United States
Environmental Protection
Agency
Office of Air Quality
Planning and Standards
Research Triangle Park, NC 27711
EPA-454/R-99-022
December 1997
Air
Guidance For Network
Design and Optimum
Site Exposure For PM2 5
And PM10
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GUIDANCE FOR NETWORK DESIGN AND
OPTIMUM SITE EXPOSURE FOR PM2 5 AND PM10
PREPARED BY
John G. Watson1
Judith C. Chow1
David DuBois1
Mark Green1
Neil Frank2
Marc Pitchford3
PREPARED FOR
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
U S Environmental Protection A|tncy
Region 5, Library (PL-12J)
77 West Jackson Boulevard, IZtn
Chicago, IL 60604-3590
December 15, 1997
'Desert Research Institute, University and Community College System of Nevada, PO Box 60220, Reno, NV 89506
2U.S. EPA/OAQPS, Research Triangle Park, NC 27711
'National Oceanic and Atmospheric Administration, 755 E. Flamingo, Las Vegas, NV 89119
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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.
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ABSTRACT
This guidance provides a method and rationale for designing monitoring networks to
determine compliance with newly enacted PM2.s and PMio 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. PMa.s monitoring sites are to be population-oriented, measuring exposures
where people live, work, and play. For comparison to the annual PM2.5 standard, the
locations must be community-oriented and as such, these do not necessarily correspond to the
locations of highest PM concentrations in an area. Existing Metropolitan Statistical Areas are
first examined to determine where the majority of the people live in each state. These are then
broken down into smaller populated entities which may include county, zip code, census tract,
or census block boundaries. Combinations of these population entities are combined to define
Metropolitan Planning Areas. These may be further sub-divided into Community Monitoring
Zones, based on examination of existing PM measurements, source locations, terrain, and
meteorology. Finally, PMj.s 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.
11
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TABLE OF CONTENTS
Page
Disclaimer i
Preface ii
Abstract iii
List of Tables v
List of Figures vi
1.0 Introduction 1-1
1.1 Objectives of Guidance 1-2
1.2 Schedule and Approvals for Network Design and Implementation 1-2
1.3 Related Documents 1-3
2.0 Concepts of Network Design 2-1
2.1 Particle Properties 2-1
2.2 Concepts 2-11
2.2.1 Spatial Uniformity 2-11
2.2.2 Receptor Site Zone of Representation 2-11
2.2.3 Community-Oriented Monitoring 2-13
2.2.4 Background and Regional Transport Monitoring 2-14
2.2.5 Emissions Zone of Influence 2-16
2.2.6 PM2.5 Sampler Types 2-16
2.3 Definitions 2-17
2.3.1 Theoretical Concepts 2-17
2.3.2 Monitoring Boundaries 2-18
2.3.3 Monitoring Networks 2-19
2.3.4 Site Types 2-20
2.4 Network Design Philosophies 2-23
2.4.1 Network Design Objectives 2-23
2.4.2 Random Sampling 2-24
2.4.3 Systematic Sampling 2-24
2.4.4 Judgmental Sampling 2-25
2.4.5 Heterogeneous Siting Strategies 2-25
2.4.6 Other Siting Strategies 2-26
3.0 Defining State Planning Areas 3-1
3.1 Identify Political Boundaries of Populated Areas 3-2
3.2 Identify Natural Air Basins 3-8
3.3 Identify Existing Air Quality Monitoring Sites 3-14
3.4 Reconcile Boundaries with Existing Planning Areas 3-14
3.5 Summary 3-20
4.0 Defining PM2 5 Community Monitoring Zones 4-1
4.1 Locate Emissions Sources 4-2
ui
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4.2 Identify Meteorological Patterns 4-2
4.3 Compare PM Concentrations 4-6
4.4 Adjust CMZs to Jurisdictional Boundaries 4-9
4.5 Locate Sites 4-9
5.0 Monitor Siting 5-1
5.1 Internal Siting Criteria 5-1
5.2 External Siting Criteria 5-2
5.3 Evaluating Zones of Representation '. 5-3
5.4 Evaluating Siting Redundancy ; 5-4
5.5 Evaluating Network Adequacy for Spatial Averaging 5-4
5.5.1 Temporal Behavior 5-4
5.5.2 Consistent trends 5-5
5.5.3 Spatial Placement of Monitors 5-5
5.5.4 Chemical Composition of PM2.5 5-6
5.5.5 Population Density and Air Quality Patterns (additional supporting
evidence) 5-6
6.0 State PM Monitoring Network Descriptions, Annual Reports, and Network
Evaluations 6-1
6.1 State PM Monitoring Network Descriptions 6-1
6.2 Annual Measurement Reports 6-3
6.3 Annual Network Evaluation 6-4
7.0 References 7-1
Appendix A Sources of Data Used for Network Design and Evaluation
Appendix B Metropolitan Statistical Areas, Primary Metropolitan Statistical Areas,
Consolidated Metropolitan Statistical Areas, and New England County
Metropolitan Statistical Areas in the United States
IV
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LIST OF TABLES
Table 2.1.1 Chemicals from particles in different emissions sources.
Table 2.2.1 IMPROVE measurement sites.
Table 3.1.1 Alabama Metropolitan Statistical Areas and Primary Metropolitan
Statistical Areas.
Table 3.1.2 California Metropolitan Statistical Areas and Primary Metropolitan
Statistical Areas.
Table 4.3.1 Uniformity measures for PMio in Birmingham.
Table 4.5.1 Number of required core PM2.s SLAMS monitors per MSA.
Page
2-5
2-15
3-5
3-6
4-7
4-10
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LIST OF FIGURES
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 PMio in Las
Vegas, NV. 2-7
Figure 2.1.5 PMio concentrations at different nearby sites centered around Fresno,
CA. 2-8
Figure 2.1.6 Spatial variation in 24-hour PMio chemical compositions from the
neighborhood to regional scale. 2-9
Figure 2.1.7 Normalized PMio concentrations at increasing distances from an
unpavedroad. 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 MS As in relation to counties, principal cities (+), and
terrain. 3-13
Figure 3.3.1 Populations in Jefferson and Shelby county census tracts. 3-15
VI
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LIST OF FIGURES (continued)
Page
Figure 3.3.2 Potential Monitoring Planning Area for the Birmingham MS A. 3-16
Figure 3.3.3 Census tract boundaries and past and present PM monitoring sites in
California's San Joaquin Valley. 3-17
Figure 3.3.4 Potential San Joaquin Valley MPAs: a) Stockton/Modesto, b)
Fresno/Visalia, and c) Bakersfield. 3-18
Figure 4.2.1 Hourly wind transport directions (from N) and distances (km).
1988-92 Birmingham airport winds. 4-3
Figure 4.2.2 Hourly wind transport directions (from N) and distances (km).
1988-92 Fresno airport winds. 4-4
Figure 5.2.1 Recommended distances and elevations of PM sampler inlets from
heavily traveled roadways. 5-3
Figure 5.5.1 Example of spatial homogeneity over a three-year period. 5-5
Figure 5.5.2 Example of temporal trends. 5-5
Figure 5.5.3 Example of spatial averaging. 5-6
Figure 5.5.4 Example of differences in chemical composition between two sites. 5-6
Figure 5.5.5 Example of using population density for monitor placement. 5-7
Vll
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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 \im (PMio) and 2.5 um (PM2.5) and are described as follows (U.S.
EPA, 1997):
• Twenty-four hour average PM2.5 not to exceed 65 (ig/m3 for a three-year average
of annual 98th percentiles at any population-oriented monitoring site in a
monitoring area.
• Three-year annual average PM2.s not to exceed 15 p.g/m3 concentrations from a
single community-oriented monitoring site or the spatial average of eligible
community-oriented monitoring sites in a monitoring area.
• Twenty-four hour average PMio not to exceed 150 |J.g/m3 for a three-year average
of annual 99th percentiles at any monitoring site in a monitoring area.
• Three-year average PM)0 not to exceed 50 (ig/m3 for three annual average
concentrations at any monitoring site in a monitoring area.
The PM2.s NAAQS are new. While the PM|0 NAAQS retain the same values as the
prior NAAQS (U.S. EPA, 1987), their form is new. Previously, the PM NAAQS applied to
the highest 24-hour or annual averages found within a monitoring planning area, and
monitoring networks were often designed to measure these highest values. These networks
did not necessarily represent the overall exposure of populations to excessive PM
concentrations. Some data from these networks were disregarded by epidemiologists as being
unrelated to health indicators such as hospital admissions and death. Air quality districts may
have been reluctant to locate source-oriented monitors that might assist in understanding
source impacts because such monitors might cause a large area to be designated in non-
attainment of NAAQS.
The new forms for these standards are intended to provide more robust measures for
the PM indicator. While PMio network design and siting criteria are unchanged, new PM2.s
monitoring networks to determine compliance or non-compliance are intended to best
represent the exposure of populations that might be affected by elevated PM2 5 concentrations.
As used in this document, the word compliance means attainment of a NAAQS. This involves
new concepts of spatial averaging and the operation of some monitoring sites for PM2.5
measurements that are not eligible for comparison to one or both of the PM2.5 NAAQS.
1-1
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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]0 network will likely
decrease as new PM2.5 sites are established. The PM2.s sites may or may not be collocated
with PMio monitoring locations. This guidance for network design and optimum site
exposure of PMio and PM2.5 monitors describes how particulate monitoring networks can
comply with these intentions.
1.1 Objectives of Guidance
The objectives of the guidance specified here are to:
• Define concepts and terms of network design.
• Present a methodology for defining planning areas and selecting and evaluating
monitoring sites in a network.
• Summarize the availability and usage of existing resources for network design.
• Demonstrate the methodology in practical applications.
This guidance builds upon the guidance specified by Koch and Rector (1987) for
monitoring associated with the previous PM NAAQS. It also considers recent advances in
sampling theory, the availability of different types of data over the Internet and on CD-ROM,
and the practical experience of different air quality management districts.
Network design guidance must be more specific than in the past with respect to types
of sites and what they represent. It should identify data available to make judgments on site
selection and define methods to use these data for those judgments. It should provide
methods to evaluate the extent to which these judgments were valid. This guidance intends to
provide this specificity.
1.2 Schedule and Approvals for Network Design and Implementation
The implementation of network design, operation and evaluation for the revised PM
NAAQS follows this schedule:
• July 18, 1997 Standards were promulgated.
• September 16, 1997:Standards became effective.
• October-December, 1997: Guidance is applied by state and local agencies in test
areas and procedures are refined. Network deployment is completed.
• January 1, 1998: Network design guidance is finalized and the regulated
requirement of PM2 5 monitoring commences.
1-2
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• July 1, 1998:Each state submits a PM monitoring network description to its EPA
Regional Administrator describing its network.
• September 16, 1998: Commence operation of at least one core PM2 5 SLAMS
site in each MSA with population greater than 500,000, one site in each PAMS
area, and two additional SLAMS sites per state. See footnote '.
• July 1, 1999, 2000, 2001, 2002, et&tate and local agencies submit annual
monitoring reports and network evaluations, based on data from previous calendar
year.
• September 16, 1999: Commence operation of other required SLAMS sites
(including all required core SLAMS, required regional background and regional
transport SLAMS, continuous PM monitors in areas with population greater than
1 million, and all additional required PM2.5 SLAMS). See footnote '.
• September 16, 2000: Commence operation of additional sites (e.g., sites
classified as SLAMS/SPM to complete the mature network). See footnote.1
1.3 Related Documents
Other documents related to PM monitoring networks are:
• The Federal Register for July 18, 1997, pages 38652-38760 and 38764-38854,
describe the proposed new PM standards, monitoring requirements, and
designation of reference and equivalent methods for PM2.5 (U.S. EPA, 1997).
1 Network deployment schedules as defined by 40 CFR part 58. Accelerated schedule may be dictated by
additional guidance and EPA policy.
1-3
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2.0 CONCEPTS OF NETWORK DESIGN
Several new concepts and definitions are embodied in the form of the revised air
quality standards. A brief overview of these concepts and definitions is given in this section.
2.1 Particle Properties
A wide variety of suspended particles are found in a typical atmosphere. Size, chemical
composition, concentration, and temporal variability all have the potential to affect public
health and perception of pollution. Several of these same properties allow suspended particles
to be attributed to their sources.
Friedlander (1970, 1971) proposes a size-composition probability density function
(PDF) to describe the number of suspended particles at given times and points in space with
specified chemical composition and particle size. While a useful theoretical concept, the exact
PDF can never be obtained in practice with current technology. Since all sizes and every
chemical component of particles cannot be measured everywhere at all times, the
measurement problem must be narrowed in scope to identify those properties that are
important for compliance.
Figure 2.1.1 shows the major features of the mass distribution of particle sizes found in
the atmosphere. The "nucleation" range, also termed "ultrafine particles", consists of particles
with diameters less than -0.08 um 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 urn. 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 |im 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.
2-1
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Nucleation
i—i—i i i 1111
Accumulation
i—i—i i 11 ii 1
0.01
0.1 1 10
Particle Aerodynamic Diameter (microns)
100
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 |im, the PMio fraction ranges from 0 to
10 |j.m, and the PM2.s size fraction ranges from 0 to 2.5 (im in aerodynamic diameter. No
sampling device operates as a step function, passing 100% of all particles below a certain size
and excluding 100% of the particles larger than that size. When sampled, each of these size
ranges contains a certain abundance of particles above the upper size designation of each
range.
Figure 2.1.2 shows typical residence times in the atmosphere for particle sizes within
each size range, based on gravitational settling in mixed and stirred chambers (Hinds, 1982).
Particles in the fine particle (PM2.s) 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 um. In this regard, fine particles
act more like gases than like coarse particles.
2-2
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Residence Time (days)
7 30
90
100%
0%
ID"2
1C'1
1 10 10*
Residence Time (hours)
10J
Figure 2.1.2. Residence times for homogeneously distributed particles of different
aerodynamic diameters in a 100 m deep mixed layer. Gravitational settling is
assumed for both still and stirred chamber models (Hinds, 1982).
Figure 2.1.1 shows the accumulation range to consist of at least two sub-modes, which
is contrary to many other presentations that show only a single peak in this region. Recent
measurements of chemically specific size distributions show these sub-modes in several
different urban areas. John et al. (1990) interpreted the peak centered at -0.2 |im as a
"condensation" mode containing gas-phase reaction products. John et al. (1990) interpreted
the -0.7 (j,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 Jim. A small shift in the
50% cut-point of a PMio sampler has a large influence on the mass collected because the coarse
mode usually peaks near 10 \im. On the other hand, a similar shift in cut-point near 2.5 Jim has
a small effect on the mass collected owing to the low quantities of particles in the 1 to 3 |im
size range.
2-3
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100% T
80%
60% +
40%-
20%-
0%
52.3%
(<10Mm)
95.8%
(<10Mm)
827%
,95.8%
|(<10pm)
93.1%
81.6%
924%
96.2%
(<10Mm)
923%
(<25pn«
91.8%
99.2%
(<10pm)
97.4%
(<2.5Mrr
87.4%
34.9%
(<10Mm)
58%
(<25Mm)
4.6%
(<1prn)
Road and Soil Agricultural
Dust Burning
Residential
Wood
Combustion
Diesel Truck
Exhaust
Crude Oil
Combustion
Construction
Dust
-10pm E3>10pm
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 PMio 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 PMio and PM25 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 PMio is often composed of geological material. Geological
material often constitutes less than ~10% of the PM2.5 mass concentrations, however, as most
of it is found in the coarse particle size fraction. As shown in Figure 2.1.3 (from Ahuja et al.,
1989; Houck et al., 1989, 1990), most particles emitted by common sources, with the
exception of fugitive dust sources, are in the PM2.s fraction.
2-4
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Paved Road Dust
Unpaved Road Dust
Construction
Agricultural Soil
Natural Soil
Lake Bed
Motor Vehicle
Vegetative Burning
Residual Oil Combustion
Incinerator
Coal-Fired Boiler
Oil-Fired Power Plant
Smelter Fine
Antimony Roaster
Marine
Dominant
Coarse
Coarse
Coarse
Coarse
Coarse
Coarse
Fine
Fine
Fine
Fine
Fine
Fine
Fine
Fine
Fine and Coarse
<0.1%
Cr. Sr, Pb. Zr
NOs". NH/, P. Zn, Sr,
Ba
Cr, Mn, Zn, Sr. Ba
NOs. NH/, Cr. Zn. Sr
Cr. Mn. Sr. Zn, Ba
Mn, Sr, Ba
Cr, Ni. Y
Ca, Mn, Fe, Zn. Br, Rb,
Pb
K*, OC. Cl, Ti, Cr, Co.
Ga, Se
V. Mn, Cu, Ag. Sn
Cl. Cr, Mn, Ga. As. Se,
Br. Rb, Zr
V. Ni. Se, As, Br, Ba
V. Mn, Sb, Cr, Ti
V, Cl. Ni. Mn
Ti, V, Ni, Sr. Zr. Pd. Ag,
Sn, Sb. Pb
0.1 to 1 %
SO/, Na', K*. P. S. Cl.
Mn. Zn, Ba, Ti
SO/. Na*. K*, P, S, Cl,
Mn, Ba.Ti
SO/, K*, S, Ti
SO/, Na*, K*. S, Cl. Mn,
Ba.Ti
Cf. Na*. EC, P, S. Cl. Ti
K*,Ti
NH/, Si. Cl. Al, Si. P,
Ca. Mn. Fe. Zn, Br. Pb
NOa , SO/, NH/, Ma*. S
NH/. Na', Zn, Fe. Si
K*. Al. Ti. Zn. Hg
NH/, P. K. Ti, V, Ni, Zn,
Sr, Ba, Pb
Al, Si. P. K. Zn
Cd. Zn, Mg, Na. Ca. K.
Se
SO/, Sb, Pb
Al, Si, K. Ca. Fe. Cu.
Zn, Ba, La
1to10%
Elemental Carbon (EC).
Al, K, Ca, Fe
OC, Al, K, Ca. Fe
OC, Al, K. Ca. Fe
OC. Al. K. Ca, Fe
OC, Al, Mg, K, Ca, Fe
SO/, Na*, OC, Al, S,
Cl. K. Ca, Fe
Cl', NOa", SO/. NH/. S
Cl". K*. Cl, K
V. OC, EC. Ni
NO}', Na*. EC. Si. S,
Ca, Fe, Br, La, Pb
SO/. OC. EC, Al, S.
