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EPA-450/4-87-009
May 1987
Network Design and
Optimum Site Exposure Criteria
for Particulate Matter
by
R. C. Koch and H. E. Rector
GEOMET Technologies, Inc.
1801 Research Boulevard
Rockville, Maryland 20850
Contract Number 68-02-3584
Project Officer
David Lutz
U.S. Environmental Protection Agency
Research Triangle Park NC 27711
U. S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park NC 27711
.
Chicago,'IL V..-j4d| i2"lflo°'
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DISCLAIMER
The development of this document has been funded by the United States
Environmental Protection Agency under contract 68-02-3584. It has been
subject to the Agency's peer and administrative review, and it has been
approved for publication as an EPA document.
Mention of trade names or commercial products does not constitute
endorsement or recommendation for use.
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CONTENTS
Figures iv
Tables vi
1. Introduction 1
2. Characteristics of PMio 3
General Principles 3
Instrumentation 6
Relationship of PMiQ to TSP and Other Measures
of Particulate Matter 8
Spatial/Temporal Patterns 9
3. Monitoring Objectives for PMio 16
Evaluation of Ambient Air Quality 18
Enforcement of Source-Specific Regulations 18
Evaluation/Development of Control Plans 19
Air Quality Maintenance Planning 19
Protection of Public Health 20
Development & Testing of Models 20
Research 20
4. Elements of Site Selection 22
Representative Scales 22
Analysis of the Area to Be Monitored 24
Taxonomy of Representative Sites 41
5. Site Selection Methodology 44
Overview of Methodology 44
Analyze Existing Ambient PM Monitoring Data .... 47
Review of Local Situation 49
Selection of Monitoring Sites 84
Installation and Followup 87
6. Example Study 89
7. References 101
Aooendix
A. Meteorological data tabulations for COM program 106
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FIGURES
Number Page
1 Average volume distribution for seven sites in the
California ACHEX study in 1972 4
2 Idealized time and coarse particle mass and chemical
composition 5
3 Distribution of annual mean IP/TSP ratios 10
4 Distribution of 24-hour IP/TSP ratios 11
5 Seasonal variations in urban, suburban, and rural areas
for four size ranges of particles 12
6 Complex processes affecting transport and
transformation of airborne particulate matter 15
7 Illustration of various spatial scales
of representativeness 23
8 Characteristics of lake coast air flow 33
9 Hourly positions of lake breeze front of August 13,
1967, with the ground track of the NACR Queen air
plotted 34
10 Flow zones around a building 35
11 Flow characteristics among multiple buildings 36
12 Idealized urban heat island air flow 36
13 Annual wind roses for U.S. locations 39
14 Planning cycle for monitoring 45
15 Procedure for selecting PM^g monitoring sites 46
16 Twelve-month running geometric means 50
17 TSP roses for four sites near a coking plant 52
18 Steps for locating regional scale monitoring site .... 60
19 Steps for locating a neighborhood scale monitoring
site in an urban area 61
20 Steps for locating micro-/middle scale monitoring
sites in urban areas 62
21 Steps for locating monitoring sites near isolated
major sources 63
22 Average measured daytime PM concentrations from a
major Philadelphia highway 66
23 Example of background site selection within 24 km
of City A 68
24 Concentration as a function of wind speed, computed
using HIWAY2 model 70
25 Concentration as a function of wind direction, computed
using HIWAY2 model 71
26 Concentration as a function of stability class, computed
using HIWAY2 model 72
27 Downwind distance to maximum concentration and maximum
relative concentration as a function of Pasquill
stability class and effective plume height in rural
terrain 78
IV
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FIGURES '(Concluded)
Number Page
28 Downwind distance to maximum concentration and maximum
xu/Q as a function of stability class and effective
plume height in urban terrain 79
29 TSP monitoring sites in Baltimore City 91
30 TSP monitoring sites in the Baltimore AQCR, excluding
Baltimore City 92
31 Annual mean TSP concentration for 1980 93
32 Annual mean TSP concentration for 1981 94
33 Maximum 24-hour TSP concentration for 1980 96
34 Maximum 24-hour TSP concentration for 1981 97
35 Wind persistence rose for Baltimore-Washington
International Airport for 1973-1977 99
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TABLES
Number Page
1 National Estimates of Particulate Emissions 6
2 Summary of National 1979 Particulate Matter Emissions
by Source Category 13
3 Principal Data Uses for PMio 17
4 Identification and Classification of Land Use Types ... 26
5 Estimates of the PMio Fraction of Total Particulate
Matter Emissions for Selected Categories of
Process Emissions 28
6 Estimates of the PMio Fraction of Total Particulate
Matter Emissions for Selected Categories of
Uncontrolled Process Fugitive Emissions and
Uncontrolled Open Sources 30
7 Estimates of the St. Louis, Missouri, Heat Island
Circulation 37
8 Dispersion Classifications 40
9 TSP Data for SAROAD Station #391720001 51
10 Available EPA Models for Six Monitoring Situations ... 58
11 Distances from Major Point that Affect Regional
Scale Monitors 65
12 Maximum Concentrations Near Downwind Edge of
Typical Urban Area Sources 73
13 Example Determination of the Number of Neighborhood
Monitoring Sites 74
14 Significant Impact Distances of Small Ground-Level
Area Sources 75
15 Significant Impact Distances of Highways 75
16 Significant Impact Distances of Elevated Sources .... 76
17 Sample Work Table for Overlap 83
18 Hi-Vol Measurements of TSP in the Vicinity of Baltimore . 90
19 TSP Emissions by Eight Largest Point Sources in
Baltimore City 95
20 Fugitive Emissions Based on 1977 Survey 95
vi
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SECTION 1
INTRODUCTION
The primary purpose of this document is to assist in planning a network
of monitoring sites for measuring particulate matter. The measurements will
conform to the new PM^g standard, which replaces the former TSP standard.1
As a secondary objective, this document will aid in understanding the
relationship between PM^g measurements and the quality of air that is
sampled. The information contained here will prove useful to both air
quality surveillance personnel and the users of air quality monitoring data.
In this document, the siting process is viewed dynamically.
Information received from monitoring sites can be used to feed back
into the siting process in order to improve the site selections. The
information can also be used to improve air quality simulation models or
other analytical tools used in the siting process; however, the process of
improving air quality models is not covered in this report.
Monitoring -is undertaken to collect needed data. In planning a moni-
toring network, these data needs must be well defined and understood. This
document provides suggestions for helping to identify what these data needs
may be. The data needs may change with time as the monitoring results help
characterize the local situation and as health effects research clarifies the
significant characteristics of air quality exposure. These considerations
apply especially to particulate matter, which is made up of highly variable
components in space and time.
The major sections of this report treat the following topics:
t Characteristics of PM]_g
• Monitoring objectives
• Elements of site selection
• Methodology for siting PM]_g monitors
• Examples of siting studies.
1 TSP refers to total suspended particulate matter, and PM^g refers to
particulate matter that includes particles in the nominal size range of
10 urn and smaller aerodynamic diameter.
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The principal steps in the siting methodology described in Section 5
include the following:
1. Determine needs for monitoring data
2. Assemble and analyze available participate matter data
3. Model levels of Pl^o
4. Determine PM^o monitoring network requirements
5. Select location and placement of PM^o monitors
6. Document and review site selection.
The appendixes include descriptions of sources of data that may be
useful in the site selection process.
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SECTION 2
. CHARACTERISTICS OF PM10
is the Indicator for the National Ambient Air Quality Standard
(NAAQS) participate matter, which replaces total suspended particulate matter
(TSP). "PM^o" means particulate matter with an aerodynamic diameter less
than or equal to a nominal 10 wm, as measured by the reference method
described in Appendix J, 40 CFR 50, and in accordance with 40 CFR 53, or as
measured by an equivalent method designated in accordance with 40 CFR 53.
In siting monitors for measuring PM^Q, it is desirable to understand the
general principles that govern the generation, transformation, and removal of
particulate matter; the basic workings of available instrumentation; and the
significant factors that control the spatial and temporal patterns of PM^o-
GENERAL PRINCIPLES
Particulate matter as an air pollutant includes a broad class of airborne
liquid or solid substances that vary greatly in chemical and physical properties.
One important characteristic is size, because larger particles are not collected
in the human respiratory tract and are therefore not a health hazard. Because
of irregularities in shape, density, composition, and structure of atmospheric
aerosols, individual particles are conveniently characterized by their aerodynamic
equivalent diameters (AED). Particles with the same fall velocity are defined
as having the same AED, which for convenience is specified as the diameter of
a uniform sphere with unit density that obtains the fall velocity (e.g., see
Corn 1976).
Throughout this document, most references to particle size refer to AED.
When the effects of particles on visibility and light scattering are considered,
the use of a different definition of particle size more closely related to
actual physical size may be necessary. The primary health hazards from
particulate matter are due to its depositon in the human respiratory tract.
The impact of particle size and chemical composition on the deposition
process is discussed in the EPA staff review of the NAAQS for particulate
matter (EPA 1981a).
The atmospheric aerosols that make up PM^Q measurements will vary both
in size distribution and in chemical composition. Generally, three distinct size
modes are present, although the smallest size mode is often difficult to detect.
This is shown by the data in Figure 1, which were collected in the California
ACHEX study (Whitby 1980). The smallest size mode (<0.1 urn) is short-lived and
most often observed as a distinct class near combustion sources. The small
nuclei (Aitken) mode particles grow rapidly by coagulation into the next largest
size mode. The middle size (accumulation) mode particles (0.1-2.5 wm) are formed
mainly by coagulation of and vapor condensation on the nuclei mode particles.
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The largest coarse size mode particles (>2.5 um) generally make up most of the
mass and include particles formed by anthropogenic processes and reentrained
surface dust. The two smaller size modes make up what is generally referred to
as fine particulate, and the largest size mode is coarse particulate.
Figure 1. Average volume size distribution for seven sites in the
California ACHEX study in 1972 (Whitby 1980).
These two classes, fine and coarse particulates, have different sources and
behave independently in the atmosphere. Fine particles mainly result from
combustion'processes, including the condensation and atmospheric transformation
of exhaust gases to form PM. Mechanical processes and wind erosion produce
coarse particles. Figure 2 summarizes the principal differences in size and
composition of the two types of particles. Fine particles typically consist of
sulfates, nitrates, carbonaceous organics, ammonium, and lead. Coarse particles
typically consist of oxides of silicon, iron, aluminum, sea salt, tire particles,
and plant particles.
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Ill
si
< Q
2 O
II till
FINE
I I ITT
COARSE
SULFATES. ORGANICS.
AMMONIUM, NITRATES,
CARBON. LEAD. AND
SOME TRACE CONSTITUENTS
CRUSTAL MATERIAL \
(SILICON COMPOUNDS. X
IRON, ALUMINUM), SEA \
SALT, PLANT PARTICLES \
\
! I
0.1
1.0
PARTICLE DIAMETER, am
10.0
Figure 2. Idealized time and coarse particle mass and chemical composition
(U.S. Environmental Protection Agency 19815).
Both manmade and natural sources emit atmospheric PM. Natural sources in
the United States emit about 84 million metric tons annually, while manmade
sources emit 125 to 383 million metric tons annually. Dust, sea spray, wild
fires, biogenic emanations, and volcanoes are the principal natural sources.
Most of the manmade emissions are fugitives from roads (unpaved and paved),
construction activities, agricultural tilling, mining activities, and industrial
processes. The emissions are estimated using approximations. Reliable
estimates of particle emissions from the combustion of fuel and well-defined
sources are also available (see Table 1), but these are estimated to include
only about 10 percent of the total manmade emissions. However, almost all of
these manmade emissions are fine particles, while the natural and fugitive
emissions are coarse particles, of which 50 percent or less are smaller than
10 um. Most of the sources of coarse particles exist in rural areas where
population densities are low.
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TABLE 1. NATIONAL ESTIMATES OF PARTICIPATE EMISSIONS
(106 metric tons per year) (EPA 1981b)
Source category 1940 1950 1960 1970 1975 1978
Stationary fuel 8.7 8.1 6.7 7.2 5.1 3.8
combustion
Industrial processes
Solid waste disposal
Transportation
Miscellaneous
TOTAL
9.9
0.5
0.5
5.2
24.8
12.6
0.7
1.1
3.7
26.2
14.1
0.9
0.6
3.3
25.6
12.8
1.1
1.1
1.0
23.2
7.4
0.5
1.0
0.6
14.6
6.2
0.5
1.3
0.7
12.5
The height of release of emissions can have an important bearing on human
health. For example, emissions from motor vehicles and home heating in
densely populated areas may be as important as emissions from large stationary
sources in remote areas. Both types of sources must be taken into account in
assessing monitoring sites.
INSTRUMENTATION
Until a sufficient data base is developed'for PM^Q measurements, most
of the information that is available to indicate the nature of particulate
matter concentrations will be based on TSP measurements made with high-volume
(hi-vol) monitors. Therefore, it is important to understand what hi-vols
measure and how this differs from what PMjo monitors measure. In addition,
the advantages and limitations of instruments that use optical reflectance and
beta attenuation need to be understood.
Hi-Vol TSP- Monitors
The hi-vol sampler collects particles on a glass-fiber filter. Air is
drawn through the filter at a relatively high flow rate (approximately
1.5 m-Vmin). Although the collection efficiency for larger (>10 urn) particles
is sensitive to wind speed, hi-vols collect essentially all particles less
than 25 urn under most conditions. The AED of particles with a 50 percent
collection efficiency varies from 25 to 30 urn. However, day-to-day variations
in wind speed account for no more than a 10 percent variability in measured
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concentrations (EPA 1981b). Under identical meteorological conditions, a
typical coefficient of variation is 3 to 5 percent. A more significant
problem is the formation of artifact mass caused by the reaction of acid
gases with material collected on the glass-fiber filter during a 24-hour
sample collection. An estimated 6 to 7 ug/m3 can be added to a 24-hour
concentration measurement by artifacts. Errors may also occur due to
loss of volatile particles, deposition on filters before and after sampling,
gas reactions after sampling, and filter handling.
Potential Reference Method for
The reference method for PMjQ is designed to measure that portion
of suspended particulate matter in the atmosphere that is likely to be
deposited in the thoracic region of the human respiratory tract. The
reference method has a collection efficiency of 50 percent for parti-
cles with 10 urn AED (i.e., DSQ = 10 um).l The measurement consists of
drawing air at a constant rate through a specially shaped inlet that
inertially separates particles larger than 10 urn from the sampling stream.
The effectiveness of the size discrimination for the 10 urn separation must
match the prescribed limits defined by the reference method, or not differ
by more than 10 percent in the expected mass concentration measured by a
sampler with the ideal size cut efficiencies. The particles contained in
each sampling stream are collected on a filter that is weighed (after
moisture equilibration) before and after sampling. As with hi-vol sampling,
the volume of air sampled is also measured and corrected to EPA reference
conditions (i.e., 25° C and 760 mm Hg).
Although the median particle size collection efficiency is the principal
characteristic of a PMjQ reference method sampler, a sampler must also meet
the following criteria to be a reference method:
t The particle'size above which the collection efficiency
is less than 50 percent must be within 1 urn of 10 urn.
• The concentration measurements must be reproducible with
15 percent precision.
• The flow rate must be stable to within 10 percent of the
initial flow rate over a 24-hour period.
The specific requirements of a PM^o reference method are given in
Appendix J of 40 CFR 50.
1 The particle size cut, 059, of a PM sampler is defined as the par-
ticle diameter at which the collection efficiency is less than 50 percent
for all larger particles.
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samplers are subject to errors due to loss of volatile particles,
artifact PM, nonsampled PM deposition, humidity, filter handling, flow rate-
variations, and air volume determinations. However, the uncertainties associ-
ated with gravimetric measures of particulate matter are less than those
associated with particulate measurements based on other principles.
Other Particulate Matter Measurements
The gravimetric method of measuring PM is limited by the need to
(1) accumulate an adequate mass for detection by use of an analytical balance,
(2) condition the filter for moisture content, (3) separate the collection
time from the mass assessment time, and (4) handle the sample between collec-
tion and assessment. To eliminate these disadvantages, optical sensing and
beta attenuation measurement principles can be used. However, measurements
based on these principles do not measure mass directly and may produce
variable concentration estimates when certain properties of the particles
vary (e.g., particle size distribution or carbon content).
A commonly used instrument based on optical sensing is the tape sampler.
Particles are collected to form a stain on a paper tape filter, which is
periodically advanced. The transmittance of light through the stain is
measured to determine the optical density or coefficient of haze {COHS). The
COHS units at a given site may be calibrated to mass measurements made with a
colocated gravimetric device. The tape sample is capable of finer time
resolution and faster readout time than gravimetric sensing methods. For
certain purposes, including response to severe pollution buildups that require
a rapid update of information, optical sensing may be a necessary alternative
to gravimetric sensing.
It is also possible to measure specific properties of collected samples.
Such properties may include sulfate and nitrate components, visibility reduc-
tion, and specific elemental components. The need for information other than
mass concentration of PM should be defined when monitoring operations are
planned and factored into siting considerations. Samples taken for mass
concentration measurements usually can be used for other purposes, because the
mass measurement techniques preserve the samples.
