 PM 2.5 

# 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.

# 4-10 MSA Population Number of Core PM 2.5 

# 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 PM 2.5 

# 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 PM 10 

# 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 PM 2.5 

# 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 PM 2.5 

# standards. Second, it encourages the location of other sites within the CMZ boundaries to give a more representative PM 2.5 

# 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.

# 5-1



# 5. 0 MONITOR SITING

PM 2.5 

# 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 -burn ing 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 PM 10 

# monitoring siting (U.S. EPA, 1987 ), and they have proven their validity in PM 10 

# 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, PM 2.5 

# sites should give preference to existing sites that make other measurements. For example, high local wind gusts may

# 5-3

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 PM 10 

# and PM 2.5 

# 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 PM 2.5 

# or PM 10 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# , 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 homogen eous 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.

# 5-6 Figure 5.5.3. Example of spatial averaging.

5.5.4 Chemical Composition of PM 2.5 

# 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 co mpo 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 MONITORIN G NETWORK DESCRIPTIONS, ANNUAL REPORTS, AND NETWORK E VALUATIONS

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 PM 10 

# and the current PAMS network for ozone to maintain continuity with longterm data sets acquired at these sites. PAMS with PM 2.5 

# 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 PM 10 

# and PM 2.5 

# . The phase-in of PM 2.5 

# monitors and changes in the existing PM 10 

# 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 PM 2.5 

# 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 PM 10 

# monitoring sites will be modified by discontinuation or reduction in measurement frequency. Network changes are to be phased in between 1998 and 1999 for PM 2.5 

# and between 1998 and 2000 for PM 10 

# . 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, 98 th 

# 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., PM 10 

# mass, PM 2.5 

# 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 PM 2.5 

# monitors at the expense of PM 10 

# 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 PM 10 

# and PM 2.5 

# annual

averages, maximum concentrations, and 98 th 

# and 99 th 

# 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 PM 2.5 

# 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 98 th 

# and 99 th 

# 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 98 th 

# 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 PM 2.5 

# 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 98 th 

# and 99 th 

# 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 PM 2.5 

# 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 grow ing areas can also be used to determine the extent to which the boundaries of planning areas and averaging zones should be expanded.

# 7-1



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# 7-5

Seigneur, C., P. Pai, J

. - F. Louis, P. Hopke, and D. Grosjean (1997). Review of Air Quality Models for Particulate Matter. Draft Report No. CP015 -97 -1a. Prepared for American Petroleum Institute, Washington, DC, by Atmospheric & Environmental Research Inc., San Ramon, CA. June 30, 1997.

Stalker, W.W., and R.C. Dickerson (1962). Sampling Station and Time Requirements for Urban Air Pollution Surveys, Part III: Two- and Four-Hour Soiling Index. J. Air Poll. Control Assoc.
, 12 :170-178.

Stalker, W.W., R.C. Dickerson , and G.D. Kramer (1962). Sampling Station and Time Requirements for Urban Air Pollution Surveys, Part IV: 2- and 24-Hour Sulfur Dioxide and Summary of Other Pollutants. J. Air Poll. Control Assoc.
, 12 :361-375.

Stull , R.B. (1988). An Introduction to Boundary Layer Meteorology. Norwell, MA: Kluwer Academic.
Trujillo -Ventura, A., and J.H. Ellis (1991). Multiobjective Air Pollution Monitoring Network Design. Atmos. Environ

. , 25A (2):469 -479.

U.S. EPA (1971). National Primary and Secondary Ambient Air Standards, Appendix B: Reference Method for the Determination of Suspended Particulates in the Atmosphere. Federal Register
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U.S. EPA (1987). Revisions to the National Ambient Air Quality Standards for Particulate Matter. 40 CFR Part 50. Federal Register
, 52 :24634. July 1, 1987.

U.S. EPA (1996). National Ambient Air Quality Standards for Particulate Matter – Proposed Rule. 40 CFR Part 50. Federal Register

. December 13, 1996.

U.S. EPA (1997a). National Ambient Air Quality Standards for Particulate Matter – Final Rule. 40 CFR Part 50. Federal Register
, 62 (138) :38651 -38760. July 18, 1997.
U.S. EPA (1997b). Revised Requirements for Designation of Reference and Equivalent Methods for PM 2.5 

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, 62 (138) :38763 -38854. July 18, 1997.

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Volpi , G., and G. Gambolati (1978). On the Use of a Main Trend for the Kriging Technique in Hydrology. Adv. in Water Res. I , pp. 345 -349.

# 7-6

Watson, J.G., J.C. Chow, F. Lurmann , and S. Musarra (1994). Ammonium Nitrate, Nitric Acid, and Ammonia Equilibrium in Wintertime Phoenix, AZ. J. Air & Waste Assoc.
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Watson, J.G., J.C. Chow, J.A. Gillies , H. Moosmuller , C.F. Rogers, D. DuBois, and J. Derby (1996). Effectiveness Demonstration of Fugitive Dust Control Methods for Public Unpaved Roads and Unpaved Shoulders on Paved Roads. DRI Document No. 685 -5200.1F1. Prepared for California Regional Particulate Air Quality Study, California Air Resources Board, Sacramento, CA, by Desert Research Institute, Reno, NV.

Wilkins, E.M. (1971). Variational Principle Applied to Numerical Objective Analysis of Urban Air Pollution Distributions. J. Applied Meteorology
, 10 :974-981

Woldt , W., and I. Bogardi (1992). Ground Water Monitoring Network Design Using Multiple Criteria Decision Making and Geostatistics. Water Res. Bull
., 28 (1):45 -62.

# 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 localphone 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 inthe 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, PM 10 , PM 2.5 ,O 3 , 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 EnvironmentalARC/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 O 3 , CO, NO 2 ,PM 10 ,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 (km 2 ) 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 (km 2 ) 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 (km 2 ) 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 (km 2 ) 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 (km 2 ) 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 (km 2 ) 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 (km 2 ) 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 (km 2 ) 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 (km 2 ) 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 (km 2 ) 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 (km 2 ) 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 (km 2 ) 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 (km 2 ) 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 (km 2 ) 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 (km 2 ) 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



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