Ca. Fe
NH/, OC, EC. Na. Ca.
Pb
Fe, Cu, As, Pb
S
NCb", SO/. OC. EC
>10%
Organic Carbon(OC). Si
Si
Si
Si
Si
Si
OC. EC
OC.EC
S, SO/
SO/. NH/, OC, Cl
Si
S. SO/
S
None reported
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.
2-5
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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 Jim, as shown
in Figure 2.1.1. Several of these particles, notably those containing ammonium nitrate, are
volatile and transfer mass between the gas and particle phase to maintain a chemical
equilibrium. This volatility has implications for ambient concentration measurements as well
as for gas and particle concentrations in the atmosphere.
Ambient concentrations of secondary aerosols are not necessarily proportional to
quantities of emissions since the rate at which they form may be limited by factors other than
the concentration of the precursor gases. Secondary particulate ammonium nitrate
concentrations depend on gaseous ammonia and nitric acid concentrations as well as
temperature and relative humidity. A nearby source of ammonia may cause a localized
increase in PM2.5 concentrations by shifting the equilibrium from the gas to the particulate
ammonium nitrate phase (Watson et al., 1994). Ammonium sulfate may form rapidly from
sulfur dioxide and ammonia gases in the presence of clouds and fogs, or slowly in dry air.
Because fine particle deposition velocities are slower than those of the gaseous precursors,
PM2 5 may travel much farther than the precursors, and secondary particles precursors are
often found far from their emissions sources and may extend over scales exceeding 1,000 km.
Compliance measurements are taken at fixed monitoring sites for specified time
intervals, usually 24 hours. While fixed site monitoring is an effective surrogate for actual
exposure, the air that people breathe depends on where they are, the most common locations
being the home, the workplace, the automobile, and the outdoors. Most outdoor human
exposure occurs during the daytime, so it is important to understand how particle
concentrations differ between day and night.
Figure 2.1.4 shows a clear diurnal cycle of hourly PM 2.5 concentrations measured with
a TEOM during the 26-day IMS95 Winter Study (Chow and Egami, 1997). This plot shows
a distinct diurnal pattern for the 50th and 80th percentile concentrations which is consistent
with emissions estimates and meteorological patterns during the winter in the southern San
Joaquin Valley. Because much of the PM 2.5 is directly or indirectly related to emissions from
motor vehicle exhaust, peaks of PM 2.5 concentrations during the morning and evening rush
hours are expected at urban sites. The evening peak is suspected to be the accumulation of
emissions from motor vehicle exhaust superimposed on domestic cooking and residential
wood combustion contributions. Since transport and mixing are lowest during the cold
evening hours, pollutant concentrations can build up rapidly after sunset and frequently carry
over to the next morning.
Meyer et al. (1992) show a similar diurnal pattern during wintertime in a mountainous
California community where wood is burned, with the evening peak remaining high well past
2-6
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6
8
-20%tile
10 12 14 16 18 20 22 24
Hour
50%tile -*-80%tile
Figure 2.1.4. Hourly variations in the 20th, 50th, and 80th percentiles of PM2.s in
Bakersfield, CA (Chow and Egami, 1997).
midnight. In some communities where fugitive dust is a major emitter, peak PMio 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 PMio mass in a saturation monitoring network in California's San Joaquin
Valley (Chow and Egami, 1997). During this study, most of the PMio mass was in the PM2.5
fraction. All scales of representation show the highly variable nature of fine particulate mass
averaged over 24 hours. The variations are most noticeable in the urban areas where the
variability was attributable to residential wood smoke and holiday driving patterns. Figure
2.1.6 shows the difference in PM|0 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.
2-7
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K)
00
i; f — •' - - .?
0 250 500 750 1000 Meters Key
Mass (M9/rn3)
Middle-scale
Average 24-h PM10 mass over
IMS 95 Winter Study
A / Local. Secondary. State Hwy
/>/Major highways
50 100 Kilometers
'
Urban-scale
mm,
t^ffr
^-.^tetm+r
0 4 8 12 16 Kilometers
Neighborhood-scale
0 40 80 120 160 Kilometers
tssssas
Regional-scale
Figure 2.1.5. PMio concentrations at different nearby sites centered around Fresno, CA.
-------
a) Neighborhood
Scale
50m to 4 km
I I ""•-»._ !'(',' i i i ,
T~
..I—
0 1000 2000 3000 4000 5000 Meters
b) Urban
Scale
4 to 100 km
County
0 25 50 75 100 Kilometers
c) Regional
Scale
100 to 1000 km
12-27-95 24-h PM10 Mass (pg/m3)
----- 1 Crustal
| Organic Material
| Trace Species
| Elemental Carbon
| Surfate Ion
| Ammonium Ion
_ Nitrate Ion
\_~\ Unidentified
Roads
/ \/ Local, Secondary, State Hwy
Interstate
100 200 300 400 Kilometers
Figure 2.1.6. Spatial variation in 24-hour PM)0 chemical compositions from the
neighborhood to regional scale.
2-9
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10
15 20 25
Distance from source (m).
30
35
40
45
Figure 2.1.7. Normalized PMio concentrations at increasing distances from an unpaved road
(Watson et al., 1996). Samples were taken at 2 m above ground level.
PM2.5 concentrations are often more homogeneously distributed over space than are
the contributions from coarse-mode, geological sources. There are exceptions, however, as
shown by Chow et al. (1989) in comparing high wood smoke contributions between a
residential and an urban sampling site separated by less than 10 km. The stagnant air
conditions prevailing during high wintertime episodes caused the wood smoke to contribute
nearly 50% of PMio at the residential site, but to contribute less than 10% of PM|0 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 PMio 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
2-10
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Riverside, CA (Pellizari et al., 1993), particle loadings in outdoor air accounted for more than
60% of the indoor PMa.s. 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 PMj.s, are more likely to penetrate indoors than are the coarse
particles, which are more likely to deposit within the cracks < and seams where air penetrates.
Coarse particles also deposit to surfaces more rapidly due to gravitational settling in the stilled
air of most indoor environments.
Most of the evidence relating ambient measurements of suspended particles taken in
compliance networks to personal exposures shows that: 1) ambient concentrations, especially
those for PM2.s 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 ug/m3,
and maximum deviations of no more than 3 ug/m3, at concentrations near the annual PM2.5
standard of 15 ug/m3.
2.2.2 Receptor Site Zone of Representation
PM!0 and PMs 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.
2-11
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• Collocated Scale (1 to 10 irijtollocated monitors are intended to measure the
same air and involve separations of 1 to 5 m between samplers. Collocated
measurements should not differ by more than the operational precision of the
monitoring method. Monitors are operated on collocated scales to evaluate the
equivalence of different measurement methods and procedures and to quantify the
measurement accuracy and precision of the same measurement methods and
procedures. The distance between collocated samplers should be large enough to
preclude the air sampled by any of the devices from being affected by any of the
other devices, but small enough so that all devices obtain air containing the same
pollutant concentrations.
• Microscale (10 to 100 m)Microscale monitors show significant differences
between PM2.s 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 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 kriljfeighborhood-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 kmprban-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.
2-12
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• Regional-Scale Background (100 to 1,000 kmjiegional-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.
2-13
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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
PM25 for sites located away from populated areas and other significant emission sources.
Transport sites are intended to measure fine particle contributions from upwind source areas,
or mixtures of source areas, that move into a planning area.
Most planning areas contain at least one substantial metropolitan area. Several of
these also include industrial sources, either concentrated in one or a few districts or dispersed
throughout the planning area. Air quality planning areas also contain less developed areas that
may be distant from the densely populated centers and industrial emitters. These may include
agricultural areas, dormant lands, large parks, wildlife and nature preserves, large military
bases, etc.
Transport sites should be located upwind of planning area boundaries, outside of the
urban-scale zone of influence. For the most part, transport sites are between planning areas,
or between districts containing large emitters (e.g., industrial complexes, isolated point
sources) and a planning area. Measurements from transport sites represent transport into the
planning area only during periods when the wind is from the direction of the external source
area toward the planning area. During other periods, the transport site may also serve the
purposes of a background site, or as a transport site for another planning area. For this
reason, transport site locations are selected to achieve multiple purposes. Meteorological data
needed to evaluate which purposes are being served should be available along with the PM2.s
measurements.
Background monitors are intended to measure PM2.5 concentrations that are not
dependent on upwind sources, although the particles they quantify will be a mixture of natural
and manmade source material. These stations should be distant from identified emitters, and
may be at higher elevations than the urban-scale community exposure monitors. Current
IMPROVE (Interagency Monitoring of Protected Visual Environments) PMa.s monitoring in
National Parks and Wilderness Areas (Eldred et al., 1990) provides the best examples of
background monitoring sites, but there is a dearth of these sites in the non-western states.
Table 2.2.1 lists the locations of IMPROVE sites and their current measurements.
Properly sited background stations should measure PM2.s 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
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Acadia NP
Badlands NP
Big Bend NP
Bryce Canyon NP
Bndger Wilderness
Dape Remain NWR
Craters of the Moon NM m
Denali NP
3eath Valley NP
Dome Land
Dolly Sods Wilderness
E.D. Forsythe NWR
Everglades NP
Glacier NP
Grand Canyon NP
(Hopi Point Fire Tower)
Great Sand Dunes NM
Great Smokey Mountains NP
Haleakala NP
Jarbidge Wilderness
Lye Brook Wilderness
Mammoth Cave NP
Mesa Verde NP
Moosehom NWR
Mount Rainier NP
National Capitol Central, D.C
Okefenoke NWR
Petrified Forest NP
Pinnacles NM
Point Reyes NP
Redwood National Seashore
Rocky Mountain NP
Sipsey Wilderness
Tonto NM
Upper Buffalo Wilderness
Wemmuche Wilderness Area
Yellowstone NP. Water Tank
Yosemrte NP. Turtleback Dome
Latitude
44.3742
43.7469
43.8719
29.3053
29.3439
37.6000
37.4667
42.9750
42.9281
38.45S3
0.0000
28.7500
42.8958
43.4606
36.5086
35.7000
39.1047
39.4681
25.3883
48.5103
48.5581
39.0053
36.0392
36.0719
35.9964
36.0778
37.7083
35.6314
0.0000
20.8039
41.9583
41.8925
40.5369
43.1444
37.2178
37.1983
O.OOOC
46.7614
38.895C
30.7402
35.0772
34.8983
36 485C
38.1231
41.5611
40.2772
40.3606
34.1847
32.1744
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68.2622
102.2308
103.1772
103.2067
112.1667
112.2278
109.7583
109.7875
109.8217
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82.5667
122.1333
113.5622
116.8478
118.2000
79.4258
74.4536
0.0000
113.9956
113.9375
114.2158
111.8300
112.1550
111.9917
112.1289
105.5172
83.9422
104.8097
156.2850
115.0847
115.4250
121.5725
73.1289
86.0736
108.4903
0.0000
122.1217
77.0367
82.1286
109.7697
109.7958
121.1556
122.9082
124.082J
105.5450
105.5806
116.9015
110.7364
118.8286
78.4361
87.3386
111.1011
93.2056
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X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
A
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
"
X
X
X
X
X
X
X
X
X
X
X
X
X
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X
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X
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X
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X
X
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X
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X
X
X
X
X
X
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 scattenng coefficient (nephelometer) RH relative humidity sensor
A IMPROVE sampler module A S02 sulfur dioxide sampler
B IMPROVE sampler module B 35 35 mm camera slides
X
X
~x~
X
X
X
X
X
X
X
X
x
X
X
X
X
Table 2.2.1. IMPROVE measurement sites.
2-15
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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 PMio over a wide region, the majority, of emissions from a
specific road influence concentrations over a few tens of meters from the emissions point (see
Figure 2.1.7).
The actual size of a zone of influence varies with meteorology, being larger downwind
than upwind, and the nature of the source (point, elevated, area, line, etc.). Zones of influence
are, therefore, expressed as orders of magnitude rather than as exact distances. The concept
is useful for locating community exposure sites that are intended to represent concentrations
for sources with large rather than small zones of influence. Actual zones of influence must be
determined empirically, by spatially dense monitoring networks, or theoretically by applying
air quality and meteorological models.
2.2.6 PM2.s 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 PMs are methods
that have been designated as such under CFR 40 Chapter 1 Part 53, having met
design and performance characteristics described in Part 50, Appendix L; Part 53,
Subpart E; and Part 58, Appendix A. Reference method instruments acquire
deposits over 24-hour periods on Teflon-membrane filters from air drawn at a
controlled flow rate through a tested PM2 5 inlet. The inlet and size separation
components are specified by design, with drawings and manufacturing tolerances
published in the Code of Federal Regulations. Most of the other measurement
components and procedures are specified by performance characteristics, with
specific test methods to assess that performance.
• Class I Equivalent Methods: Class I equivalent method instruments maintain the
same measurement principles as reference method instruments, but with minor
design changes. Class I instruments are intended to provide for sequential
sampling without operator intervention at measurement sites that sample every
2-16
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day. Testing of design and performance characteristics for Class I instruments is
given in Part 53, Subpart E.
• Class II Equivalent MethodsClass 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 n equivalent instrument than for reference or Class I
equivalent instruments. Testing of design and performance characteristics for Class
n methods is given in Part 53, Subpart F.
• Class HI Equivalent MethodsClass in 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 n
criteria, or filter or non-filter based continuous or semi-continuous samplers. Test
procedures and performance requirements for Class in candidate method
instruments will be determined on a case-by-case basis. The testing for these
instruments will be the most stringent, because equivalency to reference methods
must be demonstrated over a wide range of particle size distributions and aerosol
compositions. Other methods include all non-FRM or non-equivalent
measurement methods capable of characterizing fine particles that may not be or
have not yet been classified as an equivalent method. Existing manual and
continuous analyzers are in this category and potentially include the dichotomous
sampler, IMPROVE samplers, nephelometers, beta attenuation monitors, and
Tapered Element Oscillating Microbalances (TEOMs). Such instruments are not
precluded from becoming equivalent on a site-specific, regional or national basis.
2.3 Definitions
Several terms and abbreviations are used throughout this guidance, and in the
specification of the method for determining compliance with the revised standards. These
terms are defined for: 1) theoretical concepts; 2) monitoring boundaries; 3) monitoring
networks; and 4) site types.
2.3.1 Theoretical Concepts
As will be shown in Section 2.4, systematic sampling theory has seldom been applied
to the design of air quality measurement networks. Since monitoring resources are always
finite, trade-offs must be made off between numbers of sites, frequencies of samples, sample
durations, and the quantities measured. As more experience is gained in the design of PM2 5
monitoring networks, the theoretical and empirical basis for network design will become
better established.
• Cost Per Error (CPE) (Borgma* a/., 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.
2-17
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• Error Per Cost (EPC) (Borgmzft a/., 1998) 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 (MS A) MS As 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 (PMSAjRMSAs are single counties or
groups of counties that are the component metropolitan portions of a
mega-metropolitan area. PMSAs are similar to MSAs with the additional
characteristic of having a degree of integration with surrounding metropolitan
areas. A group of PMSAs having significant interaction with each other are
termed a Consolidated Metropolitan Statistical Area (CMSA).
• Consolidated Metropolitan Statistical Area (CMSA): A Consolidated
Metropolitan Statistical Area (CMSA) is a group of metropolitan areas (PMSAs)
that have significant economic and social integration.
• New England County Metropolitan Statistical Area (NECMSA]he 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 MS As/CMS As 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
2-18
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the Connecticut portion of the New York-Northern New Jersey-Long Island,
NY-NJ-CT-PA CMSA.
• Monitoring Planning Area (MPA): MPAs are defined by the state
implementation plan as the basic planning unit for PM2.5 monitoring. A MPA is a
contiguous geographic area with established, well-defined boundaries. MPAs may
cross state lines and can be further subdivided into Community Monitoring Zones.
A MPA does not necessarily correspond to the boundaries within which pollution
control strategies will be applied. In fact, it" is expected that emissions control
regions will be much larger than the MPAs, owing to the superposition of
regional-, urban-, and neighborhood-scale contributions to PM2.5. MPAs may
include aggregates of: 1) counties; 2) zip code regions; 3) census blocks and
tracts; or 4) established air quality management districts. Counties are often much
larger than the most densely populated areas they contain, and some large
metropolitan areas may extend over several counties. Census blocks are very small
and may be unwieldy to manipulate in some large areas. Zip code and census tract
boundaries may be the most manageable units for many areas. These boundaries
vary substantially in geography from one region to another. MPAs normally will
contain at least 200,000 people, though portions of a state not associated with
MSAs can be considered as a single MPA. Optional MPAs may be designated for
other areas of a state. MPAs in MSAs are completely covered by one or more
Community Monitoring Zones.
• Community Monitoring Zone (CMZ): When spatial averaging is utilized for
making comparisons to the annual PM2.5 NAAQS, Community Monitoring Zones
must be defined in the monitoring network description. Otherwise, they may be
used as a more informal manner, as a means to describe the communities
surrounding one or more core monitoring sites. CMZs have dimensions of 4 to 50
km with boundaries defined by existing political demarcations (e.g., aggregates of
zip codes, census tracts) with population attributes. They could be smaller in
densely populated areas with large pollutant gradients. Each CMZ would ideally
equal the collective zone of representation of one or more community-oriented
monitors within that zone. The CMZ, applicable only to PM2.s, is intended to
represent the spatial uniformity of PM2.5 concentrations. In practice, more than
one monitor may be needed within each CMZ to evaluate the spatial uniformity of
PM2 5 concentrations and to accurately calculate the spatial average for comparison
with the annual PM2 5 NAAQS. When spatial averaging is used, each MPA would
be completely covered by one or more contiguous CMZs.
2.3.3 Monitoring Networks
PM2 5 monitoring networks may be new networks or part of existing networks.
Additional sites may be added to existing networks according to this guidance.
• State and Local Air Monitoring Stations (SLAMS^MS are designed and
operated by local air pollution control districts to determine: 1) the highest
2-19
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concentrations expected to occur in each MPA; 2) representative concentrations in
areas of high population density; 3) the impact on ambient pollution levels of
significant sources or source categories; 4) general background concentration
levels; 5) the extent of regional pollutant transport among populated areas, and
6) welfare-related impacts in rural and remote areas (i.e., visibility impairment and
effects on vegetation). Only population-oriented SLAMS acquire data for
determining compliance with PM 2.5 standards, and community-oriented (core)
SLAMS acquire data for compliance with the annual PM2.s 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 PMio NAMS
should generally be good candidates for PM2.5 monitoring.