USE OF AVAILABLE DATA TO DRAW INFERENCES ABOUT PM10 LEVELS
Because of the abundance of TSP data and the limited quantity of PMjg
data available, it may be necessary to use TSP or other available measures
of PM to determine expected patterns of PMio« EPA has published a document
examininy relations of PM^ to other particulate matter (Procedure for Estimating
Probability of Nonattainment of PM10 NAAQS Using TSP or PMi0 Data). The details
of this procedure are beyond the scope of tnis document; however, a few
conclusions from this report are provided.
The ratio of PMiu/TSP was examined at sites consisting of collocated PM^g/TSP
sites operating in 1982 and 1983 for the purpose of establisning a simple ratio
which would permit the direct adjustment of TSP to PMiQ. However, upon scrutinizii
the data base, it was clear that a substantial degree of variability existed
amonyst individual ratios. (The IP/TSP ratios were also examined, only to
8
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establish that they confirmed the PMjo/TSP analyses.) This variability includes
inter- as well as intrasite differences in the ratios. As described elsewhere in
the document, the PMio/TSP ratio was also found to be somewhat sensitive to TSP
concentrations. This sensitivity is diminished by focusing on site-days observing
TSP > 100 ug/m3 or, in the case of annual analyses, site-years with TSP _> 55
ug/ni3".
Several attempts were also made to find an explanatory site descriptor
which could account for the disparity in the ratios among sites
(i.e., inter-site variability). In the first attempt, such site descriptors
as urban versus suburban were compared; however, no statistically significant
difference was found. Geographic area (East, Southwest, West Coast, etc.)
and site type (industrial, commercial or residential) likewise revealed
insignificant differences in the ratios. In a more recent and more
extensive investigation of geographic differences performed on the entire
1982 and 1983 data base, statistically significant differences were found
among individual sites as well as among larger groupings of sites. However,
the differences among larger groupings of sites are smaller and are difficult
to explain on a physical basis. These investigations conclude that unless
sufficient data to calculate a site specific PMjQ/TSP ratio are available,
the existing data base does not justify use of different distributions of
ratios for different parts of the country.
The previously described investigations of geographic, climatological ,
concentration range,or site type classifiers were attempts to reduce or
account for part of the variability in PM^Q to TSP ratios. No doubt, a part
of the overall variance" in ratios results from intra-site variation in
ratios arising from differences in the sources impacting the monitor site.
As discussed in other sections of the document, there are several issues
associated with tne precision of the TSP and PMjg measurements which affect
intra-site variance. These factors include windspeed dependence, weighing
problems, artifact formation and sampler wall losses. Thus, the inter-site
variance can potentially be eliminated by the use of site specific data, but
the intra-site variance can only be partially reduced by careful operating
procedures.
The variance among PM10/TSP ratios suggests the need to examine the frequency
distribution of ratios ratner than relying on a single value for the ratio.
The cumulative frequency distribution for PM10/TSP is presented in Figure 3 for
sue average (arithmetic mean) ratios. Figure 4 contains a similar distribution
"or <:4-riour ratios.
SPATIAL/TEMPORAL PATTERNS
National spatial and temporal patterns of PMjQ have been deduced from
a variety of available PM observations. Sections 3 and 4 of this document
contain guidelines for estimating these patterns in local areas. Important
factors that influence the patterns are the sources of emissions, topography
and other physiographic factors, and meteorology. Figure 5 shows an indica-
tion of the variation in concentrations that-can be expected with season of
the year and with rural, suburban, and urban location. These graphs are
oased on monitoring data from a small number of sites.
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TSF IHI-VOU
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SUBURBAN
~^ RURAL
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JUL »UG SEPT O:T MOV DEC
Figure 5. Seasonal variations in urban, suburban, and rural
areas for four size ranges of particles.
Source: After Trijonis et al . (1980).
12
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Influence of Sources
The highest TSP values are found in dusty arid regions and in industrial-
ized cities. Table 2 shows a breakdown of tne principal categories of sources
that comprise the EPA national inventory of participate emissions. The much
larger fugitive emissions from nonindustrial anthropogenic activities, such as
travel on unpaved roads and wind-eroded farmland, are not included in these
figures. These indirect fugitive emissions are coarse particles, and less
than 50 percent of their mass will be less than 10 urn in diameter. Furthermore,
the sources are widely dispersed and not concentrated near populated areas.
Most of the interest in controlling and monitoring particulate emis-
sions focuses on the stationary sources listed in Table 2. These emissions
are believed to contain more toxic elements and to consist primarily of fine
particles. Fugitive dust emissions from stationary sources are of particular
concern, because they exceed stack emissions, are emitted near ground level,
and contain more toxic materials than soils from farmlands and unpaved roads
away from irfdflstrial sources.
Influence of Atmospheric Processes
PM emitted into the atmosphere is transported by the wind and diluted
by various atmospheric turbulence and mixing processes. In addition, particles
are removed by dry and wet deposition processes. Particles remaining airborne
may grow by condensation, coagulation, and chemical reactions; these growth
processes are enhanced by the accumulation of moisture. Figure 6 summarizes
and graphically illustrates many of these various atmospheric processes.
Secondary pollutants, which form and grow due to these atmospheric
processes, are a major component of PM concentrations. Sulfates, formed
primarily by atmospheric reactions, often account for 40 percent of the fine
particles. Because fine particles typically contribute about one-third of
TSP mass and because PM^Q is expected to equal about 50 percent of the TSP
levels, it is reasonable to expect the sulfate contribution to equal about
25 percent of PM^g measurements. But on many occasions the total contri-
bution of secondary PM to PM^Q measurements may be considerably higher
than 25 percent. Because the formation of secondary PM requires time, the
principal sources are likely to be remote from the point where they are
measured. This makes it important to measure PM^Q concentrations upwind
of urban areas, as well as within and downwind of the areas of concern.
The formation of sulfates and nitrates is sufficiently active in both summer
and winter to produce high contributions to PMio measurements. The
formation of organic aerosols is also important; observed 24-hour concen-
trations have reached as high as 100
13
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TABLE 2. SUMARY OF NATIONAL 1985 PARTICIPATE
MATTER EMISSIONS BY SOURCE CATEGORY (EPA 1987)
1985 Emissions
Source Category (103 tons)
Coal-fired electric utility boilers 627
Coal-fired industrial boilers 132
Integrated iron and steel plants and coke ovens* 187
Portland cement plants 286
Primary nonferrous smelters* 44
Solid wasfe disposal plants 110
Kraft Pulp and paper mills 110
Asphalt batching plants 132
Concrete lime and gypsum 99
Iron and steel foundries 44
Subtotal for selected source categories 1771
Stationary sources§ 6600
Mobile sources 1430
Al1 sources 8030
Includes emissions from materials handling and storage piles.
* Includes fugitive process emissions and emissions from ore crushing and
materials handling.
$ Hy difference between all sources and mobile sources.
14
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FREETROPOSPHERIC
EXCHANGE
VERTICAL
DIFFUSION
•TRANSPORT
BY WIND
AEROSOL
CONDENSATION
I" ^ ^ — •!
COAGULATION
CHEMICAL REACTIONS
ABSORPTION IN
CLOUD ELEMENTS
SEDIMENTATION
AS AEROSOL
DRY DEPOSITION ON
THE GROUND
////''///>'"', "/"'///<
• /•/'///" "/''''''' i'
enno«e ANTHROPOGENIC
SOURCES SOURCES
• ABSORPTION IN
» t » PRECIPITATION
WASHOUT IN PRECIPITATION
Figure 6. Complex processes affecting transport .and transformation
of airborne particulate matter.
Source: Adapted from Drake and Barrager (1979).
-15
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SECTION 3
MONITORING OBJECTIVES FOR PM10
Two pressing questions arise in planning a monitoring program. How many
monitors are required? And where should they be located? The answers affect
the allocation of resources that, particularly in operational settings,
ultimately shape the final program.
Due to a wide diversity of topography, population distribution, source
locations, and climates, ambient air quality monitoring regulations and
policies rarely specify the number and location of monitors. But regardless
of the influence of physical factors, the specifications for a monitoring
network strongly depend upon the monitoring objectives.
A monitoring objective relates the monitoring mandate to spatial/temporal
variations in sources of pollution, meteorology, and receptors of pollution.
The monitoring mandate arises from specific needs and uses for data.
A monitoring objective is the link between the goals of the mandate and
appropriate siting opportunities in the monitoring scene. Monitoring objec-
tives relate program objectives that concentrate upon end uses for the
collected data and physical objectives that concentrate on the temporal and
spatial character of representative sampling.
One obvious use of PM^Q data lies in establishing environmental
regulations and policies. Such regulatory mandates are rooted in the Clean
Air Act (CAA) and other Federal, state, and local regulations that specify
air quality requirements.
Other data uses satisfy research needs and support public services. A
variety of data uses for the criteria pollutants have been summarized in
other EPA monitoring guidelines (Ball and Anderson 1977; Ludwig and Kealoha
1975; Ludwig, Kealoha, and Shelar 1977; Ludwig and Shelar 1978) and elsewhere
(e.g., EPA 1977a). Table 3 summarizes these varied data uses:
• Evaluation of ambient air quality
• Enforcement of source-specific regulations
t Evaluation/development of control plans
• Air quality maintenance planning
• Protection of public health
• Development and testing of models
• Research.
16
-------
TABLE 3. PRINCIPAL DATA USES FOR PM10
1. Evaluation of Ambient Air Quality
- Judging Attainment of NAAQS
- Establishing Progress in Achieving/Maintaining NAAQS
- Establishing Long-Term Trends
2. Enforcement of Source-Specific Regulations
- Categorical Sources (ESECA, SCS, PSD)
- Individual Sources
- Enforcement Actions
3. Evaluation/Development of Control Plans
- SIP Provisions
- Evaluation/Development/Revision of Local Control Strategies
4. Air Quality Maintenance Planning
- Establishing Baseline Conditions
- Project Future Air Quality
5. Protection of Public Health
- Air Quality Indices
- Documentation of Population Exposures
- Response to Unique Citizen Complaints
- Development/Revision of Standards
6. Development and Testing of Models
- Input for Receptor Models
- Validation and Refinement
- Assessing Representativeness of Monitoring Networks
7. Research
- Effects on Humans, Plants, Animals and Environment
- Characterization of Source, Transport, Transformation, and Fate for
Anthropogenic and Natural Emissions
- Development/Testing of New Instrumentation
17
-------
The order of the listed uses does not represent any sense of priority. The
uses are a composite of diverse program objectives that would require extended
discussion to develop in detail.
In all areas except research, a straightforward relationship exists
between mandate and program objectives or data uses. Thus these representativ<
data uses provide a range of example situations, so that physical objectives
for specific cases not covered here can be developed by analogy.
EVALUATION OF AMBIENT AIR QUALITY
The National Ambient Air Quality Standard for PM]n stipulates
acceptable air quality in terms of a 24-hour criteria level (not to be
exceeded more than the specified number of times a year) and an annual
criteria level (the 12-month arithmetic mean). Although the NAAQS is the
principal standard that must be met, other local and state agencies may set
standards that must be met.
Compliance with the NAAQS is a fundamental goal of ambient air quality
control strategies (particularly for State Implementation Plans (SIPs)) and
forms the basis for air quality maintenance planning, policy development, and
additional regulation. Data are needed to evaluate ambient air quality and
detect compliance with the NAAQS. Attainment status is conferred upon
an area, based on the expectation that the NAAQS criteria levels are not
violated. Therefore, the monitoring objectives are geared to acquiring
measurements that represent conditions throughout the area in question, the
underlying context being that air quality levels elsewhere in the area are no
worse than those indicated by the measurements.
The data are also used to demonstrate reasonable progress toward attain-
ment for areas in violation of the NAAQS, document baseline conditions for
environmentally sound expansion and development, and depict long-term trends.
ENFORCEMENT OF SOURCE-SPECIFIC REGULATIONS
Under some circumstances, major air pollution sources are allowed
to operate under demonstration that their emissions do not cause ground-level
concentrations that exceed a specified criteria level. The criteria level is
ordinarily"tied to NAAQS, but may be tied to other criteria. These situations
may prevail for power plants, coking facilities, and other categorical
sources under a variety of regulations. Source-specific regulations may
consist of tailored or negotiated agreements that are integrated to implemen-
tation plans on a source-by-source basis. Although the responsibility to
monitor may fall upon a regulatory agency or upon the source management, the
objective is to measure the impact of a known source.
18
-------
Indications of compliance/noncompliance are often used in enforcement
proceedings and frequently form the basis for litigation and negotiation.
A corollary monitoring situation entails isolating an offending source or
family of sources when an adverse impact is measured.
Many applications require a long-term, continuing monitoring program.
However, in some enforcement situations, a relatively short sampling program
or a periodic survey approach is applicable.
EVALUATION/DEVELOPMENT OF CONTROL PLANS
Government monitoring agencies and pollution source operators are
actively concerned with gaining/retaining NAAQS attainment status. Procedures
for pursuing this goal are stated in the SIP, which is expanded and modified
as needed.
Monitoring data are needed for the following purposes:
• Define nonattainment areas
• Develop control policies and strategies
• Define nondeterioration areas
• Develop air pollution emergency episode plans.
The monitoring data are used to demonstrate and characterize the need
for controls. The demonstration may identify categorical sources or specific
sources. Nondeterioration areas and areas subject to growth or economic
rejuvenation require monitoring to define the baseline conditions.
Monitoring data are needed in areas subject to extremely high concen-
trations to identify the onset and abatement of episodes. A separate guidance
document on the timely reporting of PMjQ concentrations during emergency
episodes is available (EPA 1983).
AIR QUALITY MAINTENANCE PLANNING
Planning agencies and developers from the private sector require
monitoring data to determine baseline air quality levels in locations of
projected growth and expansion. These data may be critical in determining
whether such activity will meet Prevention of Significant Deterioration
(PSD) requirements in attainment areas or interfere with progress toward
attainment of NAAQS in nonattainment areas. Siting considerations need to
consider whether special sites are needed to meet these data needs or
whether the nearest available monitoring will be adequate.
19
-------
PROTECTION OF PUBLIC HEALTH
It can be argued that all air quality monitoring is ultimately oriented
to public health. Air quality indices keep the public appraised of current
levels of air pollutants. The siting requirements to meet the data use need
to be coordinated with needs for emergency episode data and for ambient air
quality evaluations. A second category of public health oriented data
use involves documentation of population exposures. This may require a
specially sited network designed to estimate personal exposures in connection
with epidemiological studies. Special monitoring sites may also be required
to respond to unique citizen complaints. These frequently involve sources
and impacts that are not part of operational coverage.
DEVELOPMENT AND TESTING OF MODELS
Monitoring requirements to support model development or testing are
generally unique for each project. This is particularly true for model
development support where the objective is to describe and understand the
ongoing processes or to develop parameter values representative of a specific
terrain, meteorological condition, or source configuration. As a general
rule, monitoring for model development must be intensive and flexible to
provide the maximum benefit. Measurements are desired that are as tightly
spaced and as frequently recorded as are compatible with economic restraints.
However, the monitoring equipment should be mobile enough so that it can be
moved as conditions change or as analyzed information indicates a need for
information from different locations.
The primary emphasis is on demonstrating that the model being tested
adequately estimates the highest concentrations. This means that monitoring
data needs to be taken at locations downwind of major sources during critical
meteorological conditions. The data record needs to be sufficiently long to
truly characterize the data site—usually a minimum of 1 year--if the model is
to demonstrate validity at the test site. Test data, preferably from a
different locality, must be independent of data used to develop the model.
The placement and number of monitors will depend on meteorological conditions,
topography, source characteristics, and purpose of the model. Sections 4 and
5 of this report provide further suggestions with respect to these influences.
RESEARCH
Monitoring data is needed to support research allied to PM^Q questions
in order to improve the scientific tools for measurement, interpretation, and
prediction. Monitoring sites selected to support research may coincide or
20
-------
supplement other monitoring requirements. Research needs in the following
areas may be considered when selecting sites:
1. Effects on humans, plants, animals, and environment
2. Characterization of source, transport, transformation,
and fate for natural and anthropogenic emissions
3. Development and testing of new instrumentation.
21
-------
SECTION 4
ELEMENTS OF SITE SELECTION
The site selection procedures offered in Section 5 rely primarily on
inferred and demonstrated associations among PM^o sources, meteorology, and
a number of physical factors such as topography and land use. Important
outcomes (i.e., ambient concentrations) can vary tremendously from place to
place within a monitoring scene and from time to time at a given place. From
a useful perspective, any area to be monitored is going to be too complex to
bring all structures into focus at once. The concept of representative scale
is a useful way to characterize these variations on a physical basis that
can be related to comprehensible patterns.
REPRESENTATIVE SCALES
The concept of representative spatial scale is used to define a character-
istic distance over which pollutant concentrations are uniform or nearly so.
As a corollary, we can define homogeneous areas in which measurements performed
in the relatively small air volume near a sampler (nominal horizontal extent
of 1 meter) can represent conditions prevailing over some much larger area.
