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

-------
•   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

-------
       •   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
                                        2-14

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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
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20.8039
41.9583
41.8925
40.5369
43.1444
37.2178
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46.7614
38.895C
30.7402
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112.1667
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122.1333
113.5622
116.8478
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113.9956
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114.2158
111.8300
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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


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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.
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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
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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
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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,
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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
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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
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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
                                        2-25

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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

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          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
                                        3-2

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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.

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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.

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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.

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 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

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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.

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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

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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

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       •   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

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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

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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

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       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

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   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

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   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

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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

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          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.
                                        4-8

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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.
                                        4-9

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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.
                                        4-10

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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.
                                        6-3

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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

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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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7.0   REFERENCES

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Andricevic, R.  (1990). Cost-Effective Network Design for Groundwater Flow Monitoring.
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Borgman, L.E., K. Gerow, and G.T. Flatman. (1996).  Cost-Effective Sampling for Spatially
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Bowne, N.E., and RJ. Lundergan (1983). Overview, Results, and Conclusions for the EPRI
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Braaten, D.A., and T.A. Cahill (1986).  Size and Composition of Asian Dust Transported to
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Chow, J.C., D. Fairley, J.G. Watson, R. De Mandel, E.M. Fujita,  D.H. Lowenthal, Z.  Lu,
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                                       7-1

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 Chow, J.C., and J.G. Watson (1997). Imperial Valley/Mexicali Cross Border PMio Transport
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Green, H.R. (1979). Sampling Design  and Statistical Methods for Environmental Biologists.
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Larsen, R.I. (1969).  A New Mathematical  Model of Air Pollutant Concentration Averaging
       Time and Frequency. J. Air Poll. Control AssocJ9:24-30.

Lefohn, A.S., H.P. Knudsen, J.A. Logan, J.  Simpson, C. Bhumralkan (1987).  An Evaluation
       of the Kriging Method to  Predict  7-h Seasonal Mean Ozone Concentrations for
       Estimating Crop Losses. J.  Air Poll. Control Assoc37(5):595-602.
                                       7-3

-------
Lowenthal, D.H., J.C. Chow, J.G. Watson, W.A. Dipple, and D.M. Mazzera (1996).  PMio
       Source Apportionment at McMurdo Station, Antarctica.   Environ. Manager, June
       1996, pp. 28-30.

Meyer, M., J. Lijek, and D. Ono (1992). Continuous PMio Measurements in a Woodsmoke
       Environment.  In Transactions: PMw Standards  and  Nontraditional Particulate
       Source Controls, J.C. Chow and D.M. Ono, Eds. Air & Waste Management Assoc.,
       Pittsburgh, PA, pp. 24-39.

Meyer, P.D., A.J. Valocchi, and J.W. Eheart (1994).  Monitoring Network Design to Provide
       Initial Detection of Groundwater Contamination. Water Res. Bull 30(9):2647-2659.

Murai, R.E. (1975). Suspended  Particulate Concentrations: Spatial Correlations in the
       Detroit-Windsor Area. Tellus, 27:397-405.

Munn, R.E.  (1981).  The  Design of Air Quality Monitoring Networks. London:  Macmillan
       Ltd.

Nesbitt, K.J.,  and  K.R. Carter (1996).  Immunoassay Field Analytical Techniques.   In
       Principles of Environmental Sampling, 2nd ed., L.H. Keith, EdXewis Publishers, pp.
       727-735.

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      Arbor Science.

Pellizzari, E.D., K.W. Thomas, C.A. Clayton, R.W. Whitmore, R.C. Shores, H.S. Zelon, and
      R.L.  Perritt (1993).  Particle Total Exposure Assessment  Methodology (PTEAM):
      Riverside,  California  Pilot  Study.     Report  No.EPA/600/SR-93/050.     U.S.
      Environmental Protection Agency, Research Triangle Park, NC.

Peterson, J.T.  (1970).  Distribution of SO2 over Metropolitan St. Louis as Described by
      Empirical Eigenvectors  and Its Relation to  Meteorological  Parameters.   Atmos.
      Environ.,  4:501-518.

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       American  Petroleum Institute, Washington, DC, by Atmospheric & Environmental
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       Urban Air Pollution Surveys, Part HI: Two- and Four-Hour Soiling Index. J.  Air Poll.
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       Requirements for Urban  Air  Pollution Surveys, Part IV: 2- and  24-Hour Sulfur
       Dioxide and Summary of Other Pollutants. J.  Air Poll. Control AssocJ2:361-375.

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                                        7-5

-------
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       J. Derby (1996).  Effectiveness Demonstration of Fugitive Dust Control Methods for
       Public Unpaved Roads and Unpaved Shoulders on Paved Roads. DRI Document No.
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       NV.

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       Multiple Criteria Decision Making and Geostatistics. Water Res. Bull., 28(l):45-62.
                                       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

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                                  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

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                                       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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