• Photochemical Assessment Monitoring Stations (PAMSJJAMS track trends
in ozone precursor emissions, corroborate emission inventories, and support
photochemical modeling. Ozone non-attainment areas classified as serious, severe,
or extreme have PAMS sites that include enhanced monitoring of ozone, ozone
precursors, and surface and upper-air meteorology. PAMS site type 2 represent
the area of maximum O3 precursor concentration and should also represent good
locations for core PM2 5 sites. Though PAMS site type 1 and 4 are intended to be
used for regional-scale ozone assessment, their siting and measurements also apply
to secondary nitrate and organic aerosol formation and they should be considered
as potential PM25 monitoring sites, especially for transport and background
monitoring. MPAs with existing PAMS are to install a PM25 core site at a
minimum of one PAMS location.
• Interagency Monitoring of Protected Visual Environments (IMPROVED
noted above, the IMPROVE network provides long-term measurements of PM2.s
and other visibility-related observables in National Parks and Wildernesses
throughout the U.S. IMPROVE sites, and the data acquired at those sites, may
qualify as background and/or transport sites for PM2 5 networks.
2.3.4 Site Types
Several types of sampling sites, not all of which are designated for determining
compliance with NAAQS, will be part of the PM2 5 measurement networks.
• Community-Oriented (Core) Sites:Community-oriented sites are located where
people live, work, and play rather than at the expected maximum impact point for
specific source emissions. These sites are not located within the microscale or
middle-scale zone of influence of a specific, nearby particle emitter. Community-
oriented sites may be located in industrial areas as well as and in residential,
2-20
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commercial, recreational, and other areas where a substantial number of people
may spend a significant fraction of their day.
A subset of the core sites are intended to acquire PM2.5 concentrations every day.
These include core SLAMS sites and sites collocated with PAMS sites. Two or
more such core sites are to be operated in MSAs with population greater than
500,000, with at least one additional core site in each PAMS area.
Core sites are used to determine NAAQS compliance for both annual and 24-hour
PM2.5 standards. Because core sites are the only sites eligible for comparison to
both the annual and 24-hour PM2.5 NAAQS, they are the most important sites in
the new PM2.5 network. PM2.5 concentrations may be spatially averaged among
these sites within a CMZ when the annual average PM2.s 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 PM2s 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) PM2j standard, but not for the annual standard.
Because a daily compliance site does not necessarily represent community-oriented
monitoring, it may be located near an emitter with a microscale or middle-scale
zone of influence.
The PM monitoring regulations state that any population-oriented site is eligible
for comparison to the 24-hour PM2.5 standard. If the monitoring stie is also
representative of community-wide air quality, it is eligible for comparison to the
annual PM2 5 NAAQS. With a few anticipated exceptions, almost all sites in the
new network will be population-oriented. A site may be population-oriented and
at the same time be source oriented or reflective of maximum concentration. The
same is true for the existing PMio network.
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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)8PMs may or may not be used to determine
compliance. Their purpose is to understand the nature and causes of excessive
concentrations measured at compliance monitoring sites. SPMs do not necessarily
use FRMs or FEM methods, and they may be operated over short periods of time
at different locations. SPMs may be discontinued within their first two years of
operation without prejudice when their purpose has been achieved. Typical SPMs
might include: 1) portable saturation monitors operated at many locations around
core sites to determine zones of representation, zones of influence, and spatial
uniformity; 2) sequential samplers with Teflon and quartz filters or absorbing
substrates to determine diurnal distributions of PM chemical components and
precursor gases; and 3) short-time-resolution continuous monitors to determine
diurnal mass concentration changes in response to changes in emission rates and
meteorology. When SPMs use FRM or FEM samplers and satisfy other
requirements of section 58.14a, then they may be used to judge compliance.
However, non-attainment designations will not be based upon the SPM data for
the first two years of their operation.
• Transport Sites: Transport sites are intended assess the effects of emissions
within one MPA or isolated emission sources on other MPAs. To do this, they are
typically located between MPAs, or between non-urban source areas and MPAs.
Meteorological measurements will usually be associated with transport sites.
• NAMS Sites: Subsets of core and transport sites will be selected for long-term
monitoring and will be designated as PM2.5 NAMS for assessing trends and for
performing future epidemiological studies.
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• Background Sites: Background sites are intended to represent regional-scale
PM2.s concentrations that may be a combination of contributions from several
MPAs and non-urban source areas, as well as natural emissions. These are usually
located in pristine areas, such as National Parks and Wilderness areas, and possibly
at elevations higher than MPAs, but still within the typical mixed layer of the
atmosphere.
2.4 Network Design Philosophies
The design of environmental sampling networks has been studied in hydrology
(Andricevic, 1990; Kassim and Kottegoda, 1991; Woldt and Bogardi, 1992; Meyer et al,
1994), meteorology (Gandin, 1970), and the geological sciences (Camisani-Calzolari, 1984;
de Marsily et al., 1984;Russo, 1984). Only a few of these concepts have been adapted to air
quality networks. Some of the earliest work done in network design focused on
meteorological observations (Gandin, 1970).
2.4.1 Network Design Objectives
Networks are designed to attain specific objectives. Objectives of the SLAMS PM2.5
monitoring network are (U.S. EPA, 1997b):
• To determine representative concentrations in areas of high population density.
• To determine the impact on ambient pollution levels of significant sources or
source categories.
• To determine general background concentration levels.
• To determine the extent of regional pollutant transport among populated areas;
and in support of secondary standards.
• To determine the highest concentrations expected to occur in the area covered by
the network.
• To determine the welfare-related impacts in more rural and remote areas such as
visibility impairment and effects on vegetation.
Munn (1981) defines two basic methods of network design: 1) the statistical method,
and 2) the modeling method. The statistical method assumes that existing data is available to
extract meaningful statistical information for network design.
The statistical approach is based on the lognormal distribution followed by most air
quality data (Larsen, 1969; Noll and Miller, 1977). Statistical methods take advantage of the
fact that most air quality measurements are correlated either in time at the same location or in
space with other monitors in a network. Networks are optimized by examining time series
correlations from long measurement records or spatial correlations among measurements from
2-23
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many nearby monitors (Munn, 1975; Elsom, 1978; Handscombe and Elsom, 1982). Munn
(1981) identifies four types of correlation analysis: 1) time correlation (autocorrelation) at
one site; 2) cross-correlation of several pollutant concentrations at one site; 3) spatial
correlations among simultaneous measurements at different sites; and 4) spatial correlations
among different sites with time lags.
2.4.2 Random Sampling
Random sampling locates sites by chance, without taking into consideration the
sources of pollutants (Nesbitt and Carter, 1996). Random placement is accomplished by
specifying boundaries of a rectangular domain, generating x and y coordinates from a uniform-
distribution random number generator truncated at the domain boundaries, and placing
samplers as close to these coordinates as practical.
The advantages of random sampling designs are: 1) measurement bias is minimized;
2) implementation simplicity, with no knowledge assumed about the spatial and temporal
distribution of concentrations; and 3) sampling locations are objectively chosen. The
disadvantages are mat: 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 ug/m3 with a variance, a2, of
6.5 (ug/m3)2 the estimated number of samples is found to be
1.96<7
= 1
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
2-24
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number of samplers depending on the size of the MPA, and most of these samplers supply
redundant information where PM2 5 concentrations are spatially uniform.
Systematic sampling costs may be prohibitively high, even for small areas, except for
short periods during which spatial uniformity is being evaluated. The positive characteristic of
systematic sampling is that the network completely covers the planning area.
2.4.4 Judgmental Sampling
Judgmental sampling (Nesbitt and Carter, 1996) uses knowledge of source emissions
and sensitive receptor locations, coupled with mechanisms for pollutant transport, to locate
measurement sites. Noll and Miller (1977) call this the "source orientation method" and deem
it most appropriate for monitoring point sources in uneven terrain. Air pollution models can
be used to assist in this judgment, but this requires exceptional accuracy of the model
formulation and the model input data. Few areas in the U.S. have good estimates of particle
and precursor gas emissions, especially from mobile and area sources. Complex terrain and
meteorology, as well as simulating secondary aerosol formation, also present challenges to
currently available models for suspended particles.
Judgmental sampler locations may be determined by data from an existing monitoring
network or by identifying the locations of pollutant sources and inferring pollutant transport
from data analysis of emissions and wind measurements. Short-term experiments involving
spatially dense measurements and modeling may assist in making or verifying judgments.
Monitoring networks for criteria pollutants always use judgmental sampling strategies
that consider where source emissions are in relation to populations and which way the wind
blows.
2.4.5 Heterogeneous Siting Strategies
Nesbitt and Carter (1996) combine judgmental and systematic sampling by applying
the following steps: 1) identify potential sources of contamination or "hot spots" using
existing measurements or models; 2) place a grid system over these areas; 3) perform
sampling at these grid points; 4) define a systematic grid at points which yield positive
contamination; 5) use the systematic grid to assess the remainder of the study area.
Figure 2.4.1 shows how a judgmental strategy compares with a combined judgmental
and systematic strategy. The concentration isopleths can be interpolated from spatially dense
measurements or produced by an air quality model. The judgmental strategy, by itself, missed
areas of significant concentrations, while the combined judgmental and systematic strategy
covered the areas of significant concentration that had not previously been monitored.
Another hybrid method for locating potential paniculate matter samplers is based on
geostatistical sampling (Joumel, 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
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Judgmental only
Judgmental and systematic
JN
-^tm-:
:m^m
-JUT
/£P";*3P\SW SN SN
JN
*-"-J5P;\SN
'$fr-"'sN
/'i^v^^NV^^fe-s'^
\;::$$&^^ASiP I $?\
Pollutant concentration contour
N= negative sample
P= positive sample
J = judgmental sampling site
S = systematic sampling site
Figure 2.4.1. Examples of judgmental and hybrid sampling strategies.
structure to produce an estimator with the smallest possible mean square error and results in
reduced sample size compared to other methods.
Most of this guidance is based on judgmental network design, though it is expected
that networks will involve more of the hybrid approach as they are evaluated as future PM2.s
measurements and improved aerosol modeling techniques are developed.
2.4.6 Other Siting Strategies
Other statistical tools to design air quality networks include: 1) the coefficient of
geographic variation (Stalker and Dickerson, 1962; Stalker et al, 1962); 2) structure
functions (Goldstein et al., 1974; Goldstein and Landovitz, 1977); 3) cluster analysis
(Sabaton, 1976); 4) principal component analysis (Peterson, 1970; Sabaton, 1976); 5) the
variational principle (Wilkins, 1971); and 6) linear programming (Darby et al., 1974;
Hougland, 1977).
Modeling relies on a numerical or analytical model to estimate particulate
concentrations in space and time. Because of its nature and sources, PM2 5 is difficult to
model over neighborhood- and urban-scales. As noted above, modeling requires a detailed
emissions inventory over the entire domain. Efforts are being made to archive emissions data
in geographical information systems (GIS).
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Numerical source-oriented models are designed to simulate atmospheric diffusion or
dispersion and estimate concentrations at defined receptors. Numerical source models can be
grouped as kinematic, first-order closure, or second-order closure models (Bowne and
Lundergan, 1983). Kinematic models are the simplest both mathematically and conceptually.
These models simplify the non-linear equations of turbulent motion, thereby permitting a
closed analytical approximation to describe pollutant concentration (Green et al., 1980).
First-order closure models are based on the assumption of an isotropic pollutant concentration
field. Consequently, turbulent eddy fluxes are estimated as being proportional to the local
spatial gradient of the transport quantities. The Eulerian grid models, Lagrangian particle
models, and trajectory puff/plume models are included in this category. Second-order closure
models involve a series of algorithm transformations of the equations of state, mass continuity,
momentum, and energy by using the Boussinesque approximation and Reynold's
decomposition theory (Holton, 1992; Stull, 1988).
For estimating PM2.s 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).
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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^.s 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.
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3.0 DEFINING STATE PLANNING AREAS
This section specifies the steps to define the boundaries of Monitoring Planning Areas
(MPAs) for determining compliance with PMio and PMj.s standards. This procedure requires
the spatial examination of population statistics, topography, existing PM networks, past
measurements, emissions densities, pollution transport patterns, and existing planning areas.
The procedure gives preference to maintaining existing planning areas as MPAs for PM2.5
and for adapting existing sites to PM2.5 compliance monitoring. It also provides an objective
means for identifying PMio measurement locations that can be discontinued as PMio
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 Areasflot 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 BasinsCompare 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 Sitefiot 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
3-1
-------
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 AreasPlot boundaries of
existing planning areas, such as air quality management districts, urban master
plan boundaries, and/or transportation planning regions. Make initial MPA
boundaries correspond to existing planning boundaries. Add or subtract
populated entities to define the MPA as closely as possible to the existing
boundary. Where major adjustments are needed to accommodate existing
planning boundaries, define initial CMZs or general areas for locating core sites
within those boundaries according to the procedure in Section 4.
3.1 Identify Political Boundaries of Populated Areas
Appendix B lists Metropolitan Statistical Areas (MSA) and Primary Metropolitan
Statistical Areas (PMSA) in the United States. Figures 3.1.1 and 3.1.2 show these statistical
areas for the continental U.S. with shading for their populations in 1990 and 1995,
respectively. The 1990 census values are to be used to determine population cut-offs, and in
most cases these do not differ by more than ±10% from the 1995 estimates. Tables 3.1.1 and
3.1.2 are extracts from Appendix B for the states of Alabama and California, respectively.
The MSAs and PMSAs are named after the most populated cities or counties and are
intended to include the economic influence of a population center. Their boundaries may
correspond to county or municipal borders.
In Alabama, the MSAs range from ~1,500 km2 to 8,000 km2, with population
densities of-40 to 100 people/km2. This is typical of many eastern states, where the counties
are relatively small compared to those of the west. In California, on the other hand, the
MSAs range from -1,000 km2 to >20,000 km2, with 1990 population densities from 25
people/km to >1,100 people/km2. The most extreme cases in Appendix B are: 1) the Las
Vegas MSA that covers more than 100,000 km2 and includes Nye, Clark, and Mohave
Counties, among the largest counties in the U.S.; and 2) the Jersey City PMSA that includes
only 120 km2 of Hudson County with one of the highest U.S. population densities (>4,500
people/km2). More than 95% of the population in the Las Vegas MSA lives in the southern
portion of Clark County, occupying less than 5% of the MSA land area, while the Jersey City
PSMA has high population density throughout. While the majority of the MSAs remained in
the same categories from 1990 to 1995, there are several that exceeded 200,000 in population
by the year 1995. The Las Vegas MSA continued to grow and changed from a >500,000
category to a >1 million category by 1995.
Countywide population maps and MSA designations are most useful for identifying
those parts of a state that are not required to perform community exposure monitoring.
MSAs are not useful for defining the boundaries of MPAs in most cases. Figure 3.1.1 and
Appendix B show a wide variation in populations among the MSAs. A large number of
these had less than 500,000 people in them during 1990, and these are mostly in the
non-coastal western states. There are many small but highly populated MSAs along the east
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3-3
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MSA/PMSA1995 Population
< 100,000
100,000-200.000
200,000-500,000
500,000-1,000,000
1.000,000-2.000,000
2,000,000-4,000,000
4,000,000 - 6,000,000
6,000,000 - 8,000,000
> 8,000.000
0
500
1000 Kilometers
Figure 3.1.2. Metropolitan Statistical Areas and Primary Metropolitan Statistical Areas in the continental U.S. with 1995 populations.
-------
State
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
Metropolitan Area
Anniston, AL
Birmingham, AL
Decatur, AL
Dothan, AL
Florence, AL
Gadsden, AL
Huntsville, AL
Mobile, AL
Montgomery, AL
Tuscaloosa, AL
TYPE
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
Counties
Calhoun County
Blount County
Jefferson County
St. Clair County
Shelby County
Lawrence County
Morgan County
Dale County
Houston County
Colbert County
Lauderdale County
Etowah County
Limestone County
Madison County
Baldwin County
Mobile County
Autauga County
Elmore County
Montgomery County
Tuscaloosa County
1990
Population
116,034
840,140
131,556
130,964
131,327
99,840
293,047
476,923
292,517
150,522
1995 Est.
Population
117,263
881,761
139,837
134.368
136,184
100,259
317,684
517,611
315,332
158,732
1995 pop
density
(km2)
74.4
106.8
42.3
45.4
41.6
72.4
89.3
70.6
60.6
46.2
Area (km2)
1576.0
8,255.0
3304.0
2956.6
3274.0
1385.2
3556.2
7329.4
5199.3
3432.4
U)
Table 3.1.1. Alabama Metropolitan Statistical Areas and Primary Metropolitan Statistical Areas.
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State
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
Metropolitan Area
Bakersfield, CA
Chico-Paradise, CA
Fresno, CA
Los Angeles-Long Beach, CA
Los Angeles-Riverside-Orange County, CA
Orange County, CA
Riverside-San Bernardino, CA
Ventura, CA
Merced, CA
Modesto, CA
Redding, CA
Sacramento, CA
Yolo, CA
Salinas, CA
San Diego, CA
Oakland, CA
Sacramento-Yolo, CA
TYPE
MSA
MSA
MSA
PMSA
CMSA
PMSA
PMSA
PMSA
MSA
MSA
MSA
PMSA
PMSA
MSA
MSA
PMSA
CMSA
Counties
Kem County
Butte County
Fresno County
Madera County
Los Angeles County
Los Angeles County
Orange County
Riverside County
San Bernardino County
Ventura County
Orange County
Riverside County
San Bernardino County
Ventura County
Merced County
Stanislaus County
Shasta County
El Dorado County
Placer County
Sacramento County
Yolo County
Monterey County
San Diego County
Alameda County
Contra Costa County
El Dorado County
Placer County
Sacramento County
Yolo County
1990
Population
543.477
182,120
755,580
8,863,164
14,531,529
2,410.556
2,588.793
669,016
178.403
370,522
147,036
1,340.010
141.092
355,660
2,498,016
2,082,914
1,481,220
1995 Est.
Population
617.528
192,880
844.293
9,138.789
15.362,165
2,563,971
2,949,387
710,018
194,407
410.870
160,940
1,456,955
147,769
348,841
2,644.132
2,195,411
1,604,724
1995 pop
density
(km2)
29.3
45.4
40.2
869.1
174.4
1253.6
41.8
148.5
38.9
106.1
16.4
137.8
56.4
40.5
242.8
581.5
121.1
Area (km2)
21086.7
4246.6
20983.3
10515.3
88080.4
2045.3
70629.2
4781.0
4995.8
3870.9
9804.8
10571.3
2622.2
8603.8
10889.6
3775.7
13250.4
u>
o\
Table 3.1.2. California Metropolitan Statistical Areas and Primary Metropolitan Statistical Areas.