Representative spatial scales illustrated in Figure 7 have been previously
identified (EPA 1979) and are compatible with spatial scales of source areas.
We shall be concerned with the following spatial scales:
t Microscale—ambient air volumes ranging in horizontal
extent from a few meters to as much as 100 m. The
microscale encompasses the immediate vicinity of the
monitor. In the immediate presence of PMjo sources,
exposure may in reality be only representative of the
microscale. For this reason, the microscale is the final
judgmental factor in site selection (see Section 5) and
requires a site visit to make this appraisal, because maps
rarely portray confounding influences in sufficient
detail.
9 Middle scale—ambient air volumes covering areas larger than
microscale but generally no more than 0.5 km in extent.
In settled areas, this may amount to several city
blocks. As will be shown later, this is essentially the
lower limit of resolution for most models.
• Neighborhood scale—ambient air volumes whose horizontal
extent is generally between 0.5 and 4 km. The neigh-
borhood scale is aptly named. It is useful in defining
extended areas of homogeneous land use.
22
-------
,'Micro-Scale
(<0.1 kn)
\ \
Neighborhood Scales
(0.5 to * km)
URBAN COMPLEX
Regional Scales
(>50 lun)
Urban Scales
(4 to 50 'an)
Figure 7. Illustration of various spatial scales of representativeness
(Ball and Andersen 1977).
23
-------
t Urban scale—ambient air volumes whose horizontal extent
may range between 4 and 50 km. This is frequently the
most desirable representative spatial scale, because
it captures an entire urban area. However, the diversity
of sources that prevail within such areas argue against
homogeneity at this scale.
f Regional scale—ambient air volumes whose horizontal extent
ranges from tens of kilometers to hundreds of kilometers.
Monitors that are unaffected by specific sources or by
localized groups of sources can be representative at this
scale.
• National and global scales—seek to characterize air quality
from a national perspective (thousands of kilometers) or from
a global perspective (tens of thousands of kilometers).
Although all of the above scale intervals may be needed to subdivide a
monitoring scene, the neighborhood scale in urban settings and the regional
scale in substantially unsettled areas are particularly practical scales for
spatial coverage by a single monitor. In many circumstances, the representativ
ness of the small scales must be estimated by networks composed of a limited
number of sites.
ANALYSIS OF THE AREA TO BE MONITORED
The primary intent of the analytical process that supports site selec-
tion is to characterize pollutant levels within the area to be monitored.
This requires information regarding the location of important sources of
PMiQ, a description of atmospheric trajectories to trace the movement
of PM]_o, and estimates of dispersion accompanying such movement. These
reflect a complex interplay among topography and climatology that must be
cast into time frames that are compatible with the NAAQS. Three components
for analysis are as follows:
• Regional dispersion climatology—to assemble
the basis for transport/dispersion patterns
that may be applied to the area to be
monitored as a whole
• Physical differentiation—to assemble the basis
for identifying distortions of simple source/
receptor relationships due to local alterations
of trajectory and dispersion
• Emissions configuration—to assemble the basis
for identifying relevant PM^Q sources and
recognizing useful patterns.
24
-------
An area of interest with respect to air quality is usually defined by
political boundaries, such as state, county, city, or air quality control
region lines. A method of systematically characterizing the area to be
monitored into homogeneous areas of air quality levels that are potential
locations of air pollution monitoring sites requires that sources of
particulate emissions, patterns of terrain and physiography, and climatology
be taken into account. A method and data sources for performing such a
classification analysis for ambient concentrations of PM have been developed
in this study beginning with a description of the three categories of influ-
encing factors. The methodology is presented in Section 5.
Emissions Configuration
The emissions configuration is simply the spatial /temporal distribu-
tion of sources throughout the monitoring scene; in concept, it will consist
of one or more maps delineating areas of similar source characteristics.
Depending on the mix of sources and local/regional climate, such maps will
depict relevant seasonal and diurnal emissions patterns in terms of relative
intensities and release heights.
In concept, the most straightforward approach to generating maps
would be to selectively allocate the elements of a formal emissions inventory
to a suitably detailed grid. In practice, this is not
an automated approach carries a substantial burden
manipulation. Though difficult, this approach has
a trivial task; even
in data management and
merit because it develops
highly usable data for subsequent computerized modeling.
An alternative approach is to proxy these source areas by patterns
of land use. In most urban areas, planning agencies have compiled informa-
tion that can form the basis for categories of near-surface emission. In
the absence of such information, relatively unsophisticated interpretation
of aerial photographs can be helpful. Table 4 offers a land use classifica-
tion that is amenable to this approach. Emission factors can be assigned
to each land use classification based on consideration of local heating
fuels, climate, and census data in housing and population densities. In
addition, large point sources (e.g., 1000 tons per year) should be separately
identified.
The first use of an emissions configuration is in a semiquantitative
or subjective mode. The orientation of key impact zones can be surmised
with the aid of appropriate wind roses. Areas likely to be inundated by
several sources can be identified.
An emissions inventory provides important information to 'the site
selection process by identifying significant point and area sources and
cataloging emissions in terms of location, source strength, operating charac-
teristics, etc. The National Emissions Data System (NEDS), for instance,
identifies individual point sources that release 100 tons per year or more
25
-------
TABLE 4. IDENTIFICATION AND CLASSIFICATION OF LAND USE TYPES (AFTER AUER 1978)
Type
Use and structures
Vegetation
II Heavy Industrial
Major chemical, steel, and fabrication industries;
generally 3- to 5-story buildings, flat roofs
12 Light-Moderate Industrial
Rail yards, truck depots, warehouses, Industrial
parks, minor fabrications; generally 1- to 3-story
buildings, flat roofs
Cl Commercial
Office and apartment buildings, hotels; >10-story
heights, flat roofs
Rl Common Residential
Single-family dwelling with normal easements;
generally single-story, pitched-roof structures;
frequent driveways
S2 Compact Residential
Single- and some multiple-family dwellings with
close spacing; generally <2-story, pitched-roof
structures; garages (via alley), no driveways
R3 Compact Residential
Old TOjlfifamily dwellings with close (<2 m)
lateral separation; generally 2-story, flat-
roof structures; garages (via alley) and ashpits;
no driveways
34 Estate Residential
Expansive family dwelling on mu Hi acre tracts
Al Metropolitan Natural
Major municipal, state, or Federal parks, golf
courses, cemeteries, campuses; occasional single-
story structures
A2 Agricultural Rural
12 Jnaeveloped
jncultivatea; wasteland
Grass and tree growth extremely
rare; 70S vegetation
Limited lawn sizes and shade
trees; <30S vegetation
Limited lawn sizes, old estab-
lished shade trees; OS! vege-
tation
Abundant grass lawns and lightly
wooded; >951 vegetation
Nearly total grass and lightly
wooded; >955 vegetation
Local croos (e.g., corn, soybeans);
>955 vegetation
Mostly wild grasses and weeds,
lightly wooded; >90" vegetation
26
-------
of five criteria emissions (participate matter, SOX, NOX, CO, hydro-
carbons) as well as area sources aggregated at the county level (i.e., all
other stationary sources that individually emit less than 100 tons per year
and all mobile sources). More detailed approaches (e.g., Pace 1979) develop
microinventories that add perspective and structure to the area source
category.
It is beyond the intended scope of this report to promote method-
ologies for constructing emission inventories. For the purposes at hand,
an emissions inventory for parti cul ate matter emissions is assumed to be
available and ready for use. Such an inventory may be composed of NEDS-
based data (EPA 1984) or may have been specially constructed for the
monitor siting analysis.
During the last few years EPA has had PM^Q emission factors developed
for a large number of source categories. The development of PM^o emission
factors for additional source categories including some fugitive and open
sources is still in progress at this time. The user is referred to EPA's
Compilation of Air Pollution Emission Factors AP-42 for specific emissions by
source category and specific methodology for their use in developing emission
estimates. The compilation provides specific factors not only by general
source category but also for each processing step within a category. Tables
5 and 6 present example emission factors for some selected source categories.
These examples have been taken from EPA's report.
Terrain and Physiography
The patterns of ambient concentrations that occur due to the transport
and diffusion of pollutants over open and flat terrain are significantly
distorted by irregularities in the terrain and other features of physiography,
Two major factors in this regard are as follows:
• Aerodynamic diversion — flow around and over obstacles.
Distortion of the flow field may be severe during
moderate to strong synoptic winds.
• Local circulations — mountain-valley winds, land-sea
breezes, and the like that may prevail when synoptic
influences are sufficiently weak. Under these conditions,
flow patterns within the scene may "wall off" subareas.
Transport and dispersion estimates at one place are
unlikely to. reflect air motions elsewhere.
27
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TABLE 5. CUMULATIVE PARTICLE SIZE DISTRIBUTION AND SIZE SPECIFIC EMISSION
FACTORS FOR SPREADER STOKERS BURNING BITUMINOUS COAL8
EMISSION FACTOR RATING:
C (uncontrolled and controlled for
multiple cyclone without flyash
reinjectlon, and with baghouse)
E (multiple cyclone controlled with
flyash reinjection, and ESP
controlled)
rirttcl. «»••* '
11
10
t
1.1
1.11
1.00
O.tll
Tom
CMMl«l*. MM I <_ H*t*t mil*
OMMCnll**
11
10
14
7
1
1
4
100
CM...
rSS
M
71
11
1
1
1
1
100
:»
74
tl
"
17
It
14
*
100
1*4
1ST
*7
W
11
tl
4t
41
'
100
lUKllll
71
M
4t
It
II
11
7
100
CwUCtM MlMlM iMtM Ikf/M (IWIM) CMl, M flrW)
*******
1.4
(It.l)
4.0
(11.0)
4.1
(1.4)
1.1
(4.1)
1.1
(1.0)
t.l
(1.0)
1.1
(1.4)
30.0
(40.0)
CMralU*
Italtlfl*
7.1
(I4.t)
t.l
(11.4)
4.1
(l.t)
0.7
(1.4)
0.1
(0.4)
0.1
(0.4)
O.I
(0.1)
1.1
(17.0)
SS2I
4.4
(I.I)
1.*
(7.1)
1.1
(t.l)
l.t
(1.1)
1.0
(1.0)
O.I
(l.t)
0.1
(1.0)
4.0
(11.0)
m
0.11
(0.4t)
0.11
(0.44)
0.10
(0.4O)
0.15
(0.10)
0.11
(0.11)
0.10
(0.10)
*
0.14
(0.41)
W.HM..
0.04]
(O.Olt)
O.Olt
10.071)
0.011
(0.05*)
O.Olt
10.011)
0.011
(0.011)
O.OO*
(0.011)
0.004
(0.001)
0.04
«nc« tl. UP
tlymmti rvii
ISP. M.Zt;
Multiple cyclone with
flyash reinjection
ilultiple cyclone without
flyash reinjection
Saghouie
Uncontrolled
.1
.2
.4 .6
10
20
40 60 100
10.0
6.C —
•o "g
4. C Z •-
o "-
i •»
c •»
o
2.0 u-
**" O
•D O*
1'° 51,
* a
0.6 o ,_
0.2
0.1
0.10
0.06
0.04 3
u
«
o.oi §^S
^ C
0.01 * '
0.006 "§ §
= f
0.004 S-
01 •"
tf* '"'
3
o
0.002 f
CO
0.001
1 2 4 6
Particle diameter (uni)
Cumulative size specific emission factors for spreader
stokers burning bituminous coal
External Combustion Sources
23
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TABLE 5 (continued); CUMULATIVE PARTICLE SIZE DISTRIBUTION AND SIZE SPECIFIC
EMISSION FACTORS FOR DRY BOTTOM BOILERS BURNING PULVERIZED
BITUMINOUS COALa
EMISSION FACTOR RATING: C (uncontrolled)
0 (scrubber and ESP controlled
E (multiple cyclone and baghouse)
•article eUa*
(«)
15
10
»
2.)
1.2)
1.00
0.625
TOTAL
Ciawlatlv* MM I < atatod ela*
Uaceac rolled
J2
23
17
*
2
2
1
100
Coat rolled
MKlClele
cycle**
54
2*
1«
)
'
1
1
100
Scnkker
SI
71
«2
51
3)
31
20
100
ur
7»
•7
SO
24
17
14
12
100
laihoua*
»7
»2
77
5J
31
25
l«
100
QaMlatlia aoilaaloa (actor* [kf/Ni (Ik/ton) coal, a* flr*d|
Becoat rolled
UM
(3.2A)
1.1M
(2.3*)
o.au
O.7A)
0.304
(O.aA)
0.10A
(0.2A)
0.104
(0.2*)
0.05A
(0.10)
5A
(IOA)
Central lad4 .
NvlClcla
cycle**
0.54*
(1.0*4)
0.2* A
(0.5«A>
0.144
(0.2SA)
0.03A
(0.044)
O.OIA
(0.02A)
O.OIA
(0.02A)
O.OIA
(0.02A)
IA
(2A)
Scrubber
0.24A
(0.4IA)
0.2IA
(0.42A)
O.IfA
(0.3SA)
0.154
(0.3A)
0.114
(0.22A)
O.OM
(0.1«A)
0.0*4
(0.12A)
0.3A
(0.44)
esr
0.032A
(0.0*4)
0.0274
(0.054)
0.020A
(0.04A)
0.012A
(0.02A)
0.0074
(O.OIA)
0.00*4
(O.OIA)
0.0054
(O.OIA)
0.044
(O.OIA)
lag hove*
0.0104
(0.02A)
0.00*4
(0.02A)
0.00(4
(0.02A)
0.00 SA
(O.OIA)
0.0034
(0.006*)
0.003*
(0.00«A)
O.OOIA
(0.002A)
O.OIA
(0.02A)
•laferoaea 61. ESF • alectroetattc preclpltaear.
>lzpreaa*d aa aerodyaamlc aaglvalent dlaaecer.
C4 - coal aoh night Z, aa fired.
'leclMted control efficiency for nultlpl* cyclone, SOX; icnjkker. *4I;
137. 9».2I; bafhouee, <*.SZ.
2.QA
1.8A
1.6A
1.4A
1.2A
l.QA
Q.BA
0.6A
0.4A
0.2A
0
Scrubber
ESP
J 1. I I
Baghouse
Uncontrolled
Multiple cyclone
J 1—i i i i i i
l.OA
•o
0.6A 5^T
0.4A §£
i2
u
0.2A *»" H
•V
»- C^
O.LA
0.06A "o
0.04A w o
m u
O
0.02A ^"
.2 .4 .6 1 2 4 6 10
Particle diameter (urn)
40 60 100
O.OIA —I
0.1A _
o
0.06A
0.04A
0.02A
O.OIA
0.006A
0.004A
0.002A
O.OOIA
Cumulative size specific emission factors for dry bottom
boilers burning pulverized bituminous coal.
EMISSION FACTORS
29
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TABLE 6. SIZE SPECIFIC EMISSION FACTORS FOR COKE MANUFACTURING
Particulate
emission Particle
factor size
Process rating (um)
Coal preheating D
Uncontrolled
Controlled D
with venturi
scrubber
Coal charging E
Sequential
or stage
Coke pushing D
Uncontrolled
Controlled D
with Venturi
scrubber
0.5
1.0
2.0
2.5
5.0
10.0
15.0
0.5
1.0
2.0
2.5
5.0
10.0
15.0
0.5
1.0
2.0
2.5
5.0
10.0
15.0
0.5
1.0
2.0
2.5
5.0
10.0
15.0
0.5
1.0
2.0
2.5
5.0
10.0
15.0
Cumulative
mass Z
<_ stated
size
44
48.5
55
59.5
79.5
97.5
99.9
100
78
80
83
84
88
94
96.5
100
13.5
25.2
33.6
39.1
45.8
48.9
49.0
100
3.1
7.7
14.8
16.7
26.6
43.3
50.0
100
24
47
66.5
73.5
75
87 -
92
100
Cumulative
e mass emission
factors
kg/Mg
0.8
0.8
1.0
1.0
1.4
1.7
1.7
1.7
0.10
0.10
0.10
0.11
0.11
0.12
0.12
0.12
0.001
0.002
0.003
0.003
0.004
0.004
0.004
0.008
0.02
0.04
0.09
0.10
0.15
0.25
0.29
0.58
0.02
0.04
0.06
0.07
0.07
0.08
0.08
0.09
Ib/ton
1.5
1.7
1.9
2.1
2.8
3.4
3.5
3.5
0.20
0.20
0.21
0.21
0.22
0.24
0.24
0.25
0.002
0.004
0.005
0.006
0.007
0.008
0.008
0.016
0.04
0.09
0.17
0.19
0.30
0.50
0.58
1.15
0.04
0.08
0.12
0.13
0.13
0.16
0.17
0.18
(continued)
EMISSION FACTORS
30
-------
TABLE 6 (Continued)
Particulatc
emission Particle
factor size
Process rating (urn)
Mobile D
scrubber car
Quenching D
Uncontrolled
(dirty water)
Uncontrolled B
(clean water)
With baffles D
(dirty water)
With baffles D
(clean water)
Combustion stack D
Uncontrolled
1.0
2.0
2.5
5.0
10.0
15.0
1.0
2.5
5.0
10.0
15.0
1.0
2.5
5.0
10.0
15.0
1.0
2.5
5.0
10.0
15.0
1.0
2.5
5.0
10.0
15.0
1.0
2.0
2.5
5.0
10.0
15.0
Cumulativi
mass Z
<_ stated
size
28.0"
29.5
30.0
30.0
32.0
35.0
100
13.8
19.3
21.4
22.8
26.4
100
4.0
11.1
19.1
30.1
37.4
100
8.5
20.4
24.8
32.3
49.8
100
1.2
6.0
7.0
9.8
15.1
100
77.4
85.7
93.5
95.8
95.9
96
100
Cumulative
s mass emission
factors
fcg/Mg
0.010
o.ou
0.011
0.011
0.012
0.013
0.036
0.36
0.51
0.56
0.60
0.69
2.62
0.02
0.06
0.11
0.17
0.21
0.57
0.06
0.13
0.16
0.21
0.32
0.65
0.003
0.02
0.02
0.03
0.04
0.27
0.18
0.20
0.22
0.22
0.22
0.22
0.23
Ib/ton
0.020
0.021
0.022
0.022
0.024
0.023
0.072
0.72
1.01
1.12
1.19
1.38
5.24
0.05
0.13
0.22
0.34
0.42
1.13
0.11
0.27
0.32
0.42
0.65
1.30
0.006
0.03
0.04
0.05
0.08
0.54
0.36
0.40
0.44
0.45
0.45
0.45
0.47
31
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In many instances these factors are of minor influence to site selection,
particularly when viewed from the perspective of the 24-hour averaging period
that defines most operational PM^o monitoring. More often, however, these
influences are severe enough to warrant attention, particularly in source-
oriented applications. There are many circumstances where an area may experienc
aerodynamic diversion problems under moderate to strong'synoptic influences
while exhibiting local circulations when synoptic conditions are weak.
Because of this, discussion of these two factors is structured around the
physical aspects of the monitoring scene that should alert the monitoring
designer to the situation. Four primary areas for discussion have been
identified: topographic influences, coastal settings, small-scale obstacles,
and urban effects.
These factors are expressed in varying intensity from area to area. A
detailed discussion of resulting patterns is clearly beyond the intended
scope of this document. Therefore, each topical area will be treated in
summary fashion, and the description will rely heavily upon illustrations.
Topographic Influences--
Topographic elements become a factor when their influences extend into
the neighborhood scale (horizontal size order of kilometers). Because the
ratio of downstream aerodynamic effect to obstacle height is on the size
order of 10 to 1, obstacles on the order of 100 m will influence horizontal
sizes of the order of 1 km. The central problem that terrain introduces is
the added detail impressed upon the advection/dispersion field. That is,
a simple pattern that may be replicated consistently throughout a scene of
level terrain becomes an inconstant three-dimensional perturbation in
the presence of substantial terrain relief. The principal types of flow
distortion that occur include separation flow on the downwind side of
ridges when the flow is perpendicular to the ridge, channeling of air flow by
valleys, and local circulations caused by differential heating of adjacent
terrain slopes.
Coastal Settings--
In coastal settings, during periods of light synoptic winds accompanied
by a sufficiently strong thermal contrast between water temperatures and land
temperatures, a land/sea breeze circulation (or conversely, land/lake breeze)
will control air motions in the vicinity of the shoreline.
Figure 8 displays the characteristic circulation patterns associated with
a lake (or sea) breeze (8a) and a land breeze (8b). This circulation system
is not static. As shown in Figure 9, the convergence zone migrates inland as
the land surface heats up. The intensity of the sea breeze may increase
through midafternoon, but dies out after sunset as the land surface rapidly
cools. At night, the land breeze sets up, but is generally less vigorous
because thermal contrasts are smaller.
32
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Lake Breeze
Front
Lake
Land
A. Lake Breeze
Land Breeze
Front
Figure 8. Characteristics of lake coast air flow.
33
-------
Hourly lake-breeze wind-shift positions
rOPJ
Figure 9. Hourly positions of lake breeze front of August 13, 1967,
with the ground track of the NACR Queen air plotted. Hygrothermograph
traces at several distances from the shoreline are included. Surface
water temperature is 67° F. One full wind barb equals 5 knots. From
Lyons and Olsson 1972.
The primary impact of this system is to recompose a coastal monitoring
scene into at least two siting domains: one area subject to the land/sea
breeze effects, another outside of this influence. The size and extent of the
land/sea breeze-affected subarea can be assessed in a number of ways. An
obvious factor of contrast is the horizontal distribution of wind directions
on appropriate days; however, few areas have sufficiently detailed meteorologies
networks to define the horizontal extent of the area and the change in size of
the affected area with time. A more reasonable approach is to use air tenperati
and relative humidity patterns to characterize this effect. Figure 9 displays
distinctive signatures in hygrothermograph recordings and suggests a method of
analysis that may be helpful.
"34
-------
Small Scale Obstacles-
Wind deflection around and over obstacles is a concern in selecting
specific sites in an urban area, because the effects occur on the microscale.
As shown in Figure 10, air does not simply slip past an isolated structure.
There are three distinguishable zones of air around a building:
1. Displacement zone—where streamlines are deflected upwind
and outward, remaining so for some distance
2. Wake zone—where streamlines gradually recover original
configuration
3. Cavity zone—return flow in the immediate vicinity of the
downwind side.
Displacement
/
*
1
Hb
r
vt^V
Wake ^S.
// / 4/ / / /// ///III//
Iv"
lOHb
Figure 10. Flow zones around a building
In terms of site selection, this effect is of obvious importance if an
intervening obstacle contains a strong enough source to generate a ground-
level impact that would be assigned to a source further upstream—particularly
if monitoring were to unwittingly take place in the cavity zone. This effect
is further complicated when many such obstacles are placed together, as shown
in Figure 11.
35
-------
NiMTERMITTEMT
como
VORTTCSS
Figure 11. Flow characteristics among multiple buildings.
Urban Effects—
In addition to the effects of individual buildings, a city induces
large-scale modifications to the local wind field. These modifications have
a bearing on site selection, due to the heat island circulation.
When a heat island circulation exists, there is a convergence zone over
the center of the city and a return flow into outlying areas, as illustrated
in Figure 12. This circulation pattern is most pronounced at night when
differential radiative cooling rates favor higher temperatures in the urban
center. The circulation pattern is generally weaker during the day when
urban/rural thermal contrasts are not as strong. Table 7 summarizes the
general magnitude of key heat island circulation elements.
Inversion
Cold
s/ss / /
Figure 12. Idealized urban heat island air flow (After Landsberg 1975)
36
-------
TABLE 7. ESTIMATES OF THE ST. LOUIS, MISSOURI, HEAT
ISLAND CIRCULATION
Element General magnitude
Urban/rural temperature difference >_2°C
Gradient wind (900 mb) ^5 m/sec
Average surface wind 2 m/sec
Average vertical velocity 0.3 m/sec
Diameter of surface inflow 30 km
Diameter of updraft 7 km
Depth of circulation 1 km
Source: Landsberg.
Under sufficiently strong winds, the heat island circulation is over-
whelmed. Oke and Hannel (1970) have developed a simple relationship between
the threshold wind speed to prohibit the circulation and relative city size.
Oke and Hannel's empirical formulation is as follows:
U-Hm = 3.4 LogP-11.6
sre P is the population number. Thus, a large urban area whose population
counted in the millions can exhibit a heat island circulation even if
where
is
regional winds are quite strong. Although- this relationship showed a high
correlation (94 percent variance explained) for the cities studied, it should
not be treated as an absolute measure. Each urban setting will have its own
idiosyncracies due to local terrain, presence of water bodies, or other
factors.
C1imatology
Regional dispersion climatology encompasses those atmospheric parameters
of regional scale influence that affect the distribution of ambient concentra-
tion. The parameters of primary concern are advection, dispersion, and
vertical mixing. With the exception of advection (i.e., surface winds), the
instrumentation to acquire direct measures of these parameters are generally
not found in most settings. Even when relevant measurements are available,
the important fine structure needed to characterize significant air pollution
37
-------
transport 1s generally not observed (e.g, Hewson 1976; Holzworth 1974; and
McCormick and Holzworth 1976). Nevertheless, it is important to consider
what regular data are available to estimate advection, dispersion, and
vertical mixing. Additional parameters needed for air quality simulations
are also considered.
Advection—
For most monitoring objectives, advection is adequately defined by the
near-surface wind (speed and direction) measured at (or adjusted to) a '
reference height of 10 meters above the ground. Useful observations may
consist of short-term averages taken hourly or every 3 hours, as well as true
algebraic or vectorial averages over these time intervals. Nearly continuous
recordings are sometimes available.
Directional air flow is an intuitively appealing siting tool. One of
the most useful summary depictions is the wind use that expresses advection
in terms of relative frequency of occurrence by direction, usually with a
breakdown of wind speed by classes within each directional interval. By
convention, a wind direction denotes the sector from which wind is blowing.
Wind roses may be compared on an 8-point basis, a 16-point basis, or a
36-point basis.
The most common summary wind roses are compared for annual, seasonal, or
monthly distributions (see Figure 13). Under some circumstances, wind roses
are devised to study winds under critical conditions. For example, STAR1
summaries offer a joint frequency distribution of winds and atmospheric
stability. These are available from the National Climatic Center and may be
compared for various time periods. Additional categories of wind roses
include winds under important pollutant index levels, distribution of persistent
24-hour winds, and distributions within key parts of the day (i.e., morning
versus afternoon).
Dispersion--
Dispersion is the summary effect of atmospheric turbulence in actively
diluting source material. Direct measurements of the three-dimensional wind
fluctuations that manifest turbulence are rarely made. Instead, various
methods of characterizing turbulence based on theoretical and empirical
relationships are employed. The most common system is based upon associations
among wind speed, solar insolation, and cloud cover, as shown in Table 8.
Many operational models accept this type of data directly, and manual techniques
have evolved to treat these as well (see Turner 1970).
1 STabil ity A_Rray, a broad-based algorithm for determining stability in the
lower atmosphere using estimates based on winds and cloudiness. See Doty,
Wallace, and Holzworth 1976.
38
-------
w>
U
VI
2
CO
u
39
-------
TABLE 8. DISPERSION CLASSIFICATIONS (PASQUILL 1961)
Night
Surface wind
Insolation
Thinly overcast
apccu au AU HI
(m sec"l)
2
2-3
3-5
5-6
>6
Strong
A
A-B
B
C
C
Moderate
A-B
B
B-C
C-D
C
Slight
B
C
C
D
0
Ul -"t/O
1 ow cl oud
-
E
0
D
D
<3/8 cloud
-
F
E
D
D
Mixing Height-
Mixing height defines the vertical extent of mixing. Ground-based and
low-level inversions are the principal limiting factors. Mixing height is
determined from a thermodynamic analysis of vertical temperature soundings.
These soundings are routinely performed at 0000 GMT and 1200 GMT each day at
a number of stations. Contact National Climactic Center (see Appendix A) for
a list. Additionally, climatological summaries are also available (see
Holzworth 1972).
Other Parameters—
Additional parameters that may be useful are listed below. Routine data
sources are summarized in Appendix B.
• Solar radiation—for estimates of formation rates of
secondary aerosols
• Visibility—as a proxy for regional scale impacts
• Precipitation—to relate to scavenging processes
t Air temperature—to be applied to plume rise estimates, or
as a fine adjustment to residential space heating demand
as a proxy for some combustion sources.
40
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TAXONOMY OF REPRESENTATIVE SITES
By classifying monitoring objectives and monitoring sites, it is
possible to categorize all monitoring requirements into discrete groupings.
Siting methods that are appropriate to each group or to several groups can
be more easily identified. Furthermore, some groupings may be of little
interest and need not receive further attention.
In the preceding section, spatial scales of areas were defined within
which air quality levels are reasonably homogeneous for typical organizations
of human structures and activities that characterize each scale. These
definitions were very general. The physical characteristics that primarily
contribute to variations in air quality include sources of emissions, types
of terrain, and types of meteorological influences. Each of these charac-
teristics and the nature of the variations that affect air quality levels
have been previously discussed.
For the purpose of classifying representative siting situations with
respect to PMjo, the following three categories of sources of emissions are
of interest:
• Background or general region
\
• General urban or industrial area
- Homogeneous
- Complex
• Major source within an urban area
• Isolated source.
With respect to terrain influences the following categories of topograph-
ical features are of interest:
• Plains
• Coast
• Ridge and valley
• Irregular terrain
- Extremely rough
- Moderately rough
• Urban.
41
-------
Although mixtures of the above terrain influences are possible, it is unreal isti
to attempt to characterize such complex influences within the scope of present
modeling and analysis methods. For monitoring planning purposes, it may be
best to incorporate the single most important influence into the analysis.
With respect to meteorological influences on air quality levels, there
are two important categories of features that have been frequently cited as
being important in creating poor air quality levels. These categories are
(1) stagnation situations with limited vertical mixing and little advection
for prolonged periods and (2) persistent winds in which pollution from a
source is consistently transported to the same location for a prolonged
period. The following categories of meteorological influences are of interest:
• Frequent air stagnation conditions
• Frequent persistent winds
• Normally variable meteorological conditions.
For PMio ai'r quality levels, there are two averaging times of interest:
24-hour and 1-year. The pattern of effects associated with these two averag-
ing periods may differ, in that shorter term effects usually occur closer to
the source than do longer term effects.
Based on the above factors, there are 120 possible representative siting
situations consisting of all the following combinations:
4 classes of sources
5 classes of terrain
3 cl asses of meteorol ogy
2 classes of averaging times.
However, for the purpose of identifying methodologies to use in determining
siting needs, the same approach is applicable to many of the combinations.
One need not use different approaches to treat different averaging times.
Also, the meteorological influences are associated with the influences due to
terrain and need not be treated as independent factors. Eliminating time and
meteorology reduces the number of combinations to 20. With regard to air
quality levels associated with background or distance sources that affect a
general region as a whole, variations in terrain are not important. The
concentrations of PM^g will be homogeneous over large areas and not affected
by terrain variations. Siting methodologies are limited to simple situations
in which a single dominant terrain is identified. At the present time,
pratical methodologies have not been developed for treating multiple sources
in other than simple terrain situations. Practical models for treating
coastal, ridge/valley, and irregular terrain for general urban sources or a
major source in conjunction with general urban sources are not presented
here. These two source categories are not applicable to the terrain type,
leaving only the urban terrain situation. This leaves the terrain variation
being treated only with respect to isolated sources.
42
-------
There are only two terrain situations applicable to isolated sources
since the isolated source with urban terrain is the same case as a major
source within an urban area. This results in four categories of sites.
Because of the range of alternative configurations of sources in urban areas,
two categories are included, which may be designated complex and uniform.
As a result of these considerations, we have defined the following six
representative siting situations for which specific guidelines are presented
in the next section:
9 Regional scale (1)
9 General urban area
- Complex (2)
- Uniform (3)
o Major source within urban area (4)
a Isolated source
- Plains (5)
- Irregular terrain (6)
43
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SECTION 5
SITE SELECTION METHODOLOGY
The general procedure recommended for selecting sites for monitoring
is similar to that followed for monitoring any pollutant. Variations
are recommended primarily with regard to specific methodologies or data that
are needed for different topographical situations or different configurations
of emissions. Procedures are discussed and recommendations are given for
treating the six representative siting situations identified for PM^Q ln
Section 4.
OVERVIEW OF METHODOLOGY
The siting of monitors is part of a continuing planning cycle for moni-
toring, which goes on in all air pollution control agencies and operating
facilities. The three basic elements of the cycle, as shown in Figure 14,
include defining the objective of monitoring, collecting monitoring data, and
making judgments about air quality levels. The methodology for selecting
monitoring sites.is designed with the idea that this is part of an iterative
process that has been performed before and will be repeated again in the
future. The need for flexibility in the use of monitoring resources was
clearly recognized by the Standing Air Monitoring Working Group (EPA 1977).
This need has resulted in the development of three types of monitoring
activities by state and local agencies, including National Air Monitoring
Stations (NAMS), State and Local Air Monitoring Stations (SLAMS), and Special
Purpose Monitoring (SPM). The locations of NAMS and SLAMS must be coordinated
with EPA regional offices because these must be designed to meet EPA needs in
addition to state and local needs. The siting methodology is applicable to
all three types of monitoring stations and will be useful to industrial
operating facilities as well as air pollution control agencies.
The general site selection process is illustrated in Figure 15. The
procedure is applicable"to all PM^o siting requirements, although the
indicated steps may be considerably simpler for some types of monitoring
requirements than for others. Each box shown in the diagram defines a
data review and analysis step. The diamonds define decisions, and the
rounded boxes define data needs. The process is divided into the following
six steps, which are performed in sequence:
1. Analyze existing PM monitoring data
2. Review local situation to determine adequacy of mapping
analysis and/or to select a modeling procedure
3. Model air quality scene (if necessary)
4. Determine network requirements
44
-------
Figure 14. Planning cycle for monitoring.