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State
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
Metropolitan Area
San Francisco, CA
San Francisco-Oakland-San Jose, CA
San Jose, CA
Santa Cruz-Watson ville, CA
Santa Rosa, CA
Vallejo-Fairfield-Napa, CA
San Luis Obispo-Atascadero-Paso Robles, CA
Santa Barbara-Santa Maria-Lompoc, CA
Stockton-Lodi, CA
Visalia-Tulare-Porterville, CA
Yuba City, CA
TYPE
PMSA
CMSA
PMSA
PMSA
PMSA
PMSA
MSA
MSA
MSA
MSA
MSA
Counties
Marin County
San Francisco County
San Mateo County
Alameda County
Contra Costa County
Marin County
San Francisco County
San Mateo County
Santa Clara County
Santa Cruz County
Sonoma County
Napa County
Solano County
Santa Clara County
Santa Cruz County
Sonoma County
Napa County
Solano County
San Luis Objspo County
Santa Barbara County
San Joaquin County
Tulare County
Sutler County
Yuba County
1990
Population
1,603,678
6,249,881
1.497,577
229,734
388,222
451,186
217,162
369,608
480,628
311,921
122,643
1995 Est.
Population
1,645,815
6,539,602
1,565,253
236,669
414,569
481,885
226,071
381,401
523,969
346,843
136,104
1995 pop
density
(km2)
625.7
341.1
468.0
205.0
101.6
117.6
26.4
53.8
144.6
27.8
42.6
Area (km2)
2630.4
19173.7
3344.3
1154.6
4082.4
4097.5
8558.6
7092.6
3624.5
12495.0
3193.9
OJ
-Ij
Table 3.1.2 (continued). California Metropolitan Statistical Areas and Primary Metropolitan Statistical Areas.
-------
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 midwestem, 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 hi Jefferson and Shelby counties; these are
more dense and of smaller size hi and around the city of Birmingham. Five-digit zip codes
may be associated with a few hundred people hi 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 hi this MSA.
From these figures, it appears that census blocks provide more population detail than
is needed for defining an MPA. Zip code boundaries provide reasonable distributions except
at the edges of a potential MPA. Census tracts are probably the most practical units of
population to define political boundaries for the Birmingham MPA. In Birmingham, AL, the
Jefferson County boundaries provide the first estimate of the MPA, with some of northern
parts of Shelby County that abut the Birmingham metropolitan area. As will be seen below,
county boundaries are not good starting points for California's San Joaquin Valley.
3.2 Identify Natural Air Basins
In many states, including Alabama and California, political boundaries do not
necessarily correspond to terrain features that may trap or channel source emissions or
3-8
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U)
0 IMPROVE sites
HMO] National Parks/Monuments
| NWR
National Forest
Reservations
400 0 400 800 Kilometers
Figure 3.1.3. National parks and monuments, national wildlife refuges, national forests, Indian reservations, and IMPROVE
background monitoring sites.
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u>
I—1
o
c) Census Tracts
b) Zip Codes
d) Census Bloc
Figure 3.1.4. Populated entities in the Birmingham MSA: a) counties, b) zip codes, c) census tracts, and d) census blocks.
-------
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 comer 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
3-11
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30 0 30 60 Kilometers
Figure 3.2.1. The Birmingham MSA in relation to counties, principal cities (+), and terrain
in Alabama.
3-12
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80
80
160 Kilometers
I | county
fi cities
MSA boundaries
Figure 3.2.2. Central California MSAs in relation to counties, principal cities (+), and
terrain.
3-13
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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 hi 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:
3-14
-------
U)
") Jefferson Co. PM sites
Shelby Populations
| 10-500
HI 500 -1,000
1,000 - 2,000
2,000 - 3,000
mm 3,000 - 4,ooo
4,000 - 5,000
5,000 - 6,000
6,000 - 7,000
7,000 - 9,000
Jefferson Populations
f I 0-500
J| 500-1,000
^B 1,000 - 2,000
§HJ§ 2,000 - 3,000
E?m 3,000 - 4,000
4,000 - 5,000
5,000 - 6,000
6,000 - 7,000
7,000 - 9,000
Figure 3.3.1. Populations in Jefferson and Shelby county census tracts. Jefferson County Health Department PM monitoring sites
are shown.
-------
UJ
I
Figure 3.3.2. Potential Monitoring Planning Area for the Birmingham MSA. Dots represent monitoring sites.
-------
Figure 3.3.3. Census tract boundaries and past and present PM monitoring sites in
California's San Joaquin Valley.
3-17
-------
a) Stockton/Modesto
b) Fresno/Madera/Clovis/Selma
c) Bakersfield
Figure 3.3.4. Potential San Joaquin Valley MPAs: a) Stockton/Modesto,
b) Fresno/Madera/Clovis/Selma, and c) Bakersfield.
3-18
-------
• 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^s-
Special purpose monitoring between and around these MPAs may be used for source
assessment, and may result in emission reduction requirements outside of the MPAs.
Alternatively, special purpose transport monitors between the MPAs might be appropriate.
In both instances, the data from these SPMs are not necessarily needed for compliance
assessments. In the second option, all areas within the jurisdiction are part of an MPA, and
measurements from any part may be used for determining compliance.
In other cases, the MPA may extend outside of the current boundaries of the air
quality control agency, as for the Birmingham metropolitan area that extends south into
Shelby County, AL. There are two options in this case:
• Designate two adjacent MPAs, with the dividing line at jurisdictional lines. This
has the advantage of making a clean break between the two administrative
agencies, but the disadvantage of complicated coordinated emissions reduction
strategies should the PM2.5 standards be exceeded.
• Designate one MPA, but with separate CMZs (or core monitoring sites) divided
by the jurisdictional line. This has the advantage of allowing monitoring
networks to be administered by the existing air pollution control agencies, while
allowing for more coordinated planning with respect to needed emissions
reductions should the standards be exceeded within different jurisdictions.
The Jefferson County Department of Health has jurisdiction over all air quality
monitoring in the county, but none in Shelby County. Shelby County conducts no PM
monitoring, and it does not maintain an infrastructure for air quality monitoring and
emissions control. These functions are handled by the state for most of the lightly populated
counties in Alabama. In this case, the few northern Shelby tracts in Figure 3.3.2 might be
eliminated from the MPA to keep the MPA entirely within the jurisdiction of Jefferson
County. It is possible that measurements entirely within Jefferson County adequately
represent population exposures just south of the Jefferson County border. This hypothesis
could be tested by short-term SPMs in Shelby County.
3-19
-------
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 mat 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.
3-20
-------
4.0 DEFINING PM2.5 COMMUNITY MONITORING ZONES
Community-oriented monitors and optional Community Monitoring Zones (CMZ)
within MPAs are intended to quantify neighborhood-scale exposures that are added to
underlying urban and regional PM contributions. In this discussion, the term CMZ is used to
represent the specifically defined area required by the regulations when spatial averaging is
intended for making comparisons to the annual PM2.5 NAAQS or a more general area only
used for description of the communities represented by one or more core sites. CMZs are
defined based on terrain, sources, and prior monitoring within and upwind of an MPA. Core
sites and optional CMZs should be reviewed annually to determine whether or not additional
core sites or CMZs are needed or changes to CMZ boundaries are appropriate. General
locations for core sites and CMZs are defined by the following steps:
1. Locate Emissions Sources and PopulatioiRLot 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, woodbuming, 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 PM2s or visibility if available; if not, use PMio or other air
pollutant measurements. Combine potential CMZs where these concentrations are
similar. When existing PM2s 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).
4-1
-------
4. Adjust CMZs to jurisdictional boundaries: Where air quality management
jurisdictional boundaries are within a natural CMZ, divide the CMZ along these
lines so that a separate CMZ resides within each jurisdiction.
5. Locate Sites: Where existing sites are within each CMZ, give them first priority
of PM2.s 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 comer 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
4-2
-------
Winter Morning
Afternoon
Night
-30 -20 -10 0 10 20 30 -10 -20 -10 0 10 20 M -JO -20 -10 0 10 20 30
Spring
Morning
Afternoon
Night
. .11
-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30
Summer Morning
Afternoon
Night
-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30
Fall
Morning
Afternoon
Night
-30 -20 -10 0 10 20 30 -30 -20 -10 0 10 20 30 -30 -20 -10
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 morning (0700-1000 CST), afternoon
(1200-1600 CST), and night (2200-0500 CST).
4-3
-------
Winter Morning
Afternoon
Night
30
20
10
0
-10
-20-
-30
-10
-20
-30 -20 -10
10 20 30 -30-20 -10
10 20 30 -30-20 -10
10 20 30
Spring Morning
Afternoon
Night
-to
-20
-30 -20 -10
10 20 30 -30 -20 -10
10 20 30 -30 -20 -10
10 20 30
Summer Morning
Afternoon
Night
-30 -20 -10
10 20 30 -30 -20 -10
10 20 30 -30 -20 -10
10 20 30
Fall
Morning
Afternoon
Night
•- . *
-30 -20 -10
10 20 30 -30 -20 -10
10 20 30 -30 -20
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 (Sep-Nov) during morning (0700-1000 PST), afternoon
(1200-1600 PST), and night (2200-0500 PST).
4-4
-------
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 RosesWind 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 ConcentratioriKhere
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
4-5
-------
sulfate. Reactions in fogs are the only mechanisms by which nearby sulfur dioxide
emissions can transform into significant quantities of sulfate. Much of the sulfate
observed in most locations without frequent fogs results from regional-scale
transport during which slower non-aqueous reactions or reactions in elevated
clouds occur.
4.3 Compare PM Concentrations
Few areas possess sufficient PM2.5 measurements to permit comparisons for the first
selection of CMZs. PMio measurements are often available, and where these show acceptable
spatial uniformity, it is likely that the PNfo.s would also show homogeneity if it had been
measured at the same locations. When the PMio measurements are non-uniform among
different sites, however, it may be the case that PM^s 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
and possibly PM2.5 concentrations for a year or for high PM episodes. These modeled
estimates can also be used in place of or in addition to measurements to further refine CMZs.
As shown in Section 2, PM2.5 is a complex combination of chemical compounds that is
difficult to accurately represent in mathematical models. Emissions rates from area and
mobile sources are often inaccurate, as these often are episodic and based on unknown fuels
and operating conditions. Secondary particle formation depends on many factors that are
often unknown. Transport under low-wind-speed conditions, that often accompany high PM
levels, is not well measured or modeled. Modeling results need to be extensively evaluated
against chemical- and size-specific PMa.s measurements to establish confidence that they
accurately represent the applicable emissions, meteorological, and transformation processes
Once the validity of the modeling results has been established, PM2.5 concentration isopleths
can be compared with the initial CMZ boundaries to further improve the homogeneity of the
CMZ.
Table 4.3.1 shows several uniformity measures from the seven PMio 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 comer of the county;
6) Wyland (WYLA) just northeast of Northside School; and 7) Tarrant Elementary School
which is a few kilometers northeast of the North Birmingham site, but >1 km distant from a
large industrial source complex. These seven sites, for which data are listed in EPA's AIRS
data base, are fewer than the number of sites listed in the AIRS site log. Several source-
oriented SPMs have been operated over several years in Jefferson County, and these data
should be included in this type of analysis.
4-6
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Annual Averages (Mg/m )
Year
1990
1991
1992
1993
1994
1995
BESS
33.5
31.9
28.4
28.4
24.8
27.2
NOBI
47.6
41.0
38.6
32.7
INGL
34.6
30.6
28.5
26.5
24.7
NOSC
40.3
36.7
31.2
29.3
27.2
27.6
LESC
30.7
30.6
27.7
25.3
23.7
24.6
WYLA
38.1
33.1
31.4
29.6
-
TASC
37.3
32.0
30.1
27.0
25.6
27.7
98th Percentile 24-Hour Averages (ug/m3)
Year
1990
1991
1992
1993
1994
1995
BESS
62
79
52
58
50
56
NOBI
111
100
91
81
INGL
72
75
52
62
47
NOSC
77
80
66
69
58
52
Spatial Average
Year
1990
1991
1992
1993
1994
1995
Spatial
Average
37.4
33.7
30.9
28.4
25.2
26.8
Spatial
Std
5.1
3.5
3.4
2.2
1.2
1.2
Spatial
COV
13.6
10.5
11.1
7.9
4.7
4.7
Max
LESC
61
70
52
61
48
50
WYLA
85
78
70
64
TASC
76
76
55
58
50
57
Statistics (ug/m3)
Min
Average Average
47.6
41.0
38.6
32.7
27.2
27.7
Intersite PM]0 Correlation Coefficients
BESS
NOBI
INGL
NOSC
LESC
WYLA
TASC
BESS
1.000
0.848
0.872
0.916
0.873
0.811
0.844
NOBI
1.000
0.786
0.909
0.809
0.879
0.794
INGL
1.000
0.855
0.885
0.822
0.933
NOSC
1.000
0.856
0.834
0.837
30.7
30.6
27.7
25.3
23.7
24.6
Average
+20%
44.9
40.4
37.0
34.1
30.2
32.1
Average
-20%
30.0
26.9
24.7
22.7
20.2
21.4
Average Average
+10% -10%
41.2 33.7
37.0 30.3
33.9 27.8
31.2 25.6
27.7 22.7
29.4 24.1
( 1990-1 993, n=226)
LESC
1.000
0.846
0.833
WYLA
1.000
0.799
TASC
1.000
Table 4.3.1. Uniformity measures for PMio in Birmingham.
4-7
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The first two sub-sections of Table 4.3.1 show the annual arithmetic averages and 98th
percentile (second highest 24-hour maximum with sixth-day sampling) for these sites. Note
that the North Birmingham, Inglenook, and Wyland sites have no data after 1993. The North
Birmingham hivol size-selective inlet (SSI) monitor was replaced by a continuous TEOM
monitor that acquires hourly PMio 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 PMio
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 PMio concentrations were very
different from the annual average and 98th percentile concentrations at other sites during 1990,
but by 1993 they were much more similar to those at the other sites. In 1994, the INGL site
in the northeast corner of Jefferson County, and the LESC site in eastern Jefferson County
had similar average and 98th percentile PMio levels. In 1995, the BESS, NOSC, and TASC
stations near the center of the MPA show almost identical annual averages, and 98th percentile
PMio concentrations that differ by no more than 6 ug/m3. The LESC site shows ~3 ug/m3
lower annual PMio average, and a separate CMZ could be defined around this site.
Alternatively, the MPA might be denned 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 ug/m3 in 1990 to 1.2 ug/m3 in 1994. This resulted from the
decrease in concentrations at the NOBI source-oriented site, and its elimination after 1993.
Even in 1993, however, the spatial coefficient of variation (COV) was less than 10% when the
NOBI site was included in the average.
The final panel of Table 4.3.1 shows the spatial correlation coefficients among the
different sites for the 1990 through 1993 periods when data were available from each one.
Each of these exceeds 0.8, with the exception of the NOBI site. This shows that the
information content of the different monitoring locations is similar, and that some PMio sites
can be sacrificed in favor of collocated PM2 5 sites at most of the Jefferson County sites.
Other analyses of historical PMJO and PM^s that provide a basis for selecting CMZs,
and also serve as a justification for de-commissioning PMio sites in favor of PM2 5 sites are:
• Spatial Plots of Maximum, Annual and Seasonal Average ffMse consist
of pies or bars with areas or heights corresponding to PM concentration on a map.
They can be displayed on the maps of source emissions in conjunction with the
meteorological plots to gain a better understanding of source Zones of Influence
and receptor Zones of Representation.
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• Time Series Plots of PM Mass and Selected Chemical ConcentraflitajsE
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 Concentration£ollution 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 PMio concentrations
varying together. Sampling sites that show substantial deviations from other sites in the area
are identified and reasons for their deviation is sought. These sites are excluded from
consideration as core sites if they do not have neighborhood- or urban-scale zones of
representation.
CMZ boundaries are adjusted to include contiguous groups of measurements that
show a reasonable degree of spatial homogeneity, as indicated by the various homogeneity
measures in the analyses above.
4.4 Adjust CMZs to Jurisdictional Boundaries
Just as the MPAs give preference to existing Jurisdictional boundaries, the CMZ
definitions may also conform to these boundaries as long as they consist of defined populated
entities. These may include municipal borders or planning districts. An example has already
been given in Section 3. A single MPA might include portions of Jefferson and Shelby
Counties with two CMZs. The Jefferson County CMZ would be monitored by the Jefferson
County Health Department. The Shelby County CMZ would be monitored by the State of
Alabama. On the other hand, a special monitoring study might show that measurements in
Jefferson County also apply to population exposures in the more densely populated portion of
Shelby County, thereby eliminating the need for an additional CMZ.
4.5 Locate Sites
There are two options for the community-oriented monitoring approach for making
comparisons to the annual PM2.5 NAAQS. The network can either be constructed in terms of
using: 1) individual community-oriented core sites; or 2) taking the spatial average of two or
more eligible core sites in a well defined community monitoring zone. Existing sites within a
CMZ are evaluated against the PM siting criteria in Section 5. Sites that do not meet those
criteria for neighborhood or urban zones of representation are eliminated as potential
compliance monitoring sites for comparison to annual standards, though they may be
designated as daily compliance sites or SPM sites. Core PM 2.s sites should include: 1) a
population-oriented site with the highest expected community-oriented concentrations; 2) a
site with high population density with poor air quality (high population exposure); and 3) a
site collocated at a PAMS site, if the MPA is a PAMS area.
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Number of Core
MSA Population PM^ 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 PN^.s 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 PMio NAMS sites should be given prime consideration. In
addition, each state should have at least one core site for regional transport monitoring and
one site for regional background monitoring. For each MSA or PMSA with a population over
1 million, a continuous fine particle analyzer (e.g., beta attenuation analyzer, nephelometer,
transmissometer, or inertial microbalance TEOM) must be located at a core PM2 5 site. With
these criteria, neither the Jefferson County or San Joaquin Valley examples cited here would
require continuous monitors, since neither one contains an MSA with more than 1 million
inhabitants. However, continuous monitors should be considered in all areas with population
greater than 200,000 to assist with public reporting of real-time data.