45
-------
f \ :
! Monitoring | _
1 data j *"
i
Review PM j
monitoring data I
i
> Yes »_
. ,
Analyze
PM data
Are
data sufficient
for mapping
analysis?
missions and
topography
data /
Determine network
requirements
(numbers and locations)
Select monitoring
sites and
placements
Emissions and
topography
data
Figure 15. Procedure for selecting PM^g monitoring sites.
46
-------
5. Determine monitoring sites and placement
6. Document and update site exposure experience.
Site planning may vary in scope of responsibility and may include any of
the following:
• Design multipurpose network
• Supplement existing network for specific purpose
• Design single-source impact or compliance monitoring
network
t Monitor a designated area or location.
Guidelines for performing each step in the site selection process and
variations that deal specifically with each of the six types of siting
situations are described in the subsequent subsections.
ANALYZE EXISTING AMBIENT PM MONITORING DATA
In order to devise a monitoring strategy and select monitoring sites,
the monitoring planner must hypothesize the historical spatial distribution
of PM]_Q concentrations over the area of concern. An adequate data base of
related measurements, such as for TSP matter, may be available to meet this
need. If not, the distribution must be estimated by mathematical simulation
modeling or by a reasonable, physically based qualitative analysis. The best
method of estimating the distribution of air quality levels will depend on
the amount, type, and quality of available information. The information of
interest includes the following categories:
• Suspended particulate matter measurements
• Locations and amounts of partial!ate emissions
« Air pollution climatology and meteorology data
• Maps of topographical features.
As a general rule, the amount of monitoring data available to help
design a monitoring network or site new monitors is either nonexistent or
very incomplete. However, with regard to siting new PMjQ monitors, there is
likely to be a wealth of hi-vol monitoring data for TSP concentrations that
can be very helpful. Other relevant ambient PM measurements include IP
measurements, tape sampler measurements, and various types of direct and
47
-------
indirect PM measurements. The EPA SAROAD data base, available from EPA
regional offices, is a convenient source of much of the available data.
State and local air pollution control offices are also important sources of
additional data and information about other data that may have been collected
by nongovernment parties or in special studies.
After assembly of all available data and elimination of data that are
suspect because of poor quality control, a decision is made as to whether the
available data 1s sufficiently dense to justify mapping analysis, or whether
single-station analysis 1s more valuable. Generally, unless measurements are
available from at least six sites concurrently, mapping analysis is not
practical.
Mapping Analysis
When performing mapping analyses, different types of measurement data
should not be mixed on the same map unless an adequate calibration correction
is made for different types of data. If corrections are to be made, it
would be convenient if the different types of measurements were corrected to
estimates of PM^g concentrations. As a minimum, two types of maps should be
constructed, including one for annual means and one for peak 24-hour concentra-
tions (not concurrent) for each year of data, particularly the most recent
years. In addition, it will be useful to plot concurrent 24-hour data for a
few days that are distinguished by having one or more high values. The maps
may be constructed by locating the observing sites on a convenient mapping
display. The appropriate values may be entered at each site to provide a
guide for drawing a set of representative contours of concentrations. The
number and value of contours to be drawn will depend on the range of values
observed and the nature of their spatial distribution. Computer graphics
packages are available to perform the contouring analysis if manual analysis
is not practical. Generally, about six contours will provide a useful
display. However, as few as one or as many as 10 may be appropriate,
depending on the magnitude of the range relative to the mean of the values
observed. The maps will be used to identify representative spatial scales
and preliminary siting selections.
While the mapping and station analysis data may be helpful in identifying
the spatial distribution of PM^o, they may be inadequate. Having analyzed
the available data, the monitoring planner must consider whether modeling is
needed to supplement the available monitoring data. Consideration should be
given to gradients evident in the observations, locations of major sources,
terrain, and meteorology. In most cases the available PM observations will
not be adequate for planning a new monitoring network.
Single-Station Analysis
When single-station analyses are performed, it is desirable to identify
the significant influencing factors that affect the PM^g air quality levels
observed. This identification process will help determine how wide an area
48
-------
the station represents. Conclusions drawn from one station should be compared '
with results from other stations in the area of interest. Trends and frequency
distributions help in analyzing single-station data. Case study analyses of
peak values will also be helpful. Figure 16 shows an example of 12-month
running means for three sites in Youngstown, Ohio. When significant trends
exist, they may indicate the influence of a nearby source. This would be
especially true if trends at one site are more pronounced than at other sites.
The down trends at the three Youngstown stations might be attributed to
decreasing steel production in the local area. The differences among the
stations might be attributed to the locations of sites relative to steel
production areas and the prevailing wind directions. Shorter averaging
periods, such as 3-month averages, would be helpful in identifying seasonal
variations that might be associated with specific sources or meteorological
conditions.
An example of statistical analysis of single-station data is presented in
Table 9. Locations that have similar frequency distributions, particularly
over a period of several years, can be considered to be in homogeneous areas.
To further support the identification of homogeneous areas, it is useful to
review meteorological conditions associated with a selected range of high
values. Because JSP measurements represent 24-hour values, a good deal of
care is required in selecting meaningful meteorological values. The prevailing
(most frequent) and the range of wind directions corresponding to the measurement
period are useful. Wind persistence (ratio of vector mean to scalar mean wind
speed), height and magnitude of nocturnal temperature inversion, scalar
average wind speed, and range of Pasquill stability categories (see definition
in Turner 1970) are other meteorological parameters that may show consistent
values with the high TSP measurements. If the meteorological conditions
associated with high measurements differ significantly between monitoring
sites, this result indicates that the sites represent different zones of air
quality and has an important bearing in planning a monitoring network.
Another useful single-station analysis is the pollution rose. Figure 17
shows pollution roses constructed for four sites near a coking plant. The
pollution rose is constructed by computing the average measured concentration
for all values when the prevailing wind was in a given direction. The values
may be limited to days when the wind persistence index (ratios of vector to
scalar wind speed) exceeds a certain value. In Figure 17, the data include
only days with a wind persistence index equal to or greater than 0.85.
REVIEW OF LOCAL SITUATION
An important step in the process of selecting monitoring sites is to
identify the unique local influences that are affecting air quality. The
types of topographical features, the magnitudes of PM emissions, and the
locations of both with respect to one another have a major impact on where
the worst air quality levels will occur. In assessing the value of available
monitoring data and in selecting an air quality simulation model, it is
necessary to take these local influences into account. After a brief
49
-------
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50
-------
TABIZ 9. TSP DATA SUMMARY FOR SAROAD STATION *391~20001
(Units in micrograms/m3) (Pickering, Vilardo, and Rector 1981)
y i *
1 9 7 3 I?"741 i a 7 : 1975 1977*197?
s 0- 3C4DIN3S : 59 81 95 109 113 110 113 7
3i01£TPi: *£A\: 122.5 11*. 9 139.2 90.2 99.2 =5.9 =! : . 0 a I .
r,£01£TPIC S.O.: 1.* 1.6 1.6 1.3 1.7 1 n l.*5 1.
HIGHEST 2Y
LASS^ -rxTR^: 635.4 460.4 406.4 326.1 440.3 431.4 235.5 319.
IST HIGHEST: 496.0 2^6.0 214.0 259.0 275.0 3ss.o 273.0 35S.
3ATi : 73C416 740117 750413 760915 770310 780*26 790322 80053
2V 3 HlGHiST: 339.0 281.0 277.0 204.0 "231.0 237.0 22S.O 1=4.
^4TI : 7304?^ 74050S 750924 780924 77110S 731122 791123 500SO
3 OF RTAOI
rxCriDING 260 : 443011
3 Oc HEADINGS
rxc:r:iv3 153 : 24 20 22 17 27 19
0 - 65:
=6-130:
i !i-i 35 :
196-250:
251-325:
325-390:
3 = 1 -•"55:
>«55: .
9
20
20
s
2
1
0
1
11
37
22
7
-
0
n
0
15
48
20
9
3
0
0
0
29
54
23
3
0
0
0
T
25
49
32
S
1
0
c
0
27
48
27
7
0
1
0
0
36
S2
11
3
1
0
0
0
1 :
-------
LEGEND
Average TSP concentration at
center location when wind is
from direction of arm; each
circle represents 50 ug/m^
Defines river
Defines river valley
Defines location of major PM sources
610 :
_J
Figure 17. TSP roses for four sites near a coking plant (Pickering, Vilardo, and Rector 1981).
52
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description of the information needed, suggestions are given for steps to take
in evaluating available air quality and for estimating PM^o air Quality levels
by the use of mathematical models.
Emission Data
Information on the locations and magnitudes of sources of particulate
matter emissions is needed. The influence of PMio sources can be determined
by the use of air quality dispersion models and graphical aides that treat
the contributions of sources to receptor locations, and by qualitative
interpretation of the model results in the light of known topographic influ-
ences and monitoring data. Available sources of data and how they may be
used in monitor siting analysis is provided here.
Two useful items of information are a detailed and accurate land use map
and an accurate point source emission inventory. Large-area, statewide, or
multistate maps are needed to show the locations of major population and
industrial areas. Smaller area maps that show the size and location of
different types of urban development within a single city are also needed for
most monitoring objectives. There are many sources for the large-area maps.
City-size land use maps are usually available from city and county planning
offices. U.S. Geological Survey maps or Sanborn maps may be useful if other
sources of land use maps are not available. Another very useful source of
data on land use is the U.S. Geological Survey's records of aerial photographic
coverage and space imagery. Reference files of data available on microfilm
are maintained at the EROS Data Center of the U.S. Geological Survey in Sioux
Falls, South Dakota. (See Appendix B for recommended contacts.)
Detailed information on specific sources of particulate emissions is
available in state and local emission inventories. Both area and point source
emission data are needed. Area source emissions are typically estimated
on a countywide basis. However, estimates are frequently allocated to a fine
grid in order to provide inputs to dispersion models or for other purposes.
Gridded area source data that include location, emission rate, and stack
parameters (e.g., temperature and volume flow rate) are needed. When accurate
and complete, the NEDS data available from EPA include peak and average
emission rates and seasonal variations in addition to the minimum information
on location and emissions.
In addition to the emission inventory, census data and traffic data may
be used to help define the spatial distribution of particulate emissions,
particularly emissions associated with fuel combustion for space heating and
emissions from vehicle kickup and tailpipe exhaust. If seasonal variations
of emissions due to space heating are not available, they can be estimated on
a seasonal or daily basis by use of degree days.1
1 A degree day is the amount that the average of the daily maximum and
minimum temperatures is less than 65° F. Days on which the average is 65C
or greater are not counted.
53
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Emission data for parti cul ate matter are most complete and most accurate
for stack emissions from large point sources. However, the principal sources
of PMio concentrations are fugitive emissions, secondary particles, and
emissions from automobile exhaust (Watson, Chow, and Shah 1981). Special
attention is needed to ensure that the emission inventory is reasonably
accurate with respect to industrial material handling operations, fumes from
uncontained processes, mechanically reentrained road dust (both paved and
unpaved roads), and windblown dust from disturbed soil, or a variety of
industrial sources (Pace 1980).
Topography
The topography of an area will affect the transport and dispersion of
pollutants released to the atmosphere. It is important to take note of
topographical features in evaluating how adequately monitoring data represent
the expected air quality levels and In selecting a modeling approach for
simulating air quality levels. The following topographical features are of
interest:
• Shorelines of major bodies of water
• Boundaries of significant urban areas (primarily covered
by buildings and pavement)
• Significant terrain elevation features, including ridges,
valleys, and areas of complex terrain.
The influence of topography on atmospheric transport is discussed in
Section 4. The location of air monitoring sites in relation to sources of PM
emissions must be reviewed in the light of these influences. An air pollution
meteorologist may be consulted regarding the significance of topographical
effects, if there is a doubt about the effect.
The locations of these features are easily identified on topographical
maps available from the U.S. Geological Survey.
Reviewing Local Effects
Having assembled data that describe the local situation with regard to
measurements of air quality, sources of emissions, meteorology, and terrain,
the monitoring site planner is ready to assess the nature of these influences
and determine whether to use modeling or qualitative analysis for assistance
in selecting monitoring sites.
54
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With regard to sources of participate emissions, it is necessary to
identify the locations of major sources and the quantity of emissions emanating
both from stacks and as fugitive dust. Smaller sources of particulate emissions
may be represented as area sources, e.g., as emission densities over 1 km
squares. The area source emission densities should include particulate
emissions from fuel combustion by smaller commercial and industrial sources,
by residences, and by all types of mobile sources; also, process and fugitive
dust emissions from industrial, waste disposal, and construction operations
should be included. Guidelines on how to conduct an emission inventory and to
allocate emission data to a gridwork are available from EPA (1973) and are not
documented here. Both annual mean and seasonal, monthly, or daily maximum (if
they are significantly different from the annual) emission rates should be
determined. When plotted on maps, the area emission densities (both mean and
maximum) will indicate areas of relative maximum and minimum emission levels
and the degree of homogeneity in the area source emissions over the monitoring
area of interest.
The nature of major topographical features and their locations relative
to the sources of particulate emissions need to be identified. Major topo-
graphical features include coastlines, ridge lines, valley walls, and hilltops.
In addition to specific topographical features, the area may be generally
characterized by its roughness, e.g., built-up urban area, moderately rough
rolling hills or river valley, or extremely rough valleys and ridges of a
mountainous area. The treatment of terrain roughness is further complicated
by the need to deal with terrain transitions. Cities and other areas of
interest are frequently located near the base of a mountainous area or on a
coastline where major terrain transitions exist.
While the location and nature of terrain features help to identify their
influence, meteorological data are the demonstrated evidence of the effect.
All of the air quality models recommended in the EPA Guideline on Air Quality
Models (Revised) (1986) assume that meteorological conditions are homogeneous
between all combinations of sources and receptors. Therefore, the available
meteorological data should be reviewed to delineate areas and time for which
the homogeneity assumption and the recommended models are applicable.
The single most significant meteorological parameter that must be
homogeneous is wind direction. Since wind direction at a single site is
generally accurate within 10° azimuth,2 the variance in wind direction dif-
ferences between sites should not exceed the sum of that variance due to
measurement errors at the two sites. A useful rule of thumb is that the
standard deviation of the differences in wind direction at two sites should
not exceed V2 times 10°, or be less 15°, if the two sites are assumed
to be measuring the same wind direction.
2This is related to the spatial representativeness of the observations and
not the accuracy of the wind vane.
-------
If meteorological data are not available to demonstrate the homogeneity
of meteorological conditions, one can require that there be no major topographii
features between sources of pollution and potential receptor monitoring sites
in areas selected for modeling analysis. While this may be helpful in the
immediate area, it does not treat indirect effects in nearby areas due to wind
flow away from major topographical features. Lake breeze fronts and valley
drainage flow fronts are examples of air boundaries that lie away from the
topographical features that generate them. Winds on opposite sides of these
air boundaries may differ by 90° or more, and the boundary may lie several
miles away from the terrain feature. Air quality models that treat the
effects of these terrain-generated air boundaries are under development and
evaluation. One important effect of these boundaries, namely limited vertical
mixing, can be treated by the available models.
Is the Analysis of Monitoring Data Sufficient?
The patterns and directions of maximum levels may differ for long- and
short-term PM^o concentrations. Both types of patterns should be reviewed
separately. The important judgment to be made is whether the effects shown
by the monitoring data are reasonable in the light of other available infor-
mation, or whether modeling is needed to better define the spatial pattern of
concentrations.
In order to be useful for siting purposes, the monitoring data should
define the shape and magnitude of the air quality pattern. Based on the
distribution of sources, topography, and meteorology, the pattern should
reflect these influences or at least not be inconsistent with respect to
them. If these expectations are met, one may accept the pattern shown by the
monitoring data as adequate. If the expectations are not met, a more detailed
analysis based on results from air quality simulation models or from supplemen-
tary mobile monitoring may be required. There are two types of comparisons
that can be made to help judge whether the air quality patterns are acceptable.
One comparison examines the time history of the pattern. The other comparison
examines the shape of the air quality pattern with respect to the shape of
the pattern of emission densities and topographical features.
If the patterns of annual means or maximum 24-hour concentrations for
several years show the same shape and same locations of peaks when superimposed
on each other, the pattern is consistent with time. This consistency is
evidence of a stable pattern, which is a reasonable guide for planning monitorinc
sites. If the pattern is changing with time, the analysis may be adequate,
but the reasons for the changing pattern should make sense in terms of changes
in sources or in meteorological conditions. If there are no apparent reasons
for the changes, modeling results should be obtained and reviewed.
Emission densities that are chronologically consistent with the air
quality data should be plotted and used to generate contour patterns.
Topographical features may also be located on these patterns. When the
emission density contours are superimposed on the air quality patterns, there
56
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should be a reasonable relationship. One possible cause of deviations might
be due to significant amounts of emissions from stacks. The heights of the
stacks should be noted as an aid in identifying this influence. As a general
rule, most IP and TSP emissions are from ground-level sources; however,
uncontrolled or undercontrolled emissions from stacks can be major sources of
pollution, which significantly alters the pattern of air quality from what
would De observed from ground-level sources. A reasonably consistent pattern
would be one in which the air quality pattern is offset from the emission
pattern in the direction of prevailing wind flow. If the influence of major
peaks in emission density are not evident in the air quality pattern, a
modeling analysis may be helpful in identifying the magnitude of the pattern
deformation that can be expected.
Selecting a Model
Major unsolved problems are associated with modeling PM concentrations.
When using the results of model simulations to select monitoring sites, one
snould keep the following uncertainties in mind:
0 Most of the IP matter that makes up the concentrations
occurring in urban locations may not originate from
local sources.
0 Air quality simulation models recommended in the Guideline
(EPA 1986) do not treat the physical and chemical
processes that alter the size of airborne particles and
may not adequately treat their removal by wet and/or dry
deposition.
0 Emission factors and emission data that are available to
estimate emissions of particulate matter do not identify
IP emissions as a portion of total PM emissions.
0 Most IP emissions originate from fugitive sources rather
than stacks. The uncertainly associated with available
fugitive emission estimates is very high.
J Air quality simulation models recommended in the Guideline
(EPA 1986) very simplistically treat the topographical
influences on atmospheric transport and dispersion of
pollutants.
in spite of tnese uncertainties it is still useful to use modeling to
identity areas of relatively good and poor air quality and to select sites
for a monitoring network. Models that may be useful in each of the six
monitoring situations described at the end of Section 4 are listed in
Taole ID. No modeling results are needed to site a regional scale monitoring
station, because this type of site is representative of a large, relatively
nomogeneous area of air quality in which influences from nearby sources are
57
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TABLE 10. AVAILABLE EPA MODELS FOR SIX MONITORING SITUATIONS*
Monitoring Situation
Recommended model
Annual Mean Maximum 24-hour
Regional scale
General urban area
-- uniform
-- for complex sources in urban areas
Urban area with single or multiple major
IP source(s)
Single source with terrain height below
stack top# (complex source)
Single source near terrain above stack top§
None**
CDM-2.0
ISC
CDM-2.0
CRSTER
COMPLEX I***
or VALLEY
None**
RAM
ISC
RAM
CRSTER (ISC
VALLEY or
COMPLEX I**
* Available on EPA's UNAMAP Version 6.
# For multiple sources where it is not appropriate to consider the emissions
as located at a single point, the MPTER model is appropriate.
§ COMPLEX I and VALLEY are considered screening techniques. For regulatory
purposes, COMPLEX I snould be used only with onsite meteorological data as
i nput.
** Selection of model is a case-by-case decision.
*** The SHORTZ model is an appropriate screening technique for use in urbanized
valleys with onsite meteorological data as input.
58
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negligible. With regard to selecting a model, a distinction is made betwen
monitoring situations with a single source in a rural setting and monitoring
situations with multiple sources in an urban setting. A distinction is also
made between rural monitoring situations with and without complex terrain.
For modeling purposes, complex terrain is usually defined as terrain that
exceeds the stack top of the source.
For estimating annual means, the COM model is appropriate for multiple
source urban situations, and the CRSTER model is recommended for single-source
rural situations in the absence of complex terrain. In the presence of
complex terrain, the COMPLEX I screening model for rural areas and the SHORTZ
screening model for urban areas (available in the EPA UNAMAP Program System,
Version 6) are more appropriate than VALLEY, if at least 1 year of onsite
meteorological data are available. These models are relatively easy and
inexpensive to use. For estimating maximum 24-hour concentrations, the RAM
model is recommended for urban situations and CRSTER for single-source, rural
situations. When the single source or multiple major IP sources are complex
(as is frequently the case when treating fugitive emissions from large
industrial sources), the ISC model is recommended in place of RAM or CRSTER.
Procedures for using these models and for compiling data for them are
discussed in detail in the Guideline on Air Quality Models (Revised) (EPA 1986),
and the PM^Q SIP Guideline.In addition, Appendix A contains a list of cities
for which STAR data have been compiled. These data should be helpful to
modelers who wish to execute COM or ISCLT. Appendix B contains a list of
information sources that should also prove helpful.
Selecting Representative Sites Without Monitoring or Modeling Data
There may be situations in which it is not possible to use monitoring
data or the results of a modeling analysis to define the pattern of air
quality levels in an area that is to be monitored. In this case, the moni-
toring network can be planned by identifying representative sites on the
basis of availaole information on sources of emissions, climatological data,
and topographical considerations. Section 4 presents a discussion of how
these physical characteristics of the area to be monitored influence the air
quality with respect to PM^Q. On the basis of these considerations, six
representative monitoring situations were identifeid. Observations from
other locations and previous modeling analyses of general classes of source
influences may be used to select PMjy monitoring sites for these situations.
Figures 18 tnrough 21 summarize the steps that need to be followed in
selecting sites for the six types of representative monitoring situations.
Figure la treats regional scale siting. Figure 19 treats siting neighborhood-
scale sites in urban areas, and Figure 20 treats siting middle scale sites
with and without the presence of major point sources. These two figures cover
the three urban representative siting situations identified in Section 4.
Figure 21 treats siting around an isolated major point source in flat or
59
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Locate and characterize:
Major urban areas
Major point sources
Wind direction frequencies
Major terrain features
Determine number
of sites required
Select site(s) using source
avoidance and wind direction
frequency considerations
Modify site selections based
on topography considerations
Figure 18. Steps for locating regional scale monitoring site.
60
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Are there
any major
point
sources?
Yes
Locate and characterize effective
height of major point sources
Determine distance of maximum
impact from each major source
Determine locations of overlapped
effects from multiple point sources
Assemble and analyze data on highway
traffic, major indirect sources, urban
development, and wind direction frequencies
Determine number of neighborhood
scale monitors
Divide area into neighborhood and
select neighborhoods to monitor
Select sites in each neighborhood
not influenced by major point sources
Select monitor air inlets that are not
shielded by structures or affected by
adjacent local sources
Figure 19. Steps for locating a neighborhood scale monitoring site in an urban ar
61
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Are there
any major
point
sources?
Yes
Locate and characterize effective
height of major point sources
Determine distance of maximum
impact from each major source
Determine locations of overlapped
effects from multiple point sources
Assemble and analyze data on highway
traffic, major indirect sources, urban
development, and wind direction frequencies
Determine number of peak
concentration monitors
Select sites on downwind side
of major point and/or indirect
sources on downwind side of urban
area in maximum impact zone for
most prevalent wind direction
J_
Select monitor air inlets, that are not
shielded by structures or affected by
adjacent local sources
Figure 20. Steps for locating micro-/middle scale monitoring sites in urban areas
62
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Assemble and analyze emissions,
clinatological, and topographical aata
Determine zones of maximum imoact
based on climatology
re
there any
topographical
influences?
Determine zones if maximum
imoact based on
tooograpny
Determine number of monitoring
sites for monitoring background,
maximum impact, and sensitive areas
Select sites in potential maximum
impact zones that are not shielded
by vegetation, terrain, or structure
Yes
Select in sensitive areas
that *r« not smelaed Sy vegetation,
terrain, or structure
Select background site
as suggested in M'gure 18
Figure 21. Steps for locating monitoring sites
near isolated major sources.
63
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complex terrain. This includes two of the representative siting situations.
These three figures deal with all six representative siting situations.
Specific guidelines that may be used in performing these steps are discussed
below.
Regional Scale Monitoring Sites
Regional scale monitoring sites are needed to measure background levels
of PMio that are transported into the area being monitored. It is important
that regional scale monitoring sites not be affected by nearby sources, which
would significantly alter their scales of representativeness, for large
periods of time. It may be necessary to use two or more sites to measure
background concentrations when a single site cannot be found that is never
influenced by nearby sources. Figure 18 suggests four steps to follow in
selecting the site(s).
The first step is to identify all major urban areas and all major
operating facilities that may have an effect on PM^o air quality levels in
the area of concern. Locations and populations of nearby urban areas are
readily determined from maps and standard library references. Large cities
as far away as 100 km are of concern. This is based on the use of models to
estimate the distance to which emissions of 1.0 wg/m^/sec from a metro-
politan area 40 km in diameter will extend before the peak concentration is
less than 20 yg/rn^ under neutral atmospheric stability conditions and a
light wind speed of 2 m/sec. Distances from smaller cities are less critical;
e.g., a concentration of 20 ug/m3 will extend 60 km downwind of a city that
is 20 km in diameter and 15 km downwind of a city that is 10 km in diameter.
These estimates were derived using the methodology for Estimation of Con-
centrations from Area Sources proposed by D.B. Turner (1974). A concentra-
tion of 20 ug/m^ is significant because this is the 1-hour concentration
that is likely to be associated with an observed 24-hour concentration of
5 ug/m^, and because 24-hour concentrations as low as 5 gg/rn^ are small
in comparison to observed variations in regional scale IP concentrations.
Annual mean concentrations of IP at 17 monitoring sites in nonurban areas
(Watson, Chow, and Shah 1981) showed a mean of 30 ug/nP and a standard
deviation of 9 ug/rn^. A concentration of 5 ug/m^ Is about half of the
standard deviation of regional scale or background level concentrations
of IP.
Major operating facilities can be identified from state emission inven-
tories that are available from state and Federal offices listed in Appendix B.
Estimates of significant impact distances are listed in Table 11 for
various emission rates and effective source heights. Effective source height
refers to the height above the ground at which the center of the plume of
emissions from a plant is transported. This includes the height of release
from a stack or vent plus the rise that may occur due to momentum and/or
heat in the exhaust stream. For fugitive emissions blown from the ground or
vented fron open windows and doors, the effective height may be essentially
zero or ground level. All areas affected by major sources can be circled on
64
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a map by a radius scaled to the significant impact distance. The circles
should include the urban area and major sources in the area being monitored
as well as nearby sources, outside of the area. Any areas not covered by
circles are suitable for regional-scale monitoring sites. Sites within 40 m
of major highways (see Figure 22) or unpaved roads are also not suitable.
This is because emissions from motor vehicles in heavy traffic and the reen-
trainment of dust from unpaved roads are also significant sources of particu-
late matter. If there are no uncovered areas or if the uncovered areas are
unsuitable because of accessibility or other considerations, it is necessary
to use more than one site to monitor the regional scale. Operations from
different sites would be applicable to background levels on different days.
TABLE 11. DISTANCES FROM MAJOR POINT THAT AFFECT REGIONAL SCALE MONITORS
Downwind distance (km) beyond which the product of
Emissions Effective concentrations and wind speed does not exceed
rate source 40 ug/m^ x m/sec for four Pasquill stability classes
(g/sec) height (m) B C D E
400
100
40
1C
4
all
300
£150
300
100
<70
>300
"100
<30
100
<30
14
7
7
4.5
4.5
4.5
<•»*
2.1
2.1
1.2
1.4
30
14
14
7
8
8
«•»
4
4
2.0
2.4
>100
33
50
M>
25
27
• M
8
10
— —
5
>100
*
>100
— —
50
57
— —
11
19
• •
9
* Dashes indicate values as high as 40 ug/m^/sec do not occur.
NOTE: 40 ug/m2/sec represents the lowest value that is expected to produce
a 24-hour concentration contribution of at least 5 ug/m^. This is
based on the assumptions that a 24-hour value will be about 25 per-
cent of the 1-hour peak concentration and that wind speed will be
2 m/sec. A concentration contribution of 5 ug/m3 is small in
comparison to variations in regional scale IP concentrations (see
text). Tabulated values are based on curves from the EPA Workbook
of Atmospheric Dispersion Estimates (Turner 1970).
65
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10.0-
M
A
S
S _
C^
CT.
0 «3
0.0-
-2.5-
20 40 60 80 100 120 140 160 180
DISTANCE(M)
LEGEND: SIZE
^/ if COARSE » » » FINE
TOTAL
Figure 22. Average measured PM concentrations (downwind less upwind)
from a major Philadelphia highway (Burton and Suggs 1982).
66
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When two sites are needed to monitor background concentrations, one
station should be selected that is upwind of the area of concern most fre-
quently or downwind least frequently. If this site cannot be clear of con-
tributions from nearby sources for all wind directions, a second site is
required. This site should be selected to supplement information obtained
from the first site to the maximum extent possible, so that one site or the
other is measuring the background level at all times. One strategy is to
place the second site in the direction that is upwind of the area of concern
second most frequently. If the first and second most frequent wind directions
are more than 120° apart, this may be a good plan. If they are less than 90°
apart, both sites may be downwind of the primary area of concern or of the
same large source on the same day. This risk can be minimized by selecting a
second site that has bearing from the primary area of concern that is 180°
from the bearing to the first site. A climatological wind rose showing the
frequency with which the wind blows in each direction is useful for selecting
sites. The map of circled major sources may be used to show areas that are
not affected by major sources for specific wind directions. Figure 23 shows
an example. In this case the monitoring agency must select a site within
24 km (15 miles) of its offices. However, the impact zone of the city (City A)
extends out 90 km, so the agency must monitor on both sides of the city. The
most frequent and second most frequent wind directions, shown in the lower
right-hand corner of'the figure, are about 120° apart. However, a site
directly south of the city is not desirable because of interference from
City 0. An alternative site slightly to the east of south would still be
representative for south winds and less affected by City D. Another alterna-
tive site is 180° from the direction for which the first site was selected.
Selected regional scale monitoring sites should not be influenced by topo-
graphical features. Sites along shorelines, in or at the base of pronounced
valleys, near sharp bluffs, or in low-lying areas should be avoided. The
topography around the most suitable sites is uniform.
Urban Areas with Mo Major Point Sources
Some urban areas will have no major sources of PM^Q emissions. Because
most of the measured IP concentrations come from geological materials, from
motor vehicle traffic, or from secondary aerosols formed in the atmosphere
(EPA 1981; Watson, Chow, and Shah 1981), this may be the situation in a
number of areas for which monitoring is planned. Figures 19 and 20 describe
steps that may be used to select monitoring sites in such situations.
The first step is to obtain and analyze traffic and urban development
data that can be used to identify potential variations in otherwise homoge-
neous neighborhood scale patterns of PM]_Q concentrations. Areas of high
traffic density, such as major highways, shopping centers, sports areas,
amusement parks, airports, and parking facilities, need to be identified and
analyzed. Also, areas that are concentrated sources of parti oil ate matter
emissions, such as solid waste handling facilities, unpaved roadways, central
business districts, and construction operations, need to be analyzed.
67
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30 kr
^Second Site—Alternate 2
24\km (15 mi)
(Xfsecond Site—Alternate 1
MOST
FREQUENT
KEY WIN
DIRECTlOi
SECOND MOST
FREQUENT
Figure 23. Example of background site selection within 24 km (15 mi)
of City A.
68
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Figures 24 through 26 show the model peak concentrations downwind of highways
that occur within 15 m of the roadway. Data in Table 12 show the peak concen-
trations expected downwind of other sources that are centers of intensive
traffic-generated emissions. These guides can be used to estimate where the
pollution increases above general neighborhood levels will occur, which can
be expected in the vicinity of these sources.
On the basis of the magnitudes of the PMjQ enhancement predicted for
all the traffic-concentrated areas and the locations of the source areas
relative to the downwind edge of the city for the most prevalent wind direc-
tion, a decision must be made on how many monitors will be used to measure
the maximum PM^Q concentration. Unless a single source or source area is
clearly more significant than any other, a number of sites should be selected
as potential peak concentration monitoring sites. These sites will be
representative of micro- or possibly middle scale areas. The monitoring site
should be located as close to the source as possible without infringement or
interference from the source. The most suitable sites are within 5 to 15 m of
the sources on the downwind side of the prevailing wind direction. It is
usually not practical to locate a site less than 5 m from a source. Generally,
one site is sufficient for each source area.
Neighborhood sites are needed to represent the areas that encompass or
surround the peak concentration sites. Due to variations in the type and
intensity of land uses throughout an urban area, a large metropolitan area
may be characterized by well over 1000 different neighborhoods. The process
of identifying and classifying all neighborhoods in a metropolitan area in
terms of their potential PM^o air quality levels is a worthwhile effort for
air pollution control planning purposes. The use of monitoring or modeling
data is the most satisfactory way to making such classifications. However,
it is also possible to characterize neighborhoods in a qualitative fashion
by preparing a detailed emission inventory that identifies the spatial distri-
bution of emissions from the many indirect and fugitive sources of
By examining the locations and magnitudes of these sources in relation
to the climatology of wind direction frequencies, one can rank neighborhoods
in terms of their expected levels of high PMjo concentrations. Neighbor-
hoods that encompass the middle or microscale areas that are expected to
contain high concentrations are clearly high priority neighborhoods for
monitoring sites. One or two neighborhoods adjacent to the maximum concentra-
tion neighborhoods are desirable secondary sites. A third category of
monitoring sites includes neighborhoods that are of special interest because
of large population density; because of rapid growth expectations; or because
of a highly sensitive population such as elderly (e.g., nursing home), ill
(e.g., hospital), or young (e.g., day care center).
Sites in the third category of interest may also meet the second category
of interest. There are no firm rules to determine how many sites to monitor.