The selection of the known or anticipated community-oriented monitoring site with the
highest concentration as the first site in the CMZ serves two purposes. First, it allows the site
to be used for determination of the 24-hour and annual PM2 5 standards. Second, it
encourages the location of other sites within the CMZ boundaries to give a more
representative PM2.5 spatial average.
The number of required monitors in an MPA is a function of the MPA's population.
Table 4.5.1 shows the minimum number of these core monitors for a given MSA population.
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5.0 MONITOR SITING
PM2.5 monitors are situated to meet requirements as core sites, community averaging
sites, or daily compliance sites. Internal requirements are those for operating the needed
instruments, while external criteria address site surroundings to achieve specific monitoring
purposes.
5.1 Internal Siting Criteria
Internal criteria refer to the logistics of locating and service instruments for multi-year
monitoring. These include:
• Long-term Site Commitment:NAMS sites are meant to measure trends as well
as compliance, and a long-term commitment from the property owner for
continued monitoring is required. Public buildings such as schools, fire stations,
police stations, recreation halls, and hospitals often have more stability and a
motive for public service than do private or commercial buildings.
• Sufficient Operating Space: A large, flat space, elevated at least 1 m but no
more than 14 m above ground level, is needed to place monitors and monitoring
probes. The space available for samplers should be at least 5 m distant and upwind
(most common wind direction) from building exhausts and intakes and at least 2 m
from walls, parapets, or penthouses that might influence air flow. Buildings
housing large emitters, such as coal-, waste-, or oil-burning boilers, furnaces or
incinerators, should be avoided.
• Access and Security: Access to the sampling platform should be controlled by
fencing or elevation above ground level. Sampler inlets should be sufficiently
distant (>10 m) from public access to preclude purposeful contamination from
reaching them in sufficient quantities to bias samples. Access should be controlled
by a locked door, gate, or ladder with documentation of site visitations and the
purposes of those visits.
• Safety: Wiring, access steps, sampler spacing, and platform railings should
comply with all relevant codes and workplace regulations, as well as common
sense, to minimize potential for injury to personnel or equipment.
• Power: Power should be sufficient for the samplers to be operated on a long-term
basis, as well as for special study and audit samplers to be located at a site. Where
possible, a separate circuit breaker should be provided for each instrument to
prevent an electrical malfunction in one monitor from shutting off power to the
other monitors at the site.
• Environmental Control: Environments surrounding monitoring instruments
should be maintained within the manufacturers specifications for proper instrument
function. Most FRM filter-based samplers are designed to operate under a wide
5-1
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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
monitoring siting (U.S. EPA, 1987), and they have proven their validity in
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, PMi.s sites should give preference to existing sites that
make other measurements. For example, high local wind gusts may
5-2
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100
w
o
X
(0
S
o
oc
CD
I
O
<
NEIGHBORHOOD SCALE
40 -
20
0 20 40 60 80 100 120 140 160
Distance of PM10 Samplers from Nearest Traffic Lane, meters
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 PMio and PM2 5 monitors will provide information on the
size distribution of suspended particles.
5.3 Evaluating Zones of Representation
A site originally selected to represent community exposure (generally on a
neighborhood or urban scale) may have its zone of representation change owing to long-term
changes in land use or short term events that affect that particular site.
• Annual Site Surveys: The land use and sources around a monitoring site may
change from year to year, especially in high growth areas. Maps should be
updated as part of the annual measurement network summary, and the setbacks
5-3
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from emissions sources and obstructions should be re-evaluated to ascertain that
they are still met.
• Records of Intermittent Events: High PM or PMio concentrations may have
corresponded to a specific event, such as construction or a fire, occurring near the
measurement location. Visual events should be recorded as part of the
measurement network maintenance, and these should be summarized at the end of
each year for inclusion in the annual network evaluation report.
• Saturation Monitoring Studies:To evaluate the zone of representation of sites,
many monitors may be located at different distances around and between monitors
in a CMZ. Spatial uniformity measures can be determined for these temporary
locations and compared to those from the sites within the CMZ to evaluate how
well the long-term sites represent population exposures to PM.
5.4 Evaluating Siting Redundancy
The spatial uniformity measures specified in Section 4 to can be applied to PM2.5
concentrations at the sites within a CMZ to determine the extent to which each one supplies
additional information concerning exposure. When information content is redundant,
recommendations can be justified for the transfer of sites within a CMZ to other parts of the
CMZ or to other CMZs.
5.5 Evaluating Network Adequacy for Spatial Averaging
Appendix D of 40 CFR 58 lays out several specific requirements for PM2.5 monitoring
sites to be eligible for spatial averaging: (1) they each represent a neighborhood or larger
spatial measurement scale and (2) their community monitoring zone represents homogeneous
air quality. To satisfy the latter, the sites' annual averages must be within +20% of the CMZ-
wide average on an annual basis, be influenced by similar sources and be reasonably correlated
on a daily basis (r > 0.6). This assessment can be made in a preliminary manner with one year
of data, but will require three years of PM2.s air quality data before final evaluation of site
eligibility and comparisons to the NAAQS can be made.
EPA recommends that the State follow additional design criteria to ensure that the air
quality in a community monitoring zone is spatially homogeneous. This should include an
evaluation of several factors including the year-to-year variability of each individual site, the
effect of changing emissions over time, the relative distribution of concentrations among the
sites, the spatial patterns within the CMZ, similarities and differences in speciated PM2.5, and
the relationship between population density and air quality patterns.
5.5.1 Temporal Behavior
The evaluation of ±20% year-to-year variability should support spatially homogeneous
air quality during 3-year period.
5-4
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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%.
4AI
15
10
5
n
ft
^-^ -_ ._.-
- "
YES
I98 1999 20C
VS.
4.W
15
10
5
n
NO
1999
200
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.
- 20%
20
10
5
+/- 30%
1999 2000
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.
5-5
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site avg=14.8
modeling
interpolation
-------
PM2.5
concentrations
B2J high
medium
low
Air Quality Pattern Population Density
B-
Supports
placement
Does not support
placement for
spatial averaging
Figure 5.5.5. Example of using population density for monitor placement.
5-7
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6.0 STATE PM MONITORING NETWORK DESCRIPTIONS,
ANNUAL REPORTS, AND NETWORK EVALUATIONS
As implied by this guidance, monitoring network configurations are likely to change
from year to year. NAMS sites will remain fixed to the extent allowed by such considerations
as leasing arrangements, urban renewal projects, and loss of monitoring locations due to other
construction, fires, or natural disasters. NAMS will include some of the current NAMS
network for PMio and the current PAMS network for ozone to maintain continuity with long-
term data sets acquired at these sites. PAMS with PM2.s 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 PMio and
PM2.5. The phase-in of PM2.s monitors and changes in the existing PMio 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 theMPAs 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.
6-1
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or
4. Transport and Background Areas: Show the areas where transport between
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 ChangShow the planned start-up
date for each new PM25 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 IE equivalent or other non-reference/equivalent methods), and its
measurement frequency. Show which PM]0 monitoring sites will be modified by
discontinuation or reduction in measurement frequency. Network changes are to
be phased in between 1998 and 1999 for PM2.5 and between 1998 and 2000 for
PM.O.
A separate appendix for each MPA should present the following detailed information:
1. Introduction: Describe the physical setting of the MPA, population
characteristics, climate and weather, dominant economic activities, and emissions
sources. Much of this information can be concisely and efficiently summarized on
maps such as USGS (1:50000 or 1:2400), aerial photographic, or commercial
maps available on CD-ROM (Appendix A identifies several options).
2. Community Monitoring Areas or Zones: Show maps of the selected
community monitoring areas or community monitoring zones, if appropriate, and
justify them based on the procedures in Section 4. Document modeling and data
analysis activities that were conducted to determine these zones. These maps
should include, where available and appropriate: 1) populated entity boundaries
(e.g. census tracts or blocks), 2) relevant jurisdictional boundaries; 3) commercial,
residential, agricultural, and industrial land uses; 4) suspected area source
emissions hot spots (e.g. wood burning communities, diesel emissions hot spots
identified from bus transfer locations, railyards, marine terminals, waste recycling,
land preparation, unpaved roads, etc.). Include tables of spatial uniformity
measures (spatial averages, spatial coefficients of variation, 98th percentile
concentrations, and spatial correlations) using existing data to determine the zone
of representation of existing monitors. Use wind roses and trajectories to identify
potential transport pathways and terrain maps to identify potential barriers to
transport.
3. Sampling Site Descriptions: Provide site descriptions, including maps showing
surrounding sources as well as verbal descriptions of activities surrounding the
site. Define the variables measured at each site in terms of observables measured
(e.g., PMio mass, PMa.s mass, chemical composition), sample duration, frequency,
and measurement method.
6-2
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4. Sites Intended for Comparison with NAAQSpecify those sites that acquire
measurements to be compared with the NAAQS, with their designation as
community-oriented, daily compliance, or other sites. Identify sampling methods
that acquire compliance data at these sites.
5. Special Purpose Monitoring Projects: Where there is doubt concerning the
validity of the CMZs, the zone of representation of a sampling site, or the influence
on PM from different sources, define special purpose monitoring projects to
resolve these concerns.
6. Phase-In and Responsibilities: Estimate costs of hardware, operation, and
maintenance for the number of stations and measurement frequencies required, and
reconcile these costs with current resources. Justify trade-offs between additions
of PM2.s monitors at the expense of PMio 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 and PM2.s annual
averages, maximum concentrations, and 98th and 99th percentile concentrations for
each monitoring site for each year. For most situations these data can be obtained
from EPA's AIRS database. This will include data from non-compliance monitors,
including SPMs, as well as for compliance monitors.
2. Spatial Averages: When spatial averaging is utilized, include tables of spatially
averaged annual-average PM2.s concentrations for each CMZ for each year.
Identify sites with annual averages differing by more than ±20% from the spatial
average, and remove them from the average.
3. Compliance Statistics: Include three-year-average annual averages for each core
site (and when appropriate for each CMZ) and three-year-average 98th and 99th
percentile averages for each eligible site.
4. Compliance Determination Compare the compliance statistics with standards,
and discuss the compliance or non-compliance of each site and/or CMZ in the
MPA. Compare measurements at background and transport sites with those at
core sites to estimate the extent to which urban-scale (within the MPA) or
regional-scale (within and outside of the MPA) sources contribute to the excess
concentrations. Discuss the comparison of concentrations at background and
transport sites with the core sites.
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6.3 Annual Network Evaluation
The annual network evaluation should include the following information for each
MPA:
1. Changes in Site CharacteristicsDocument changes in site exposure owing to
construction or demolition of nearby buildings or the growth of foliage, the
presence of temporary (e.g., building construction, road repair) or permanent (e.g.,
an industrial facility or a new highway) emitters within 1 km of the site, or special
events (e.g., accidental fires, major wind storms). Record the magnitude, location,
and duration of these changes. Examine PM measurements in conjunction with
these changes to evaluate the continued population-orientation of the site.
2. Concentration Uniformity Measures:Evaluate spatial time-series plots, spatial
correlation coefficients, differences between site-specific averages and 98th
percentile values, and spatial coefficients of variation within and between CMZs
within an MPA. These measures are especially important for evaluating the spatial
average used to determine compliance with the annual PM^s standard. The annual
average at each site within a CMZ should not differ by more than ±20% from the
CMZ average. If one or more of these site-specific averages exceeds this
tolerance, it may be necessary to re-define the CMZs to better represent
community exposure, or to re-evaluate the site exposure. On the other hand, high
correlations among measurements, low spatial coefficients of variation, and annual
averages and 98th and 99th percentiles that do not differ by more than ±5% within a
CMZ indicate that some stations are redundant. Justification can be made for
moving one or more of the non-NAMS trend stations to another location where it
might better represent population exposure. Similar results for measurements in
different CMZs within the MPA indicate that the adjacent CMZs with similar
concentrations might be combined into a larger CMZ, with a consequent reduction
in the number of monitors needed to represent the spatial average.
3. Monitoring Site Additions and Deletions: When sites are determined to no
longer represent population exposure, or when spatial uniformity measures show
that they provide consistently redundant information, recommendations and
justifications for deletion may be submitted to the EPA regional office for
approval. When spatial uniformity measures show high variability within a CMZ,
when a populated area expands beyond its original boundaries, or when special
monitoring sites are deemed necessary, measurement locations may need to be
added. These, too, should be justified. The intent of this annual evaluation is to
continually re-define the network, within available monitoring resource constraints,
to best represent population exposures. This section of the evaluation allows
substantial flexibility for networks to evolve to attain this end.
4. Changes to CMZ and MPA Boundaries and Site Designations. SLAMS with
PM2 5 standard exceedances should be considered for re-designation as core sites.
SPM sites showing NAAQS violations should be considered for designation as
6-4
-------
SLAMS sites. Changes in population or emissions may require changes in MPA or
CMZ boundaries, including the creation of additional MPAs or CMZs. The
evaluation of spatial uniformity measures may also justify recommendations for
changes in MPA or CMZ boundaries. In particular, the 2000 census will provide
more current information on population distributions, and when these data become
available in 2001 or 2002, the MPA and CMZ boundaries will need to be
re-assessed. Locally generated land-use patterns in rapidly growing areas can also
be used to determine the extent to which the boundaries of planning areas and
averaging zones should be expanded.
6-5
-------
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7-6
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APPENDIX A
Sources of Data Used for Network Design and Evaluation
-------
Appendix A (continued)
Sources of Data Used for Network Design and Evaluation
Database
U.S. Geological Service Digital Elevation
Model database
U.S. Geological Service North American
Digital Elevation Models
U.S. Geological Survey Topographic, Image
and Related Maps
The USGS Geographic Names Information
System (GNIS)
lohns Hopkins Univ/ Applied Physics
Laboratory Digital Elevation Model images
U.S. EPA Forest Land Distribution Data of
the United States
U.S. Bureau of the Census TIGER Mapping
Service
U.S. Bureau of Census U.S. Gazetteer
Temporal Urban Mapping at USGS
USGS Geologic Information
Description
This site contains 1 minute or 1 -250,000-scale
Digital Elevation Models of the US arranged by
USGS quadrants
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.
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 IS
minute (1:50,000 and 1:63,360) County maps are
available in 1:50,000 and 1:100,000.
The GNIS contains name and location information
for approximately 2 million physical and cultural
features located throughout the U.S. and it's
territories.
This site provides excellent 5x5 degree color
shaded relief maps of the lower 48 states.
Full U.S. forest GIS coverages and GIF images are
available for forest type and forest density
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.
This gazetteer is used to identify places to view with
the Tiger Map Server and obtain census data from
the 1 990 Census Lookup server. You can search for
places, counties or MCDs by entering the name and
state abbreviation (optional), or 5-digit zip code.
This site includes several digital satellite and
geographic images of the SF Bay area (data
limited to Bay area & Baltimore/Washington area)
This she mainly contains digital geologic maps for
the central and western US, national geologic map
database but has some useful base maps
Access
WWW and ftp
WWW and ftp
These maps can
be obtained at
local map
dealers as well
as from the
USGS
FTP
WWW
WWW
WWW
WWW
www
WWW
Format(s)
USGS DEM format
(ASCII)
compressed BIL
(band interleaved by
line) format that can
be read into GIS
software
paper maps
Formatted ASCII
text file
Digital GIF format
Digital GIF,
ARC/INFO grid
formats
Digital GIF format
Census data is
provided in HTML,
tab delimited or
CODATA formats.
The map images are
in the digital GIF
format.
Digital GIF format
Maplnfo,
ARC/INFO format
Relevant Data Fields
Digital elevation data are
read into ARC/INFO,
ArcView, other GIS
packages as data layers They
are also used in dispersion
models.
Digital elevation data are
read into ARC/INFO,
ArcView, other GIS
packages as data layers. They
are also used in dispersion
models.
Topographic maps may be
useful in defining MPAs,
CMZs and general mapping
at the microscale to urban
scale.
This database provides
location (latitude and
longitude) for populated
entities as well as geographic
features.
These images can be easily
printed out or imported into
ArcView as a terrain layer.
This data set may be useful
in defining natural air basins
by showing forest cover &
tree type coverages as land-
use.
This web site produces quick
turn-around, user-defined
US maps zoomed in on any
region of the U.S.
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.
population growth maps,
satellite orthophoto images
and Lansat of San Francisco
geologic GIS and US base
maps
Reference or Internet Address
http://edcftp.cr.usgs.gov:80/pub/data/DEM/250/
http://edcwww.cr.usgs gov/landdaac/gtopo30/gtopo30.html
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.ujgs.gov/esic/ujrmage/dealers.html or the local
phone book. Maps may be ordered from the USGS directly by calling (800) USA-
MAPS or by mail at: USGS Information Services, Box 25286, Denver, CO 8022S.
http://rnapping.usgs.gov/www/gnij/gnisftp.html
http://fermi.jhuapl.edu/states/states.html
http://www.epa.gov/dcics/grd/forestjnventory/
http://tiger.cenjus.gov/
http://www.census.gov/cgi-bin/gazetteer
http://edcwww2.CT.usgs.gov/umap/umap-html
http://geology.usgs.gov/maps.html
-------
Appendix A (continued)
Sources of Data Used for Network Design and Evaluation
Bay Area Digital Geo Reource (BADGER)
National Park Service boundary database
US TIGER/Line databases
U.S. Census Summary Tape Files (STF)
U.S. Census metropolitan area population
estimates
Landview II software
Voyager and Voyager Viewer mapping
software
ESRI ARC/INFO and ArcView CIS software
Western Regional Climate Center
High Plains Climate Center
Midwestern Climate Center
Northeast Regional Climate Center
Southern Regional Climate Center
Southeastern Climate Center
National Climatic Data Center
IMPROVE network data
U.S. EPA SCRAM Meteorological Data
The Bay Area Shared Information Consortium
makes available maps, images and data on the San
Francisco Bay area
National Park Service boundaries and regions are
distributed in ARC/INFO CIS export format at this
site. An ARC/INFO coverage of U.S. States is also
available at this site
These disks contain a multitude of geographic
information such as state,county,census block ,
census tract etc boundary databases
These disks contain US population &
socioeconomic data taken during the 1990 census
199010 1995 MSA/PMSA/CMSA/NECMA
population estimates by the U.S Bureau of Census
Landview is a display software for TIGER/Line,
STF data. Currently a DOS application with limited
exporlabilitv
Voyager is a multidimensional data browser for
browsing Voyager data files
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.
This climate center serves CA, NV, OR, WA, ID,
MT, UT. AZ. NM: meteorological data
This climate center serves ND, SD, NE, KS, CO,
WY data; meteorological data
This climate center serves MN. IA, MO, WI, IL, IN,
KY. MI. OH; meteorological data
This climate center serves ME, NH, VT, NY, MA,
RLCT, PA, NJ, DE, MD, WV; meteorological data
This climate center serves LA, TX, OK, AR, MS,
TN, meteorological data
This climate center serves SC, NC, VA, AL, FL,
GA, Puerto Rico, Virgin Islands; meteorological
The NCDC provides a wealth of climate data,
publications, databases, images related to climate in
the US and world-wide. Some data is available on-
line.
Data is available for the IMPROVE network
(visibility in National Parks & wilderness areas)
Airport Surface & Upper Air Meteorological data
for sleeted US airports
WWW
WWW
CD-ROM
CD-ROM
WWW
CD-ROM
WWW
Through ESRI
WWW
WWW
WWW
WWW
WWW
WWW
WWW and ftp
listserve via e-
mail to
subscribe
internet (web,
telnet, ftp),
phone dial-up
Several digital
formats including
GIF satellite images
ARC/INFO export
brmat
ASCII (in
TIGER/Line
database format)
ASCII
ASCII text files that
are easily
importable into
spreadsheet
>rograms
Reads ASCII
TIGER and census
data
Reads Voyager data
files.
CD-ROM
various formats
various formats
various formats
various formats
various formats
various formats
various formats
ASCII text files
ASCII text files
10 meter satellite imagery
over SF Bay area
>ark boundaries in GIS
US boundary databases
which includes some land-
use information
JS population databases
US MSA, CMSA, PMSA,
NECMA population
estimates
DOS based text and
graphical display and
provides quick hard copies
This software package is
useful as a visualization tool
for temporal and spatial air
quality databases.
These software tools can be
used to simply display
cartographic data or provide
spatial data analysjs of the
data sets in this appendix.
climate data
climate data
climate data
climate data
climate data
climate data
climate data world-wide but
mainly for the United States
aerosol and visibility data
surface meteorology and
mixing height
ittp://www.svi.org/badger.html
ftp://ftp.its.nps.gov/pub/park boundaries/
Customer Services, Bureau of Census, 301-457-4100
Customer Services, Bureau of Census, 301-457-4100
http://www.census.gov/population/www/esrimates/metropop.html
Customer Services, Bureau of Census, 301-457-4100
http://capita.wustl.edu/CAPITA/utilities/utilities.html
fhe 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
ittp://www.esri.com or (800) 447-9778.
http://wrec.sage.dri.edu
http://hpccsun.unl.edu
http://mcc.wsw.uiuc.edu
http://met-www.cit.cornell.edu/
http://www.srcc.tsu.edu/srcc.html
http://water.dnr.state.sc.us/climate/sercc
http://www.ncdc noaa.gov/
LISTSERVE@caesar.ucdavis.edu; "SUBSCRIBE IMPROVE-DATA-USERS"
http://www.epa.gov/scramOOI/t25.htm
-------
Appendix A (continued)
Sources of Dat? Used for Network Design and Evaluation
U.S. EPA Aerometric Information Retrieval
System (AIRS)
U.S. EPA Region 3 CIS data
U.S. EPA Region 7 CIS data
U.S. EPA Region 8 CIS data
U.S. EPA Region 9 Nonattainment Maps
STATSGO data (U.S. Department of
Agriculture Soil Conservation Service
Geographic Database)
Oregon digital map library
US. GeoData DEM and DLG files from the
EROS Data Center
Aerometric Information Retrieval System — primary
data source for air quality and meteorological data
from the EPA
The region 3 web site gives access to the EPA
Region HI Land Cover Data Set and GIS coverages
of watersheds, forests, ecoregkms, TRI sites,
CERCLIS, hydrology, roads, railroads, NPL and
USGS DEM.
The Quad 100 GIS data library contains 1 : 100,000-
scale base data for Region 7 and is tiled by United
Slates Geological Survey (USGS) l:IOO,000-scale
(30 X 60 arc minutes) quadrangle boundaries.
This site contains Region VIII Environmental
ARC/INFO GIS Data covering natural and man-
made boundaries.
This she provides O,, CO, NO3,PM,0,class 1, tribal
land maps in EPA's region 9
This US soil geographic database is arranged
according to state.
This site contains Oregon 1:2,000,000 to 1:24,000
digital maps of agriculture land, cities, geology,
highways, population, soils, etc
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.
phone dial-up
WWW
WWW
WWW
WWW
WWW
WWW
WWW
ASCII reports
GIS coverages are in
ARC/INFO export
format with GIF
preview images.
The land cover data
set an ERDAS
image readable in
ARC/INFO.
This site provides
ARC/INFO GIS
coverages.
The data files are in
Unix GZIPed
Arc/Info 7.02
export format.
Digital GIF format
ARC/INFO, DLG-3
ARC/INFO
These digital files
can be imported
into ARC/INFO and
other GIS packages.
air quality and emissions
data such as TSP, PMIO,
PM2 s,Oj, NO, CO data
The data provided at this
web site may be useful in
determining state planning
areas and CMZs by showing
land use and possible
biogcnic emission sources.
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.
Country Boundaries,Census
Tract
Boundaries,Dams,Ecoregion
s.Federal Land
Boundary .Hydrologic Unit
Codes.Linetr Hydrology
(streams, nvers,
etc),PolygonaI
Hydrology,Mines,National
Pollution Dischlrge.Histork
& Current Place
Names,Raikxnds,Seismic
Activity .State
Boundary. Tribal Land.Toxic
Release
lnventory,Treatment,Storage
& Disposal Facilitres,Zip
Codes
nonattainment maps
GIS soil coverages for
ARC/INFO and ArcView
many GIS useful coverages
for land-use but only for
Oregon
DEM provides elevation &
boundary data for use with a
GIS. The DLG provide
boundary, streets and
topographic data for a GIS.
http://www.epa.gov/docs/airs/airs.html or US EPA OAQPS at (919) 541-5454
http://www.epa.gov/reg3giss/libraryp.htm
http://www.epa.gov/region07/envdata/gis/ql00lib.html
http://www.epa.gov/region08/data.html
http://www.epl.gov/region09/air/mapi/maps lop.html
ftp://ftp.ftw.nrcs.usda.gov/pub/statsgo/
http://www.sscgis.state.or.us/data/data.html
http://edcwww.cr.usgs.gov/doc/edchome/ndcdb/ndcdbhtml
-------
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
Sute
AK
AL
AL
AL
AL
AL
AL
AL
AL
AL
AL
AR
AR
AR
AR
AR-OK
AZ
A2
AZ
AZ-UT
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
Metropolitan Area
Anchorage, AK
Anniston.AL
Birmingham, AL
Decatur.AL
Dothan.AL
Florence. AL
Gadsden.AL
HuntsviHe, AL
Mobile, AL
Montgomery, AL
Tuscakxaa, AL
FayetteviHe-Springdate-Rogere, AR
Jonesboro, AR
Little Rock-North Little Rock. AR
Pine Bluff. AR
Fort Smith, AR-OK
Phoenix-Mesa, AZ
Tucson, AZ
Yuma. AZ
Flagstaff, AZ-UT
Bakersfield, CA
Chico-Paradise. CA
Fresno, CA
Los Angeles-Long Beach, CA
Los Angeles-Riverside-Orange County, CA
Orange County, CA
Riverside-San Bernardino, CA
Ventura. CA
Merced, CA
Modesto. CA
Redding. CA
Sacramento, CA
Yolo, CA
Salinas, CA
San Diego, CA
Oakland, CA
Sacramento-Yolo, CA
San Francisco, CA
TYPE
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
PMSA
CMSA
PMSA
PMSA
PMSA
MSA
MSA
MSA
PMSA
PMSA
MSA
MSA
PMSA
CMSA
PMSA
Counties
Anchorage Borough
Calhoun County
Bkxmt County
Jefferson County
SLCUr County
Sheby County
Lawrence County
Morgan County
Date County
Houston County
Colbert County
Lauderdab County
Etowah County
Limestone County
Madison County
Baldwin County
Mobile County
Autauga County
Bmore County
Montgomery County
Tuscatoosa County
Benton County
Washington County
Faulkner County
Lonoke County
Pulaski County
Saline County
Jefferson County
Crawford County
Sebastian County
Sequoyah County
Mancopa County
Final County
Pima County
Yuma County
Coconrno County
Kane County
Kem County
Butte County
Fresno County
Madera County
Los Angeles County
Los Angeles County
Orange County
Riverside County
San Bernardino County
Ventura County
Orange County
Riverside County
San Bernardino County
Ventura County
Merced County
Stanislaus County
Shasta County
El Dorado County
Placer County
Sacramento County
Yoto County
Monterey County
San Diego County
Alameda County
Contra Costa County
B Dorado County
Placer County
Sacramento County
Yoto County
Marin County
San Francisco County
1990
Population
226.338
116.034
840.140
131,556
130.964
131,327
99.840
293,047
476,923
292.517
150,522
210.908
513,117
85,487
175,911
2,238,480
666.880
106,895
101,760
543,477
182.120
755,560
8.863.164
14.531.529
2.410,556
2.588.793
669,016
178.403
370.522
147,036
1.340.010
141.092
355.660
2,498,016
2,082.914
1.481.220
1.603.678
1995 Est.
Population
251.335
117,263
881.761
139.837
134.368
136,184
100,259
317,684
517.611
315,332
158,732
252,640
543,568
84.042
188,572
2.563.582
752,428
132.869
116.498
617.528
192.880
844.293
9.138,789
15.362,165
2,563,971
2.949.387
710,018
194,407
410.870
160.940
1.456.955
147,769
348,841
2,644.132
2,195.411
1.604,724
1,645.815
19*5 pop
(tensity
(ton-1)
57.2
74.4
106.8
42.3
45.4
41.6
72.4
89.3
70.6
60.6
46.2
54.4
72.2
36.7
40.3
67.9
31.6
9.3
29.3
45.4
40.2
869.1
1744
1253.6
41.8
1485
38.9
106.1
16.4
1378
56.4
40.5
242.8
581.5
121.1
625.7
Area (km1)
4396.9
1576.0
8.255.0
3304.0
2956.6
3274.0
1385.2
3556.2
73294
5199.3
34324
4645.3
7533.2
2291.6
4676.9
37746.7
23794.4
142824
21086.7
4246.6
20983.3
10515.3
880804
2045.3
70629.2
4781.0
4995.8
3870.9
9804.8
10571.3
2622.2
8603.8
10889.6
3775.7
13250.4
2630.4
B-l
-------
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
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CO
CO
CO
CO
CO
CO
CO
CO
CT
CT
CT
CT
CT
CT
CT-RI
DC-MD-VA-VW
Metropolitan ATM
San Franasco-Oakland-San Jose, CA
San Jose, CA
Santa Cruz-Watsonvilte, CA
Santa Rosa, CA
Vallejo-Fairfield-Napa. CA
San Luis Obispo-Atascadero-Paso Rotates, CA
Santa Barbara-Santa Mana-Lompoc, CA
Stodrton-Lodi, CA
Visalia-Tulara-PorterviNe. CA
Yuba City, CA
Colorado Springs, CO
Boulder-Longmont. CO
Denver, CO
Denver-Boulder-Greeley, CO
Greeley, CO
Fort Collms-Loveland, CO
Grand Junction, CO
Pueblo, CO
Hartford, CT
Bridgeport CT
Danbury, CT
New Haven-BridBeport-Stamford-Watertmry-Danbury, CT
Stamford-Norwalk, CT
Waterbury, CT
New London-Norwich, CT-RI
Washington, DC-MD-VA-WV
TYPE
CMSA
PMSA
PMSA
PMSA
PMSA
MSA
MSA
MSA
MSA
MSA
MSA
PMSA
PMSA
CMSA
PMSA
MSA
MSA
MSA
NECMA
PMSA
PMSA
NECMA
PMSA
PMSA
4ECMA
PMSA
Counties
San Matoo County
Alameda County
Contra Costa County
Marin County
San Francisco County
San Malao County
Santa Clara County <
Santa Cruz County
Sonoma County
Napa County
Solano County
Santa Clara County
Santa Cruz County
Sonoma County
Napa County
Solano County
San Luis Obispo County
Santa Barbara County
San Joaquin County
Tulare County
Suttar County
Yuba County
B Paso County
JoukJer County
Adams County
Arapahoe County
Denver County
Douglas County
Jefferson County
Boulder County
Adams County
Arapahoe County
)enver County
Douglas County
Jefferson County
Weld County
NeM County
.arirner County
riesa County
3ueblo County
Hartford County (pt.)
Lrtchfield County (pt.)
Middlesex County (pt.)
Tolland County (pt.)
F airfield County (pt.)
New Haven County (pt.)
F airfield County (pt.)
Lrtchfield County (pt.)
Middlesex County (pt.)
New Haven County (pt.)
F airfield County (pt.)
: airfield County (pt.)
Lrtchfield County (pt.)
New Haven County (pt.)
view London County, CT (pt.)
District of Columbia
Carvert 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
auquier County, VA
(ing George County, VA
oudoun County, VA
Pnnce William County, VA
1990
Population
6.249,881
1.497,577
229,734
388,222
451,186
217.162
369,608
480,628
311,921
122,643
397,014
225.339
1.622.980
1.980.140
131,821
186.136
93,145
123.051
1,123,678
1,631,864
1,001,737
1,631.864
827.645
978,311
254,957
4,223,485
1995 EsL
Population
6,539.602
1.565,253
236,669
414,569
481,885
226,071
381,401
523.969
346.843
136.104
465.800
253.850
1.831,308
2.233.172
148,014
217,215
106,548
129,758
1,115,223
1,625,513
250.404
4,509,932
1995 pop
(tensity
dun-*)
341.1
468.0
205.0
101.6
117.6
26.4
53.8
144.6
27.8
42.6
84.6
132.0
188.0
101.6
4.3
32.2
21.0
282.5
505.3
267.5
Ana (km1)
19173.7
3344.3
1154.6
4082.4
4097.5
8558.6
7092.6
3624.5
12495.0
3193.9
5508.1
1923.0
9740.6
21981.2
10341.3
6737.7
6187.0
3947.1
3190.0
4003.8
3216.8
1621.0
3951.8
16862.7
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
Sine
DC-MD-VA-WV
DE
DE-MD
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
FL
Metropolitan ATM
Washington-Baltimore, DC-MD-VA-VW
Dover, DE
Wilmington-Newark, DE-MD
Daytona Beach, FL
Fort Myers-Cape Coral, FL
Fort Pierce-Port SL Lucie, FL
Fort Walton Beach, FL
Gainesville, FL
Jacksonville. FL
Lakeland-Winter Haven. FL
Melboume-TKusviHe-Palm Bay, FL
Fort Lauderdate, FL
Miami. FL
Miami-Fort Lauderdale. FL
Naples, FL
Ocala, FL
Orlando, FL
Panama City, FL
Pensacola, FL
Punta Gorda, FL
Sarasota-Bradenton, FL
Tallahassee, FL
Tampa-St Petersburg-Clearwater, FL
TYPE
CMSA
MSA
PMSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
PMSA
PMSA
CMSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
Counties
Spotsyrvania County, VA
Stafford County. VA
Warren County, VA
Berkeley County. WV
Jefferson County, WV
Anne Arundel County
Baltimore County
Carroll County
Harford County
Howard County
Queen Anne's County
Washington County
District of Columbia
divert County
Chane* 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
Spotsyrvania County
Stafford County
Warren County
Berkeley County
Jefferson County
Kent County
New Castte County
Cecil County
Planter County
Volusia County
Lee County
Martin County
St. Lucie County
Okatoosa County
Alachua County
Clay County
Duval County
Nassau County
St Johns County
Polk County
Brevard County
Broward County
Dade County
Broward County
Dade County
Collier County
Marion County
Lake County
Orange County
Oscecla County
Seminole County
Bay County
Escambia County
Santa Rosa County
Chartotte County
Manatee County
Sarasota County
Gadsden County
Leon County
Hemando County
Hillsborough County
Pasco County
Pinedas County
1990
Population
6,726,395
110,993
513.293
399,413
335,113
251,071
143.776
181.596
906,727
405.382
398.978
1.255.488
1.937.094
3.192.725
152,099
194,833
1,224,852
126.994
344.406
110,975
489,483
233,598
2.067.959
1995 Est
Population
7,107,116
121,725
546,063
448.904
375,381
283.552
163.707
196.106
979.045
436,701
450.646
1,412.165
2.031.336
3,443.501
181,381
226,678
1,390,574
142,690
377.914
129.381
525,606
257.295
2.180.484
1995 pop
density
(tan-1)
286.5
79.6
272.2
108.9
180.4
97.0
67.5
66.6
143.4
89.9
170.8
451.0
403.4
34.6
55.4
153.8
72.1
86.9
72.0
154.6
84.0
329.6
ATM (km1)
24809.5
1529.8
2005.9
4120.4
2081.3
2921.9
2423.7
2264.4
6826.3
4856.1
2637.9
3131.0
5036.2
5245.9
4089.6
9041.6
1978.1
4349.8
1798.6
3400.6
3063.8
6616.1
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
FL
GA
GA
GA
GA
GA
GA-AL
GA-SC
Ml
IA
IA
IA
IA
IA
IA-IL
IA-NE
ID
IL
IL
IL
West Palm Beach-Boca Raton, FL
Albany, GA
Athens, GA
Atlanta, GA
Macon, GA
Savannah, GA
Columbus. GA-AL
Augusta-Aiken. GA-SC
Honolulu. HI
Cedar Rapids, IA
Des Momes. IA
Dubuque. IA
Iowa City, IA
Waterloo-Cedar Falls, IA
Davenport-Moline-Rock Island, IA-IL
Sioux City. IA-NE
Boise City. ID
Bloommgton-Normal, IL
Champaign-Urbana. IL
Chicago, IL
TYPE
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
PMSA
Counties
Palm Beach County
Dougherty County
Lee County
Clarke County
Madison County -
Ooonee County
Barrow County
Barlow County
Carroll County
Cherokee County
Clayton County
Cobb County
Coweta County
DeKato County
Douglas County
Fayette County
Forayth County
Futon County
Gwmnett County
Henry County
Newton County
PauWing County
Pickens County
Rockdale County
Spalding County
Walton County
Bibb County
Houston County
Jones County
Peach County
Twiggs County
Bryan County
Chatham County
Effingham County
Russell County, AL
Chattahoochee County. GA
Hams County, GA
Muscogee County, GA
Columbia County. GA
McDuffie County. GA
Richmond County. GA
Aiken County, SC
Edgefield County, SC
Honolulu County
Linn County
Dallas County
Polk County
Warren County
Dubuque County
Johnson County
Black Hawk County
Henry County, IL
Rock Island County, IL
Scott County, IA
Woodbury County, IA
Dakota County. NE
Ada County
Canyon County
McLean County
Champaign County
Cook County
DeKalb County
DuPage County
Gmndy County
Kane County
Kendall County
Lake County
McHenry County
Will County
1990
Population
863.518
112.571
126,262
2.959.950
290.909
258.060
260.860
415,220
836.231
168,767
392.928
86.403
96.119
123,798
350,861
115,018
295,851
129,180
173.025
7,410.858
1995 Est
Population
972,093
117,433
134.793
3,431,983
309,756
279.468
272.380
453,209
877,198
178,559
421,447
88,566
101,291
123.077
358,243
120,033
360.341
139.274
169.096
7,724,770
1995 pop
density
(ton-1)
184.5
66.1
88.1
216.3
78.1
79.2
67.0
524
564.3
96.1
94.2
56.2
63.6
83.8
81.0
40.8
84.6
45.4
65.5
588.9
Area (km1)
5268.9
1775.4
1530.8
15866.8
3967.9
3526.6
4066.3
8643.7
1554.5
1858.4
4474.7
1575.3
1591.7
14695
44236
2943.8
4260.0
3065.6
2582.7
13118.3
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
IL-IN-W!