Each monitoring jurisdiction must determine what its priorities are and how
far down the priority list of potential sites it is able and willing to go.
69
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TABLE 12. MAXIMUM CONCENTRATIONS NEAR DOWNWIND
EDGE OF TYPICAL URBAN AREA SOURCES
Type Source
Typical Maximum 24-hour
Concentration
References
Urban Expressway (1)
Street Canyon (2)
Parking Garage* (3)
Roadway Tunnel (2)
Shopping Mall (4)
Sports Stadium* (4)
85
45
45
650
80
10
Burton and Suggs 1982
Ingalls 1981
Ingalls 1981
Ingalls 1981
Ingalls 1981
Ingalls 1981
* Very high short term concentrations may occur near this source.
(1) Based on observed upwind-downwind differences in IP over 14 hours,
corrected to 24 hours and PM^Q.
(2) Based on a 24-hour average to peak ratio of 0.5, a vehicle emission
rate of 0.28 g/km, and a peak traffic flow of 3000 vehicles/hour.
(3) Based on model estimates and an emission rate of 0.085 g/min.
(4) Based on CO observations of 2.5 ppm (24 h) for shopping centers,
and 22 ppm (15 min) for sports stadiums, and ratio of PM^Q to CO
emissions of 0.0286.
73
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Table 13 illustrates a rationale for selecting 15 sites. In this example,
four neighborhoods are identified that potentially have high micro- and
middle scale PMjg levels. The neighborhoods that border on a neighborhood
containing high concentrations are also expected to have a chance of exceeding
the NAAQS for PM^O. As a result, two sites in adjacent neighborhoods will
be selected. There are also three neighborhoods that contain health care
treatment facilities with persons who are highly sensitive to air quality.
After discussions with various officials responsible for providing funds for
air monitoring operations, a decision is made to put monitors at 15 sites.
TABLE 13. EXAMPLE DETERMINATION OF THE NUMBER
OF MONITORING SITES IN A METROPOLITAN AREA*
Priority
1
2
Type of scale
for PMjo
Includes selected
micro- or middle
scale site
Adjacent to major
Recommended
number of (X) Number (=) Number
sites of areas of sites
1 4
2
4
8
source area
3 Special interest 133
Total 15
* This case was selected to be representative of a city with a population of
500,000 and four major source areas. Smaller cities and cities with fewer
source areas may require fewer monitoring sites.
Each neighborhood selected for monitoring must be reviewed carefully to
identify areas containing micro- or middle scale PM^Q effects. Neighborhood
scale sites must be selected to avoid these areas. The data presented in
Tables 14 through 16 identify the distances to which middle scale effects
extend from the types of sources associated with PM emissions. These
distances should be shown as circles around sources in neighborhoods selected
for monitoring.
74
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TABLE 14. SIGNIFICANT IMPACT DISTANCES OF
SMALL GROUND-LEVEL AREA SOURCES
Area (m x m)
250 x 250
500 x 500
103 x 103
Emission
rate (kg/km2/day)
10
102
103
10
102
103
10
102
103
Maximum downwind di
with significant
0.25
1.0
5
0.6
2.5
14
1.4
7
45
stance (km)
impact*
Based on 24 ug/m3, F stability class and 2 m/sec wind speed. Estimated
using Workbook of Atmospheric Dispersion Estimates (Turner 1970) by
treating source as a point. This worst case situation is expected to
produce a 24-hour concentration of 6 wg/m3. ~
TABLE 15. SIGNIFICANT IMPACT DISTANCES OF HIGHWAYS
Average Maximum downwind distance (km)
daily traffic (veh/day) with significant impact*
100,000
50,000
25,000
15,000
12,000
0.22
0.11
0.05
0.02
0
Based on 6 ug/m3, Pasquill stability class D, and wind speed of 2 m/sec
at 45 degree angle with highway. Estimated using EPA HIWAY2 model and
vehicle emission rate of 0.28 g/km. Because concentrations downwind of
highways are not sensitive to variations in wind direction, the worst case
24-hour concentration is based on a persistent worst case 1-hour concen-
tration. This allows the effect to be comparable with worst case effects
from elevated points (Table 16) and small areas (Table 14).
75
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300
TABLE 16. SIGNIFICANT IMPACT DISTANCES OF
ELEVATED SOURCES
Effective
plume
height (m)
30
100
Emission
rate
(kg/hr)
30
10
3
100
30
10
Critical
Pasquill
stability class
C
A
Maximum downwind
distance (km) with
significant impact*
3.3
1.7
0.9
1.2
0.8
0.5
100
1.2
Based on 24 ug/rn^ and 2 m/sec wind speed. Estimated using Workbook
of Atmospheric Dispersion Estimates (Turner 1970). This worst case
situation is expected to produce a 24-hour concentration of 6 ug/m3.
Monitoring Isolated Major Sources in Flat Terrain
Figure 20 suggested steps to be followed in selecting monitoring sites
near an isolated major source. A distinction must be made between sources
with the principal emissions from a tall stack and sources with the principal
emissions from ground level. For ground-level sources, the maximum concentra-
tions will occur immediately adjacent to the source in the most prevalent
downwind directions from the source. Wind observations will easily identify
the most suitable siting areas. Additional monitors may be used to help
define the extent of the area near the source that has high concentrations
and the neighborhood scale level of PM^Q in the vicinity of the source.
Two types of information can be helpful in determining the extent of the
high impact area: (1) the relative concentration isopleths from the EPA
(1970) Workbook of Atmospheric Dispersion Estimates and (2) annual wind
direction frequency statistics published by the National Climatic Center
(see Appendix A).
76
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•It is easily seen from the Workbook data that the peak concentration
falls off rapidly with distance for ground-level sources. The peak concen-
tration 100 m from the source drops by a factor of 10 at a distance of 40 km
from the source for all stability conditions. The more stable the atmosphere,
the more slowly the peak concentration drops with increasing distance from
the source. The Workbook curves show that even for very stable conditions
(Pasquill Class F), the peak concentration drops by a second factor of
10 within 1600 m from the source. These data show the microscale influences
within 100 m of the source are at least 10 times greater than the middle scale
influences from 100 to 500 m from the source. If there is public exposure
within 100 m, it is important to locate a monitor there. Middle scale
monitoring sites within 500 m of the source are desirable in each prevailing
wind direction. One of the middle scale sites should be downwind for the
wind direction that occurs most frequently with stable conditions and low
wind speeds. A Star climatology analysis for the closest weather observing
station maybe used to determine this direction (see Appendix A).
If the primary emissions are from a tall stack, the highest ground-level
concentrations will be away from the source. Detailed manual computational
procedures for estimating the magnitude and location of the maximum impact of
tall stack emissions are given in Volume 10 of the EPA Guidelines for Air
Quality Maintenance Planning and Analysis (Budney 1977). Figures 27 and 28
(taken from Budney 1977) show how the distance to the maximum short-term
concentration varies with the effective height of the exhaust gas plume and
atmospheric stability. Figure 27 treats sources in rural terrain, and Figure 28
treats sources in urban terrain. Budney's Guideline describes a method of
estimating the effective height of the source. Because the PMio monitors
will observe 24-hour and annual mean concentrations, the large variation in
distance to the maximum concentration with variations in atmospheric stability
class must be taken into account in selecting a site. It may be noted in
Figure 27 that the maximum concentrations occur with the greatest instability
(i.e., Class A). Therefore, it is important to site a monitor close to the
source where the maximum contributions will occur under unstable conditions.
As shown by Figure 27, this will be as close as 100 m to a source with a 20 m
effective height and as far as 800 m downwind of a source with a 300 m
effective height.
Another important factor in selecting a site is the persistence of the
wind direction over the observation period. Because the wind direction is
highly variable under unstable conditions and because persistent wind direc-
tions are generally associated with neutral (Class D) stability conditions, a
good strategy is to select a second monitoring site at a distance associated
with the peak for neutral stability. The distance downwind' to the peak
concentration will vary from about 350 m for an effective height of 20 m to
between 15 and 20 km for an effective height of 300 m.
77
-------
E
o"
S
1
""T30NV.LSIC1 OMIMNMOQ
78
-------
0.1
10-5 2 5
MAXIMUM \ti/Q.. m'2
1 fl-
Figure 28. Downwind distance to maximum concentration and maximum
as a function of stability class and effective plume height in urban
terrain (Budney 1977).
79
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The peak concentration will be sharp, with high concentrations falling
off rapidly with distance from the. peak, when the peak is close to the
source. This is a middle-scale effect, and the maximum impacts will be
observed over an area within 200 to 300 m of the peak. The frequency of
wind directions associated with only unstable conditions should be taken into
account in selecting sites for observing the middle-scale peak.
When selecting a site to observe concentrations from a tall stack
(effective height of 100 m or more) during persistent wind conditions (and
neutral stability), the concentrations will fall off gradually with distance
from the peak. The impacted area will be on a neighborhood scale, with high
concentrations (within 25 percent of the peak) occurring at distances of 2 km
from the peak when the effective height is 100 m and to distances of 10 km
when the effective height is 300 m. Wind direction frequencies associated
with neutral conditions should be used to site monitors. It may be noted
that there is a large area within which to select a site.
Wind observations from remote sites (e.g., a regional airport) are very
useful for selecting neighborhood-scale sites. When selecting a middle-scale
site, it is necessary that the wind observations be representative of the
very small scale area in the vicinity of the site. In the next section,
topographical influences are discussed that may make wind observations
unrepresentative. Suggestions are made for taking the local influences into
account in selecting monitoring sites.
Monitoring Isolated Major Sources in Complex Terrain
There are a number of situations in which the complexity of the terrain
in the vicinity of a major source will influence how pollutants are distributee
in the nearby vicinity. These influences must be taken into account in
siting monitors if the observations are going to achieve their objectives.
Available meteorological observations may not be adequate to desribe the
effects, especially if they are taken from a single site. In particular, the
effects of elevated terrain, coast lines, and urban structures need to be
taken into account. The air flow characteristics in the vicinity of these
types of terrain were discussed in Section 4. Suggestions are given here for
using the topographical characteristics of an area to select monitoring
sites and to modify the site selection guidelines for flat terrain.
Typical influences due to elevated terrain include two-sided boundaries
such as a valley and one-sided boundaries such as a mountain range or a
pronounced bluff. Air flow in a valley is subject to nighttime drainage down
the slopes and along the valley floor, to upslope covection and fumigation
during the day, and to channeled flow when strong winds blow diagonally
across the valley.. Near one-sided boundaries, emissions on the downwind side
of a ridge or hill may become entrapped in the turbulent wake flow downwind
of the ridge, or separated from ground-level when overshoot separation flow
occurs over the ridge. Emissions near either one-sided or two-sided terrain
boundaries may impact the terrain under very stable conditions with the flow
80
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directed towards the elevated terrain. Each of these effects produces a
pollution impact zone, which is associated with the terrain configuration.
Monitoring sites are needed that measure the results of these effects. The
following terrain-oriented sites are needed to supplement or replace sites
that conform to flat terrain siting selections:
o Down- and up-valley in place of or in addition to
downwind of the most prevalent wind directions
o Terrain elevation at the effective height of the
source plume or at maximum elevation (if less than
effective height) in prevailing downwind directions
• Nearest terrain elevation at effective height of
source plume.
Near a lake or ocean coast, there will be an invisible boundary between
the air influenced by the temperature of the underlying water surface and the
air influenced by the temperature of the underlying land surface. A great
difference in the two surface temperatures can significantly alter air flow
in the vicinity of the coast line. The two effects that are of interest
in selecting sites for monitors are (1) the tendency for the air flow to be
perpendicular to the coast and (2) the formation of a vertical circulation
with its axis centered on the coast line. The first effect indicates the
need for a monitoring site directly inland from a source near the coast. The
second effect indicates the need to have sites along the coast on both sides
of the source. These sites are to catch the impact of air that initially
moves inland, but that subsequently rises, moves back over the water, sinks,
and blows back inland at low levels. Under these conditions, pollution moves
perpendicular to the apparent ground-level wind observations. The magnitude
of the air pollution effect from this recirculation of air over the coast
line is difficult to anticipate. It could be an important, controversial
contribution to establish. These siting considerations should be taken as
supplements to the guidelines given for more uniform terrain situations.
Urban Areas with Major Point Sources
When major point sources of PM emissions are present in an urban area,
there is a need to consider the impacts of the point and the urban area
sources individually and of their joint overlapped effects. Siting consid-
erations relating to both urban areas and points as individual sources were
previously discussed. The overlapped effects can be best identified by
considering lines connecting pairs of nearly individual sources. When the
connecting lines parallel one of the prevailing wind directions, locations
that are downwind of both sources and near the maximum of the second down-
wind source are likely locations of maximum 24-hour PMio concentrations.
However, the maximum annual mean concentration is likely to be in a location
that is central to the individual sources. Such a location will be affected
81
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by different sources at different times, rather than by the simultaneous
overlapping of the effects of two or more sources. These two qualitative
criteria regarding the Impact of overlapping effects can be used to help
Identify locations that are probably sites of maximum concentrations. These
criteria are helpful when a modeling analysis is not available to evaluate
the joint effects of multiple sources.
Simple calculations and graphical analysis may be used to apply the
above siting criteria for multiple sources. For instance, in deciding which
pairs of overlapped source contributions are most significant, the relative
emission sites and distances between sources should be taken into account.
The contribution of a source to the PM^o concentration at any location is
directly proportional to the emission rate and inversely proportional to the
distance from the source. Although the distance relationship is a complex
function of atmospheric stability conditions and the effective height of the
emissions, the distance effect is most frequently very nearly proportional to
the Inverse square of the distance. For the purpose of evaluating the
importance of overlaps from the sources, the following relationship can be
used:
where A = Relative contribution from second source
E = Emission rate (second source)
D = Distance to second source.
To illustrate the use of this relationship, consider a major urban
freeway with a nearby source only 0.5 km away that emits 10 Ib/hr. The
overlap contribution from the source will be more important than any other
source emitting 100 Ib/hr or less at a distance of 1.6 km or more away,
since
AI • To^yz •4°
100
A2 • 71^2 - 39
A good way to define the scale and locations of the effect of overlapped
sources is to construct a representative graph of peak concentrations versus
distance downwind of the second source. This can be.done quite easily by the
use of the EPA Workbook of Atmospheric Dispersion Estimates (Turner 1970) or
82
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Volume 10 of the EPA Guidelines for Air Quality Maintenance Planning and
Analysis (Budney 1977). The following steps may be used:
1. Pick a representative stability condition (e.g., C stability)
and find the appropriate xu/Q versus distance graph.
2. For the larger of two overlapping sources, use the selected
graph to find a dozen pairs of xu/Q and distance values that
straddle the peak xu/Q value, and multiply the xu/Q values by
the emission rate to get (xu)i values.
3. Add the distance (D) between the two sources to the distances
read in step 2 and read a new x/Q value from the graph for
each new distance.
4. Multiply the second set of xu/Q values by the second source
emission rate to get (xu)g values.
5. Add the two sets of xu values together and plot the sum as a
function of the initial distance (without D added).
6. Repeat steps 2 through 5 for additional distances to make
the curve complete.
Table 17 shows a sample work table for use with the above steps'. The procedure
may be repeated for more than one stability class to help identify a range of
distances from the source within which the maximum concentrations will occur.
The buildup and fall off of concentration with distance will help identify
the distance scale that the combined concentrations will affect.
TABLE 17. SAMPLE WORK TABLE FOR OVERLAP EFFORTS
Distance Distance (Xu)l
from larger from smaller +
source (x) (xu/Q)j (xu)i source (x+D) (xu/Q)£ (*u)2 (*u)2
83
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This procedure is expected to be adequate for most monitor siting
purposes. However, the graphs referenced above do not include any effects
of particle removal due to fallout or other atmospheric processes. Actual
concentrations may decrease more rapidly with downwind distance than is
represented by these curves. More accurate graphical representations of
the relationship may become available in the future and should be used when
appropriate.
When considering sites to measure long-term concentrations that include
contributions from many sources, a simple numerical evaluation procedure may
be used to help select the best sites. Over a long-term period, both the
distance from the source and the frequency with which the wind blows from
each source to the potential monitoring site must be taken into account.
The following simple source weighting function takes these two effects into
account:
N
where B = Monitoring site pollution index
E-j = Source i emission rate
f-j = Relative frequency with which wind blows from source i
to the monitoring site
D-j = Distance from source i to monitoring site
N = Number of urban area and major point sources.
This site evaluation equation may be used to rank alternative monitoring
sites. The best way to perform the site evaluation process is to plot the
major urban area and major point sources on a map. A number of locations in
the middle of the sources and close to or downwind of the larger sources may
be selected as potential monitoring sites. The evaluation equation may then
be used to score the relative pollution levels expected at each potential
site. The highest score would indicate the site most likely to measure the
highest PM^o concentration.