IL
IL
IL
IL
IL
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN-KY
KS
KS
KS
KY
KY
KY-IN
Metropolitan Ar*a
Chfcago-Gary-Kenosha, IL-IN-WI
Kankakee, IL
Oecatur. IL
Peoria-Pekin, IL
Rockford, IL
Spnngfield. IL
Btoomington, IN
Gary, IN
Elkhart-Goshen, IN
Fort Wayne, IN
Indianapolis, IN
Kokomo, IN
Lafayette, IN
Munae, IN
South Bend, IN
Terra Haute. IN
Evansville-Hendereon. IN-KY
Lawrence, KS
Topeka. KS
Wichita, KS
Lexington, KY
Owensboro, KY
Louisville, KY-IN
TYPE
CMSA
PMSA
MSA
MSA
MSA
MSA
MSA
PMSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
Counties
DeKalb County
DuPage County
Grundy County
Kane County
Kendall County
Lake County
McHenry County
WU County
Lake County
Porter County
Kankakee County
Kenosha County
Kankakee County
Macon County
Peoria County
Tazewel County
Woodford County
Boone County
Ogle County
Wtanebago County
Menard County
Sangamon County
Monroe County
Lake County
Porter County
Elkhart County
Adams County
Aden County
DeKalb County
Huntmgton County
Wells County
Wnffley County
Boone County
Hamilton County
Hancock County
Hendricks County
Johnson County
Madison County
Marion County
Morgan County
Sheby County
Howard County
Tipton County
Clinton County
Tippecanoe County
Delaware County
St. Joseph County
Clay County
VermiHion County
Vigo County
Posey County, IN
Vanderfaurgh County, IN
Warm* County, IN
Henderson County. KY
Douglas County
Shawnee County
Butter County
Harvey County
Sedgwick County
Bourbon County
Clark County
Fayette County
Jessamine County
Madison County
Scott County
Woodford County
Davtess County
Clark County, IN
1990
Population
8,239,820
96.255
117,206
339,172
329,676
189,550
108,978
604.526
156.198
456,281
1,380,491
96.946
161,572
119,659
247.052
147.585
278,990
81,798
160,976
485,270
405,936
87.189
948.829
1995 ESL
Population
8,589,913
102.046
116.414
345,555
350,538
197.015
115.208
623.159
166.994
471,508
1.476,865
100.226
167,879
118,577
258,083
149,769
288,369
88,206
165,062
508.224
435,736
90,662
987,102
1995 pop
density
OtnV)
475.6
58.2
77.4
74.3
87.1
64.3
112.8
262.9
139.0
74.4
161.8
69.9
71.6
116.4
217.9
56.8
75.9
74.5
115.9
66.1
87.6
75.7
183.9
Area dun1)
18060.0
1754.7
1503.6
4653.0
4025.2
3062.8
1021.4
2370.5
1201.3
6339.5
9125.4
1433.5
2343.9
1018.7
1184.5
2636.3
3800.4
1183.5
1424.1
7686.7
4973.0
1197.7
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
LA
LA
LA
LA
LA
LA
LA
LA
MA
MA
MA
MA
MA
MA
MA-CT
MA-NH
MA-NH
MA-NH
MA-WI
MD
MD
MD-WV
Alexandria. LA
Baton Rouge, LA
Houma, LA
Lafayette, LA
Lake Charies. LA
Monroe, LA
New Orleans, LA
Shreveport-Bossier City, LA
Bamstable- Yarmouth, MA
Brockton, MA
Fitchburg-Leominster. MA
New Bedford. MA
Pittsfield. MA
Springfield. MA
Worcester, MA-CT
Boston-Worcester-Lawrence-Lowell-Brockton, MA-NH
Lawrence. MA-NH
Lowell, MA-NH
Duluth-Supenor, MN-WI
Baltimore, MD
Hagerstcwn, MD
Cumberland. MD-WV
TYPE
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
NECMA
PMSA
PMSA
PMSA
NECMA
NECMA
PMSA
NECMA
PMSA
PMSA
MSA
PMSA
PMSA
MSA
Floyd County, IN
Harrison County. IN
Scott County. IN
BuBtt County. KY
Jefferson County. KY
OWham County. KY
Ascension Parish
East Baton Rouge Parish
Livingston Parish
West Baton Rouge Parish
Lafourche Parish
Terrebonne Parish
Acadia Parish
Lafayette Parish
SL Landry Parish
St. Martin Parish
Cafcasieu Pansh
Ouachita Parish
Jefferson Parish
Orleans Parish
Plaquemines Parish
St. Bernard Parish
St. Charles Parish
St. James Parish
SL John the Baptist Parish
St. Tammany Pansh
Bossier Parish
Caddo Parish
Webster Parish
Bamstable County (pt.)
Bristol County (pt.)
Norfolk County (pt.)
Plymouth County (pt.)
Middlesex County (pL)
Worcester County (pt.)
Bristol County (pt.)
Plymouth County (pt.)
Berkshire County (pt.)
Hampden County (pt.)
Hampshire County (pt.)
Wlndham County. CT (pt.)
Hampden County, MA (pt.)
Worcester County. MA (pt.)
Bnstol County, MA (pt.)
Essex County. MA (pt.)
Middlesex County, MA (pt.)
Norfolk County, MA (pt )
Plymouth County, MA (pt.)
Suffolk County, MA
Worcester County, MA (pt.)
Rockmgham County. NH (pt.)
HiHsborough County, NH
Stratford County, NH
Essex County, MA (pt.)
Rockmgham County, NH (pt )
Middlesex County. MA (pt.)
HiHsborough County, NH (pt.)
St. Louis County, MN
Douglas County. Wl
Anne Arundel County
Baltimore County
Carroll County
Harford County
Howard County
Queen Anne's County
Washington County
AJksgany County, MD
1990
Population
131.556
528,264
182.842
344,953
168.134
142.191
1.285.270
376,330
186,605
1,557,688
2.108,173
941,601
139,352
602.878
1.268,540
5,685,763
915,925
1,734,541
239,971
2,382,172
121,393
101,643
1995 EsL
Population
127,167
563.994
188.757
365.857
175.868
146,826
1,315,294
379.778
199.804
135,743
592.587
5,768.968
2,469,985
127,189
101,275
1995 pop
density
OW)
37.1
137.3
31.1
54.5
63.4
92.8
149.4
63.3
194.9
56.3
199.6
345.7
365.5
107.2
51.9
Ana (km1)
3425.7
4109.1
6060.3
6717.9
2774.4
1582.5
8804.8
5999.7
1025.0
4186.0
6052.1
3151.1
2412.3
2988.1
6849.2
16689.9
3090.7
4403.1
19515.4
6758.1
1186.6
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
ME
ME
ME
Ml
Ml
Ml
Ml
Ml
Ml
Ml
Ml
Ml
Ml
MN-WI
MN
MN
MN-WI
MO
MO
MO
MO
MO-IL
Metropolitan Area
Banner, ME
Lewiiton-Aubum, ME
Portland. ME
Benton Harbor, Ml
Ann Arbor, Ml
Detroit. Ml
Detroit-Ann Arbor-Flint, Ml
Flint. Ml
Grand Rapids-Muskegon-Holland, Ml
Jackson, Ml
Kalamazoo-BaWe Creek, Ml
Lansing-East Lansing, Ml
Saginaw-Bay City-Midland, Ml
Duluth-Superior, MN-WI
Rochester, MN
St. Cloud. MN
Minneapolis-St. Paul, MN-WI
Columbia, MO
Joplm, MO
St. Joseph, MO
Springfield, MO
St. Louis. MO-IL
TYPE
NECMA
NECMA
NECMA
MSA
PMSA
PMSA
CMSA
PMSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
Counties
Mineral County. WV
Penobscot County (pt )
Androscoggin County (pL)
Cumberland County (pt)
Bemen County
Lenawee County
Livingston County
Washtenaw County
Lapeer County
Macomb County
Monroe County
Oakland County
StClair County
Wayne County
Lenawee County
Livingston County
Washtenaw County
LaPeer County
Macomb County
Monroe County
Oakland County
St. CMr County
Wayne County
Genesee County
Genesee County
ADegan County
Kent County
Muskegon County
Ottawa County
Jackson County
Cathoun County
Kalamazoo County
Van Buren County
Clinton County
Eaton County
Inflhsm County
Bay County
Midland County
Saginaw County
St. Louis County
Douglas County
Olmsted County
Benton County
Steams County
Anoka County. MN
Carver County. MN
Chisago County, MN
Dakota County. MN
Hennepin County, MN
Isand County. MN
Ramsey County, MN
Scott County. MN
Sherbume County. MN
Washington County. MN
Wright County, MN
Pierce County. Wl
St. Croix County. Wl
Boone County
Jasper County
Newton County
Andrew County
Buchanan County
Christian County
Greene County
Webster County
Clinton County. IL
Jersey County. IL
Madison County. IL
Monroe County. IL
1990
Population
146.601
105.259
243.135
161'.378
490.058
4,266.654
5,187.171
430,459
937,891
149,756
429.453
432.674
399.32r
239.971
106.470
148.976
2,538.834
112.379
134.910
97.715
264,346
2.511.698
1995 Est
Population
145.905
103,751
248.526
162.623
522,916
4,320.203
5.279.500
436.381
997.895
154.010
443.253
437.633
403.572
239.921
112.619
158,802
2,723.137
123.742
143.804
97.679
294,526
2.547.686
1995 pop
density
-------
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
MO-KS
MS
MS
MS
MT
MT
MY
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC-SC
ND
ND-MN
ND-MN
Metropolitan ATM
Kansas City, MO-KS
Bitoxj-Guttport-Pascagoula, MS
Hattiesburg, MS
Jackson, MS
Billings, MT
Great Falls, MT
Casper. WY
Asheville, NC
Fayetteville, NC
Goidsboro, NC
Greensboro-Wnston-Salem-High Point. NC
Greenville, NC
Hickory-Morganton-Lenoir, NC
Jacksonville. NC
Raleigh-Durham-Chapel Hill, NC
Rocky Mount. NC
Wilmington, NC
Chariotte-Gastonia-Rock Hill. NC-SC
Bismarck, ND
Fargo-Moornead. ND-MN
Grand Forks, ND-MN
TYPE
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
Counties
StClair County, IL
Franklin County, MO
Jefferson County, MO
Lincoln County, MO
St. Charles County. MO
St. Louis County. MO
Warren County. MO
Johnson County, KS
Leavenwortti County, KS
Miami County. KS
Wyandotte County. KS
Can County, MO
Clay County. MO
Clinton County. MO
Jackson County, MO
Lafayette County, MO
Plane County. MO
Ray County, MO
Hancock County
Harrison County
Jackson County
Forrest County
Lamar County
Hinds County
Madison County
Rankin County
Yellowstone County
Cascade County
Natrona County
Buncombe County
Madison County
Cumberland County
Wayne County
Alamance County
Davidson County
Davie County
Forsyth County
Guilford County
Randolph County
Stokes County
Yadkin County
Pitt County
Alexander County
Burke County
CaWwell County
Catawba County
Onstow County
Chatham County
Durham County
Franklin County
Johnston County
Orange County
Wake County
Edgecombe County
Nash County
Brunswick County
New Hanover County
Cabarrus County. NC
Gaston County. NC
Lincoln County, NC
Mecklenburg County, NC
Rowan County, NC
Union County. NC
York County, SC
Burieigh County
Morton County
day County
Cass County
Polk County
1MO
Population
1,582.875
312.368
98.738
395,396
113.419
77.691
61.226
191,774
274,566
104,666
1.050.304
107,924
292,409
149,838
855.545
133.235
171,269
1.162,140
83.831
153,296
103.181
1995 ESL
Population
1,663.453
341.548
106.195
416.297
124,655
81,091
64,025
207,448
285.869
110.174
1.123,840
117,740
310,236
143.324
995.256
141.932
200,610
1.289,177
89.440
163,618
104.571
1995 pop
density
(km-1)
118.8
73.9
68.0
18.3
11.6
4.6
72.4
169.0
77.0
111.8
69.8
73.1
72.2
110.1
524
389.3
147.3
9.7
22.5
11.8
Area (km1)
14003.3
4622.0
6120.2
6825.2
6987.9
13830.8
2863.8
1691.6
1431.2
10056.6
1687.7
4244.3
1986.2
9041.7
2707.6
515.3
8750.5
9219.4
7280.6
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
Stale
NE
NE-IA
NH
NH
NH-ME
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NJ
NM
NM
NM
NV
NV-AZ
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY-NJ-CT-PA
Metropolitan Area
Lincoln. NE
Omaha, NE-IA
Manchester, NH
Nashua, NH
Portsmouth-Rochester, NH-ME
Bergen-Passaic. NJ
Jersey City. NJ
Middlesex-Someraet-Hunterdon. NJ
Monmouth-Ocean, NJ
Newark, NJ
Trenton. NJ
AUantic-Cape May, NJ
Vineland-Millville-Bridgeton, NJ
Albuquerque, NM
Las Cruces, NM
Santa Fe. NM
Reno, NV
Las Vegas, NV-AZ
Albany-Schenectady-Troy, NY
Bmghamton, NY
Buffalo-Niagara Falls, NY
Elmira, NY
Glens Falls. NY
Jamestown. NY
Dutchess County. NY
Nassau-Suffolk. NY
New York. NY
New York-No. New Jersey-Long Island. NY-NJ-CT-PA
TYPE
MSA
MSA
PMSA
PMSA
PMSA
PMSA
PMSA
PMSA
PMSA
PMSA
PMSA
PMSA
PMSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
PMSA
PMSA
PMSA
CMSA
Counties
Grand Forks County
Lancaster County
Pottawattamie County, IA
Cess County, NE
Douglas County, NE
Saipy County, NE ~
Washington County, NE .
HWsborough County (pt)
MenTmack County (pt.)
Rocklngham County (pi.)
HBsborough County
York County, ME (pt)
Rockingham County, NH (pt.)
Stafford County, NH (pt.)
Bergen County
Passalc County
Hudson County
Hunterdon County
Middlesex County
Somerset County
Monmouth County
Ocean County
Essex County
Morris County
Sussex County
Union County
Warren County
Mercer County
Atlantic County
Cape May County
Cumberland County
Bernalilto County
Sandoval County
Valencia County
Dona Ana County
Los Alamos County
Santa Fe County
Washoe County
Mohave County
Clark County
Nye County
Albany County
Montgomery County
Rensselaer County
Saratoga County
Schenectady County
Schohane County
Broome County
Tnga County
Erie County
Niagara County
Chemung County
Warren County
Washington County
Chautauqua County
Dutchess County
Nassau County
Suffolk County
Bronx County
Kings County
New York County
Putnam County
Queens County
Richmond County
Rockland County
Westchester County
Bergen County
Passaic County
Fairfield County
1890
Population
213.641
639,580
701,923
581,918
514,665
1.278.440
553.099
1,019,835
986,327
1,915,928
325.824
319,416
138,053
589,131
135.510
117.043
254.667
852.737
861.623
264.497
1.189.288
95,195
118.539
141.895
259.462
1,321,864
8,546,846
17,830,586
19*5 ESL
Population
228,638
670,322
1.308,655
550,183
1.080.450
1,050,052
1.936,096
330.305
332.336
138,058
659,855
158.849
135,018
290.833
1,138,758
873,361
257.403
1.184.052
94,082
122.559
141,677
262.062
2.659.476
8,570.212
18,107,235
1995 pop
density
(km"1)
105.2
104.5
1205.2
4553.2
399.0
365.8
473.8
564.4
157.2
108.9
42.9
16.1
25.8
177
11.2
104.6
81.1
2916
89.0
27.7
51.5
126.2
1126.9
2883.4
779.7
Area (km*)
2172.7
6412.4
6491.2
4070.7
5322.6
1085.9
120.8
2707.7
2870.3
4086.5
585.2
2114.4
1267.3
15393.6
9861.3
5228.5
16426.9
101969.1
8346.3
3174.3
4060.2
1057.2
4418.6
2751.0
2076.3
2360.1
2972.3
23221.9
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
NY
NY
NY
NY-PA
OH
OH
OH
OH-KY-IN
OH
OH
Metropolitan Area
Rochester, NY
Syracuse. NY
Utica-Rome. NY
Newburgh, NY-PA
Canton-Massillon. OH
HamiNon-Middletown, OH
Akron, OH
Cincinnati-Hamilton, OH-KY-IN
Cleveland-Akron, OH
Cleveland-Loram-Elyna, OH
TYPE
MSA
MSA
MSA
PMSA
MSA
PMSA
PMSA
CMSA
CMSA
PMSA
Counties
New Haven County
LtehfiekJ 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
Genesee County
Livingston County
Monroe County
Ontario County
Orleans County
Wayne County
Cayuga County
Madison County
Onondaga County
Oswego County
Herkimer County
Oneida County
Orange County. NY
Pike County. PA
Carroll County
Stark County
Butter County
Portage County
Summit County
Dearborn County
Ohio County
Boone County
Campbell County
Gallatin County
Grant County
Kenton County
Pendleton County
Brown County
Qermont County
Hamilton County
Warren County
Butler County
Portage County
Summit County
Asntabula Caounty
Cuyahoga County
Geauga County
Lake County
Lorain County
Medina County
Asntabula County
1990
Population
1,062.470
742,177
316,633
335.613
394,106
291,479
657,575
1.817,569
2.859,644
2.202.069
1995 ESL
Population
1,088,516
750.090
308,562
359,744
403.695
315,601
678.834
1.907,438
2.903,808
2,224.974
1995 pop
density
(km")
122.7
93.9
45.4
101.9
160.5
260.8
289.5
192.4
309.5
317.3
Area (km1)
8872.5
7984.5
6797.7
3531.3
2514.5
1210.3
2344.5
9914.5
9383.5
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
OH
OH
OH
OH
OH
OH
OH-KY-IN
OH-VW
OK
OK
OK
OK
OR
OR
OR
OR-WA
OR-WA
Metropolitan Area
•
Columbus. OH
Dayton-Springfield. OH
Lima, OH
Mansfield. OH
Toledo. OH
Youngstown-Warren. OH
Cincinnati, OH-KY-IN
Steubenville-Wemon, OH-WV
Enid, OK
Lawton, OK
Oklahoma City, OK
Tulsa, OK
Eugene-Spnngfield, OR
Medford-Ashland, OR
Salem, OR
PorUand-Satem, OR-WA
Portland-Vancouver. OR-WA
TYPE
MSA
MSA
MSA
MSA
MSA
MSA
PMSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
PMSA
CMSA
PMSA
Counties
Cuyahoga County
Qeauga County
Lake County
Lorain County
Medina County
Delaware County
FairfiekJ County
Franklin County
Licking County
Madison County
Pfckaway County
Clark County
Greene County
Miami County
Montgomery County
AVen County
Augtaize County
Crawford County
Richland County
Fulton County
Lucas County
Wood County
Columbiana County
Mahoning County
Trumbull County
Dearborn County. IN
Ohio County, IN
Boone County. KY
Campbell County, KY
GaHatan County. KY
Grant County, KY
Kenton County, KY
Pendleton County, KY
Brown County, OH
Clermont County. OH
Hamilton County. OH
Warrui County, OH
Jefferson County, OH
Brooke County, WV
Hancock County. WV
Garfiekt County
Coman che County
Canadian County
Cleveland County
Logan County
McClain County
Oklahoma County
Pottawatomie County
Creek County
Osage County
Rogers County
Tulsa County
Wagoner County
Lane County
Jackson County
Marion County
Polk County
Clackamas County
Columbia County
Muttnomah County
Washington County
Yamhill County
Clark County
Manon County
Polk County
Clackamas County
Columbia County
Muttnomah County
Washington County
1MO
Population
1,345.450
951.270
154,340
174,007
614.128
600,895
1,526.092
142,523
56,735
111.486
958.839
708,954
282.912
146.389
278,024
1,793,476
1,515.452
1995 EsL
Population
1,437.512
956,412
156,276
176.154
612,798
602.608
1.591,837
139,862
57,330
115,672
1.015,174
746,500
303.426
166,080
311.722
2.021.982
1,710,260
1995 pop
density
dun"1)
176.6
219.3
74.9
75.6
173.4
148.8
183.9
92.9
20.9
41.8
92.3
57.5
25.7
23.0
62.5
112.4
131.3
Area (km*)
8138.2
4360.7
2086.8
2329.4
3534.3
4049.8
8656.5
1506.2
2741.5
2769.6
11000.8
12988.7
11795.3
7214.1
4988.5
17984.9
13021.6
B-ll
-------
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
PA
PA
PA
PA
PA
PA
PA-NJ-DE-MD
PA
PA
PA
PA
PA
PA
PA
PA-NJ
RI-MA
SC
SC
SC
SC
Metropolitan Area
Allentown-BethlehenvEaston. PA
Altoona. PA
Ene. PA
Harrisburg-Lebanon-Cariisle. PA
Johnstown, PA
Lancaster. PA
Philadelphia-Wilmington-Atlantic City. PA-NJ-DE-MD
Pittsburgh, PA
Reading, PA
Scranton-Wilkes-Barre-Hazleton, PA
Sharon, PA
State College, PA
Williamsport, PA
York, PA
Philadelphia, PA-NJ
Providence-Warmck-Pawtucket, RI-MA
Charleston-North Charleston, SC
Columbia, SC
Florence, SC
Greenville-Spartanburg-Anderson. SC
TYPE
MSA
MSA
MSA
MSA
MSA
MSA
CMSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
PMSA
NECMA
MSA
MSA
MSA
MSA
Counties
YemhiH County
dark County
Carbon County
Lehigh County
Northampton County
3lair County
Erie County
Cumberland County
Dauphin County
Lebanon County
Perry County
Cambria County
Somerset County
Lancaster County
Atlantic County
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
Allegheny County
Beaver County
Butler County
Fayette County
Washington County
Westmoreland County
Berks County
Columbia County
Lackawanna County
Luzeme County
Wyoming County
Mercer County
Centre County
Lycoming County
York County
Burlington County, NJ
Camden County, NJ
Gloucester County, NJ
Salem County. NJ
Bucks County, PA
Chester County. PA
Delaware County, PA
Montgomery County. PA
Philadelphia County. PA
Bristol County. MA (pt.)
Bristol County. Rl
Kent County, Rl
Newport County. Rl (pt.)
Providence County, Rl
Washington County. Rl (pt.)
Berkeley County
Charleston County
Dorchester County
Lexington County
Richland County
Florence County
Anderson County
Cherokee County
Greenville County
Pickens County
Spartanburg County
1990
Population
595.208
130.542
275,572
587,986
241,247
422.822
5.893,019
2.394,811
336,523
638,466
121.003
123,786
118.710
339.574
4,922.257
916,270
506,875
453,331
114,344
830,563
1995 EsL
Population
613.466
131,647
280,460
612.617
240,644
447,521
5,967,323
2,394,702
349.583
635.559
122.254
131,968
120,194
362,793
4.950.866
907.801
506.420
481.718
122.769
884.306
1995 pop
density
OW1)
214.7
96.7
135.0
118.8
52.7
182.0
386.9
200.0
157.1
109.9
70.3
46.0
37.6
.154.8
370.1
75.4
127.6
59.3
106.3
Arm (km*)
2857.0
1362.0
2077.2
. 5156.4
4565.8
2458.2
15423.4
11975.9
2225.4
57B2.3
17401
2868.7
3198.5
2343.0
2452.7
6712.7
3774.5
2070.0
8315.8
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
SC
sc
SD
SO
TN
TN
TN
TN-AR-MS
TN-GA
TN-KY
TN-VA
TX
TX
TX
TX
TX
TX
TX
TX
TX
Metropolitan Am
Myrtle Beach, SC
Sumter. SC
Rapid City, SO
Sioux Falls. SD
Jackson, TN
Knoxville, TN
Nashville, TN
Memphis, TN-AR-MS
Chattanooga, TN-GA
Clarksville-Hopkinsville. TN-KY
Johnson City-Kingsport-Biistol, TN-VA
Abilene. TX
Amarilto. TX
Austin-San Marcos, TX
Beaumont-Port Arthur, TX
Brownsville-HarlingervSan Benito. TX
Bryan-College Station. TX
Corpus Christ], TX
Dallas, TX
Dallas-Fort Worth, TX
TYPE
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
PMSA
CMSA
Counties
Horry County
Sumter County
Pennington County
Lincoln County
Minnehaha County
Cheater County
Madison County
Anderson County
Btount County
Knox County
Louden County
Sevier County
Union County
Cheathem County
Davidson County
Dickson County
Robertson County
Rutherford County
Sumner County
VWNamson County
Wilson County
Crittenden County, AR
DeSoto County. MS
Fayette County, TN
Shelby County, TN
Tipton County, TN
Catoosa County, GA
Dade County, GA
Walker County, GA
Hamilton County, TN
Manon County, TN
Christian County. KY
Montgomery County, TN
Carter County, TN
Hawkins County, TN
Sullivan County, TN
Unicoi County, TN
Washington County, TN
Scott County, VA
Washington County. VA
Taylor County
Potter County
Randall County
Bastrop County
Caktwell County
Hays County
Travis County
Williamson County
Hardin County
Jefferson County
Orange County
Cameron County
Brazos County
Nueces County
San Patriot) County
Collin County
Dallas County
Denton County
EUis County
Henderson County
Hunt County
Kaufman County
Rockwall County
Collin County
Dallas County
Denton County
EUis County
Henderson County
Hunt County
1990
Population
144.053
102.637
81,343
139.236
77,982
585,960
985.026
1,007.306
424,347
169,439
436.047
119.655
187.514
807.964
361.226
260.120
121 .862
349.894
2,676,248
4,037,282
1995 EsL
Population
157,902
106,823
87.304
153.307
83,715
640.700
1.093.836
1,068.891
443.060
189.477
454,056
122.791
201.012
999.936
374.637
309.578
130.486
378.936
2.957.910
4,449,875
1995 pop
density
(km"1)
53.8
62.0
12.1
42.7
58.0
101.0
103.7
137.2
93.7
58.0
61.2
51.8
42.6
1157
67.1
132.0
86.0
95.8
184.6
277.7
Area (km*)
2936.3
1723.5
7190.8
3593.2
1442.9
6343.2
10549.2
7789.6
4726.2
3264.8
7421.8
2371.7
4723.9
8644.1
5579.9
2345.4
1517.3
3956.6
16023.3
16023.3
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
Stile
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX
TX-AR
UT
UT
VA
VA
VA
VA
VA
VA-NC
Fort Worth-Arlington. TX
El Paso. TX
Brazona, TX
Galveston-Texas City, TX
Houston. TX
Houston-Galveston-Brazoria, TX
Killeen-Temple, TX
Laredo. TX
Longview-Marshall. TX
Lubbock. TX
McAHen-Edinburg-Mission. TX
Odessa-Midland, TX
San Angelo. TX
San Antonio, TX
Sherman-Denlson, TX
Tyler, TX
Victoria, TX
Waco, TX
Wichita Falls, TX
Texarkana,TX-Texaricana, AR
Provo-Orem. UT
Salt Lake City-Ogden. UT
Chariottesville, VA
Danville, VA
Lynchburg, VA
Richmond-Petersburg, VA
Roanoke. VA
Norfolk-Virginia Beach-Newport News, VA-NC
TYPE
PMSA
MSA
PMSA
PMSA
PMSA
CMSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
Counties
Kaufman County
Rodman County
Hood County
Johnson County •
Parker County
Tarrant County
B Paso County
Brazona County
Garveston County
Chambers County
Fort Bend County
Harris County
Liberty County
Montgomery County
Water County
Brazoria County
Garvetton County
Chambers County
Fort Bend County
Harris County
Liberty County
Montgomery County
Walter County
Bell County
Coryetl County
Webb County
Gregg County
Harrison County
Upshur County
Lubbock County
Hidalgo County
Ector County
Midland County
Tom Green County
Bexar County
Corral County
Guadalupe County
Wilson County
Grayson County
Smith County
Victoria County
McLennan County
Archer County
Wtehita County
Miller County. AR
Bowie County, TX
Utah County
Davis County
Salt Lake County
Weber County
Albemarie County
Fluvanna County
Greene County
Ptttsyrvania County
Amherst County
Bedford County
Campbell County
Charles City County
Chesterfield County
Dinwiddie County
Goochland County
Hanover County
Henrico County
New Kent County
Powhatan County
Prince George County
Botetourt County
Roanoke County
Currituck County, NC
1990
Population
1,361.034
581.610
191.707
217,399
3.322.025
3,731,029
255.301
133.239
193.801
222.636
383,545
225,545
98,458
1.324.749
95.021
151,309
74,361
189.123
130,351
120.132
263,590
1.072,227
131.107
108,711
193.928
865.640
224,477
1,443,244
1995 EsL
Population
1.491.965
678.313
216,016
237.533
3,710.844
4.164,393
289,903
170.863
203,949
232.276
479,783
239,245
101,555
1.460,809
98,336
161,986
79.992
200,111
133.386
122,991
298,789
1,199,323
142,148
109.890
204,212
927,435
228,805
1,540,446
1995 pop
density
(km-1)
197.4
258.5
60.1
230.0
242.0
207.0
53.0
19.7
44.7
99.7
118.1
51.3
25.8
169.5
40.7
67.4
35.0
74.2
33.5
31.4
57.7
286.3
46.6
41 8
44.0
121.6
103.9
253.2
Area (km1)
7557.9
2623.9
3592.0
1032.6
15335.8
20116.5
5467.1
8694.6
4560.0
2330.0
4063.9
4665.8
3942.5
8616.4
2418.2
2404.8
2285.9
2698.6
3982.0
3916.1
5175.9
4189.3
3048.6
2626.0
4638.3
7626.9
2203.6
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
VT
WA
WA
WA
WA
WA
WA
WA
WA
WA
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Wl
Wl
WI-MN
WV
WV-KY-OH
WV-OH
WV-OH
WY
Metropolitan Area
Burlington, VT
BeDingham, WA
Richland-Kennewick-Pasco. WA
Bremerton, WA
Orvmpia. WA
Seattte-Bellevue-Everett, WA
Seattte-Tacoma-Bremerton. WA
Tacoma, WA
Spokane, WA
Yakima, WA
Appteton-Oshkosh-Neenah, Wl
Kenosha, Wl
Eau Clare, Wl
Green Bay, Wi
Janesville-Betoit. Wl
Madison, Wl
Milwaukee-Waukesha, Wl
Milwaukee-Raane, Wl
Raane. Wl
Sheboygan. Wl
Wausau. Wl
La Crosse, WI-MN
Charleston, WV
Huntmgton-Ashland. WV-KY-OH
Parkersburg-Manetta. WV-OH
Wheeling, WV-OH
Cheyenne. WY
TYPE
NECMA
MSA
MSA
PMSA
PMSA
PMSA
CMSA
PMSA
MSA
MSA
MSA
PMSA
MSA
MSA
MSA
MSA
PMSA
CMSA
PMSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
Counties
Gloucester County. VA
Isle of Wight County, VA
Jamas City County. VA
Mathews County. VA
York County, VA
CNttanden County (pt)
Franklin County (pt.)
Grand Isle County (pt.)
\nAi8tcorTi County
Benton County
Franklin County
KKsap County
Thuraton County
Island County
King County
SnonoffliSn County
Kitsap County
Thurston County
Island County
King County
Snohomish County
Pierce County
Pierce County
Spokane County
Yakima County
Calumet County
Outagamie County
Wfcwebago County
Kenosha County
Cnippewa County
Eau Claire County
Bnmn County
Rock County
Dane County
Milwaukee County
Ozaukee County
Washington County
Waukesha County
Milwaukee County
Ozaukee County
Washington County
Waukesha County
Racine County
Racine County
Sheboygan County
Marathon County
Houston County, MN
La Crosse County, Wl
Kanawha County
Putnam County
Boyd County. KY
Carter County, KY
Greenup County, KY
Lawrence County, OH
Cabell County, WV
Wayne County, WV
Washington County, OH
Wood County, WV
Belmont County, OH
Marshall County, WV
Ohio County, WV
Laramie County
1990
Population
177,059
127.780
150.033
189.731
161.238
2.033,156
2,970,300
586,203
361,364
188.823
336,073
128.181
137,543
194,594
139,510
367,085
1,432,149
1,607,183
175,034
103.877
115,400
116,401
250,454
312,529
149,169
159.301
73,142
1995 ESL
Population
188,175
148.92S
177,529
226,720
191,974
2.197,451
3.265,139
648.994
401.205
212,035
336,073
139,938
142,663
210,303
148.349
393.296
1,457.939
1.640.831
182.892
108.326
120.776
121,005
255,139
317,489
152,131
157,349
78.444
1995 pop
density
dm,'')
- 57.7
27.1
23.3
221.1
101.9
191.7
174.3
149.5
87.8
19.1
92.8
198.0
33.4
153.6
79.5
126.3
385.6
352.9
212.0
81.4
30.2
46.2
78.8
56.8
58.6
63.9
11.3
Area (km1)
3259.9
5490.9
7628.3
1025.6
1883.1
11460.5
18733.4
4339.7
4568.3
11126.9
3623.1
706.6
4268.7
1369.4
1866.2
3113.6
3781.3
4649.0
662.8
1330.4
4001.7
2619.1
3236.0
5594.1
2596.7
2461.8
6957.4
B-15
-------
TECHNICAL REPORT DATA
(PLEASE READ INSTRUCTIONS ON THE REVERSE BEFORE COMPLETING)
1. REPORT NO.
EPA-454/R-99-022
3. RECIPIENTS ACCESSION NO.
4. TITLE AND SUBTITLE
GUIDANCE FOR NETWORK DESIGN AND OPTIMUM
SITE EXPOSURE FOR PM 2.5 AND PM 10
5. REPORT DATE
12/15/97
6. PERFORMING ORGANIZATION CODE
EPA/OAQPS/EMAD/MQAG
7. AUTHOR(S)
NEIL FRANK, MARC PITCHFORD, JOHN WATSON, JUDITH CHOW,
DAVID DUBOIS AND MARK GREEN
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
CX824291-01-1
12. SPONSORING AGENCY NAME AND ADDRESS
U. S. EPA
MONITORING AND QUALITY ASSURANCE GROUP, MD-14
RTP.NC 27711
13. TYPE OF REPORT AND PERIOD COVERED
14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
16. 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 aras
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 metroplitan 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.
17.
KEY WORDS AND DOCUMENT ANALYSIS
b. IDENTIFIERS/OPEN ENDED TERMS
i. DESCRIPTORS
PM 2.5, National Ambient Air Quality Standards, Monitors,
Monitoring Network, Network Design,
c. COSATI FIELD/GROUP
18. DISTRIBUTION STATEMENT
RELEASE UNLIMITED
21. NO. OF PAGES
22. PRICE
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