SELECTION OF MONITORING SITES
Number and Locations of Monitors
The preceding steps have been concerned with developing a pattern
of PMio air quality that occurs in an area of concern for which monitoring is
planned. This may be an area administered by an air pollution control
agency or an area impacted by a particular source. In either case, there are
84
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three types of information regarding the patterns which are of interest,
including:
e Maximum PM^g concentration
• Background PM^o concentration
• Area impacted by significant PMjo concentrations.
Significant PMjo concentrations may be levels associated with air quality
standards, PSD increments, specific increments above background levels, or
other criteria of interest. There is another type of site that does not
involve a selection process (i.e., sensitive sites of special interest). In
a simple pattern, there will be one maximum and a single regularly shaped
contour that defines the area impacted by significant concentrations.
Complex patterns have two or more peaks that may or may not lie within a
single closed contour of impacted areas of interest. Unless one peak is much
higher than the others, two or more peak areas will need to be monitored.
The number of monitors needed to define impacted areas will include a
minimum of two and may include six or more depending on how large, how
complex and how definitive the impacted area is. A single, well-sited
monitor, located well away from any nearby sources or source areas, may be
adequate for determining background concentrations. If it is impractical to
locate a monitor far away from nearby sources, it may be desirable to select
two nearby monitors, one or more of which is measuring background concen-
trations on any given day, depending on wind direction. Because PM^Q concen-
trations are measured over 24-hour periods and because the wind direction is
frequently variable over a 24-hour period, this is a less desirable option
than a single, well -si ted monitor.
In planning and revising air monitoring plans, it is important to bear
in mind that the need for monitoring data is dynamic and will change from
year to year. Once the nature of the air quality pattern for PM^g concen-
trations has been established or verified, fewer stations are needed to
evaluate general ambient conditions and trends. This is especially true for
areas where the ambient levels are well within acceptable limits and there is
no significant impact area. Reducing the amount of resources allocated to
fixed monitoring stations will allow resources to be reallocated to meet
other special purpose monitoring needs.
Previous monitoring and modeling provide a first estimate of the
air Quality patterns, but a large amount of uncertainty may still exist
regarding both the shape and the magnitude of the pattern. Therefore, some
monitoring resources should be allocated to verifying the assumptions made
regarding the pattern. Two forms of monitoring are recommended for this
purpose, including temporary sites and mobile monitoring. This type of
monitoring is most effective when it is used in conjunction with modeling
results to confirm or deny the influence of specific sources on air quality
levels. An example of appropriate use of this type of monitoring is to
85
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establish the validity of a kink or a bulge in the air quality pattern due
to the influence of a specific nearby source or source area. Modeling
results could be obtained to show the expected contribution of specific
sources to the bulge. Air monitoring results along with appropriate meteoro-
logical data could be used to establish the validity of the influence. A
temporary monitor could be moved from one location to another to investigate
the validity of a number of these influences. The monitoring results would
increase confidence in the modeling results or provide the basis for either
model improvements or selection of a more accurate model.
Mobile monitoring can also be used to help establish the influence of
specific sources. Mobile monitoring is effective when it is used to identify
peaks in concentrations during crosswind sampling traverses downwind of
large elevated point sources. Another effective use of mobile monitoring is
to encircle area sources in order to establish concentrations upwind and
downwind of suspected significant sources of ground-level fugitive emissions.
A limitation in mobile monitoring is the need to use a continuous type of
analyzer. Continuous measurements of PM will necessarily be based on physical
measurement other than the weight of size-selected particulate matter col-
lected on a filter. As a result, it will be necessary to correlate the
mobile measurements with fixed station measurements before interpreting the
mobile measurement data. Some guidelines on ways of making these correlations
are provided in the Guidelines for PM-10 Episode Monitoring Methods (Pelton
1982).
Specific Site Selection
Once a general area for a monitoring site has been selected, it is
necessary to select a specific location for the sampling operation. The intake
for the monitor must be representative of the siting area, as close to the
breathing zone as possible, and not biased abnormally high or low by influences
which are only representative of the probe intake. The nature of biasing
influences is documented in CFR 40 Part 58 and includes the following:
e Chemical reactions due to the air stream passing
near reacting surfaces
• Unusual micrometeorological conditions
e Vegetation that serves as a pollutant sink
« Undue influence from nearby small sources (e.g.,
incinerator or furnace flue)
t Shielding influences from nearby obstructions.
86
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Based on the consideration of these factors, the following guidelines for
siting problems were promulgated in CFR 40, Part 58:
o 2-15 m above ground, as near to breathing height as
possible, but high enough not to be an obstruction
and to avoid vandalism
e At least 2 m away horizontally from supporting
structures or walls
• Should be 20 m from d.ripline of trees
o Should not be near furnace or incinerator flues
« No nearby obstructions to air flow due to buildings,
structures or terrain, at least in directions of
frequent wind.
These guidelines were provided for TSP but are equally applicable to PM]_o.
INSTALLATION AND FOLLOWUP
Each time a monitoring site is established, a documented description
of the site is established. This record will help in the interpretation of
results obtained from the site and in the evaluation of the need for changes.
The following information is useful in documenting a site with regard to
effects on measured PMjQ concentrations:
e Exposure diagram
- Horizontal depiction showing location relative to
nearby streets, buildings, and other significant
structures, terrain features, or vegetation
- Vertical depiction showing location relative to
supporting structures, including buildings, wa.lls,
etc.
9 Height of sampling intake above ground level
o Microinventory map showing locations of roads (with
traffic counts), open fields, storage piles, and any
visible emissions within 500 m of sampler
• List of all inventoried point and area sources within
1.5 km of sampler and all major point sources within
8 km of sampler
• 87
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a Make and model of PMio monitor
• Types of meteorological and other air monitoring
equipment operated at the site.
Once a monitoring site is selected and approved, the above site infor-
mation should be compiled. As soon as it is practical, data collected from
the site should be reviewed and scrutinized to determine that they do not
contain undue influences from nearby sources. The suggestions for analyzing
single-station air quality records, presented earlier in this report, should
be used to evaluate the observations.
-------
SECTION 6
EXAMPLE STUDY
To illustrate and test the ideas for selecting monitoring sites that
were described in Section 5, TSP data for the City of Baltimore and surround-
ing areas for 1980 and 1981 are listed in Table 18. Figure 29 shows the
locations of monitoring sites within the city limits; Figure 30 shows moni-
toring site locations outside the city limits.
For the purposes of this example, it is assumed that the State of
Maryland and the City of Baltimore will cooperatively operate monitoring
stations in the city for the following objectives:
t Evaluate progress in meeting and judge the attainment or
nonattai nment of NAAQS
• Develop and revise as necessary the Maryland Implementation
Plan for controlling
0 Provide data to EPA to meet national monitoring needs and
to evaluate the State's management of air quality
• Provide data for model research and development
• Support enforcement activities
• Provide the public with information on air quality
exposure and trends
• Provide data to identify and document episode exposure
situations.
The annual mean concentrations for 1980 and 1981 are plotted in
Figures 31 and 32. Isopleths are also shown to help interpret the patterns
indicated by these data. The locations of the eight major point sources with
particulate matter emissions in excess of 100 tons/yr are also shown and
identified-by number. The estimated emission rates for these sources are
listed in Table 19. Fugitive emissions shown by squares in the air quality
maps are listed in Table 20.
The maximum 24-hour concentration of TSP that were measured during 1980
and 1981 are shown in Figures 33 and 34. The 1981 pattern is based on 15 obser-
vations, while the 1980 pattern is based on 10 observations. The patterns of
maximum concentration are quite different between the 2 years. The tongue of
89
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TABLE 18. HI-VOL MEASUREMENTS OF TSP IN THE VICINITY OF BALTIMORE
(MARYLAND AIR MANAGEMENT ADMINISTRATION 1980, 1981)
Geometric mean
Site, county
35.
38.
39.
40.
41.
42.
44.
47.
48.
49.
50.
51.
52.
53.
54.
23.
26.
28.
29.
33.
34.
18.
20.
23.
Fire Department Headquarters, City
NE Police Station, City*
NW Police Station, City*
SE Police Station, City*
SW Police Station, City*
Fire Department #10, City
Fairfield, City
Canton Pier #4, City**
AIRMON-02, City
Fire Department #22, City**
Ft. McHenry, City
Holabird Elementary School, City**
Westport, City
Canton Recreational Center, City**
1-95, City**
Garrison, County
Catonsville, County
Essex, County
Padonia, County
Chesapeake Terrace Elementary
School , County
Sellers Point
Linthicum, Anne Arundel County**
Glen Burnie, Anne Arundel County
Riviera Beach, Anne Arundel County
1980
82
54
69
81
65
—
89
~
—
82
103
(72)2
93
—
(73)3
49
47
64
67
66
79
(56)2
68
60
1981
70
48
56
68
55
88
89
(141)2
67
(85)2
89
71
71
(75)2
73
47
46
61
60
60
80
--
61
58
Maximum
(6-day cycle)
1980
284
138
275
269
201
«
206
—
—
222
195
(175)2
178
—
(133)3
94
86
134
183
147
145
(81)2
132
85
1981
203
129
122
166
135
325
310
(575)2
146
(165)2
231
161
140
(176)2
155
93
112
136
114
140
176
—
125
137
* Operated on a 3-day cycle, rather than a 6-day cycle.
** Values in parentheses represent only two or three quarters.
90
-------
^ " ZryT" , ~1:. *
Figure 29. TSP monitoring sites in Baltimore City,
91
-------
Figure 30.
TSP monitoring sites in the Baltimore AQCR
excluding Baltimore City. '
92
-------
3-3
Figure 31. Annual mean TSP concentration for 1980.
93
-------
3-3
~S " ;i*~
-------
TABLE 19. TSP EMISSIONS BY EIGHT LARGEST POINT SOURCES IN BALTIMORE CITY
Number
1
2
3
4
5
6
7
8
Name
BG&E
Davison Chemical
General Refractory
Carton Elevator
Allied Chemical
National Gypsum
Louis Dreyfus
U.S. Gypsum
Emissions
(tons/year)
181
133
116
1,475
145
126
2,193
1,612
Type
Fuel burning
Process
Process
Process
Process
Process
Process
Process
TABLE 20. FUGITIVE EMISSIONS BASED ON 1977 SURVEY
(Schakenbach and Koch 1978)
Area
identification
1
2
3
4
5
6
7
8 -
9
10
11
12
13
14
15
Emission
rate
(tons/day)
11.7
8.0
2.2
4.1
2.2
7.3
2.4
2.6
10.9
2.7
1.8
1.7
3.8
4.1
2.1
Principal
sources
Dirt roads
Dirt roads, construction
Dirt and gravel roads
Dirt and gravel roads
Dirt and gravel roads
Dirt roads
Dirt roads, construction
Dirt and gravel roads
Dirt and gravel roads
Dirt and gravel roads
Gravel roads
Construction sites
sites
sites
Storage piles, gravel roads
Gravel roads
Gravel roads
95
-------
3-3
Figure 33. Maximum 24-hour TSP concentration for 1980.
96
-------
Sfr4) '"fe """"^a M*:ft^—-4ll-^O)*""
+*''*'' W* ___ a u**« iMiV ^A t4««i
Figure 34. Maximum 24-hour TSP concentration for 1981
97
-------
high concentrations shown for the 1980 data is not confirmed in 1981. It is
possible that the two high observations to the east and northwest ends of the
tongue were not properly sited and showed unrepresentative local influences.
The 1981 pattern for maximum 24-hour concentrations is more compatible with the
two annual mean patterns, showing a primary peak around the open harbor area an
a secondary peak over the primary central city area just west of site 35.
The TSP monitoring data indicate a core area of high concentrations
centered on the Baltimore harbor region. The highest point and area source
emissions of parti cul ate matter also form a ring around the harbor zone.
Figure 35 is a wind rose showing the frequency of 24-hour mean wind
directions with a wind persistence index of 0.85 or greater. (An index of
1.0 indicates a continuous wind direction without variation.) The wind
directions with the most frequent occurrence of a persistent wind are
west-northwest, west, and northwest. The persistent wind directions closely
parallel the orientation of the harbor along the Patapsco River. Therefore,
the persistent winds also favor a core of high particulate matter concen-
trations around the harbor zone. The tongue of high values north of the
principal sources shown in the peak 1980 concentrations is not well supported
and is not evident in the 1961 data.
concentrations may be expected to show a flatter pattern with less
pronounced peaks than the TSP data. This is because there will be lower
contributions from the larger particles released close to local sources.
Monitoring sites farther from the local sources will be less affected by the
deletion of larger particles and will show smaller reductions. This will
result in a smoother pattern.
At least one site in the harbor area is needed to measure the peak
PM]_o concentrations. Since the area is presently out of compliance with NAAQS
for particulates, there will need to be sufficient monitors in the area
surrounding the harbor to delineate the general shape of a potential noncom-
pliance area for the new PMjQ standards. One strategy would be to select
locations northwest, northeast, and south or southwest of the harbor area.
In view of the potential for high levels of PM^Q concentrations, there is a
need to inform the public of PMjg exposure levels and trends, to document
episode situations, and to support enforcement activities. For these reasons,
it is desirable to site at least one and ideally two additional PMjo monitors
in the harbor area. Once the magnitude of PM^g concentrations relative to
P^IO standards has been established, the siting requirements need to be
reevaluated. There is also a need for a background monitoring site. There
are many suitable sites that are presently monitoring TSP concentrations.
Baltimore County Site 23, about 15 km northwest of Baltimore City, is upwind
of the persistent prevailing wind directions. Furthermore, TSP measurements
made at this site are indistinguishable from TSP measurements made at a site
35 km to the northwest (site 53) in very rural Carroll County (see Figure 31).
98
-------
NNW
NW
WNW
W
WSW
SV>'
ssw
NNE
SE
SSE
ENE
ESE
Figure 35. Wind persistence rose for Baltimore-Washington
International Airport for 1973-1977 (wind persistence
index greater than 0.85) (Pickering et al. 1979).
• 99
-------
The preceding discussion describes the development of PM^g monitoring
network requirements where there is adequate TSP monitoring data to define
the shape of the expected pattern of PMio concentrations. In this situation,
modeling is not necessary. The subsequent selection of specific monitoring
placements require onsite inspection of potential sites and the criteria
described in Section 5.
100
-------
SECTION 7
REFERENCES
Auer, A.H., Jr. Correlations of Land Use and Cover with Meteorological
Anomalies. J. Appl. Meteorol. 17:636-43, 1978.
Ball, R.J, and 6.E. Anderson. Optimum Site Exposure Criteria for S02
Monitoring. EPA-450/3-77-013, U.S. Environmental Protection Agency,
Research Triangle Park, North Carolina, 1977.
Budney, L.J. Guidelines for Air Quality Maintenance Planning and Analysis,
Vol. 10 (Rev.): Procedures for Evaluating Air Quality Impact of New
Stationary Sources. EPA-450/4-77-001, U.S. Environmental Protection
Agency, Research Triangle Park, North Carolina, 1977.
Barton, R.M., and J.C. Suggs. Philadelphia Roadway Study. Draft Report.
U.S. Environmental Protection Agency, Research Triangle Park, North
Carolina, 1982.
Changery, M.J., W.T. Hodge, and J.Y. Ramsdell. Index—Summarized Wind
Data. BNWL-2220 WIND-11 UC-60, U.S. Department of Commerce, National
Climatic Center, Asheville, North Carolina, 1977.
Corn, M. Aerosols and the Primary Air Pollutants—Nonviable Particles.
In: Air Pollution (3rd edition), Volume I, A.C. Stern, ed. Academic
Press, Inc., New York, New York, 1976.
Doty, S.R., B.L. Wallace, and G.C. Holzworth. A Climatological Analysis
of Pasquill Stability Categories Based on STAR Summaries. National
Oceanic and Atmospheric Administration, Asheville, North Carolina, 1976.
Drake, R.L., and S.M. Barrager. Mathematical Models for Atmospheric
Pollutants. EA-1131, Electric Power Research Institute, Palo Alto,
California, 1979.
Fujita, T.T. Tornadoes and Downbursts in the Context of Generalized
Planetary Scales. J. Atmos. Sci. 38:1511-34, 1981.
Hewson, E.W. Meteorological Measurements. In: Air Pollution (3rd
edition), Volume I, A.C. Stern, ed. Academic Press, New York, New
York, 1976.
Holzworth, G.C. Mixing Heights, Wind Speeds, and Potential for Urban Air
Pollution Throughout the Contiguous United States. AP-104, U.S. Environ-
mental Protection Agency, Research Triangle Park, North Carolina, 1972.
101
-------
Holzworth, G.C. Climatological Aspects of the Composition and Pollution
of the Atmosphere. Tech. Note No. 139. Secretariat of the World Meteoro-
logical Organization, Geneva,- Switzerland, 1974.
Ingalls, M.N. Estimating Mobile Source Pollutants in Microscale Exposure
Situations. EPA-460/3-81-021, U.S. Environmental Protection Agency,
Ann Arbor, Michigan, 1981.
Jutze, C.E., et al. Technical Guidance for Control of Industrial Process
Fugitive Particulate Emissions. EPA-450/3-77-010, U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina, 1977.
Landsberg, H.H, Atmospheric Changes in a Growing Community. Tech. Note
No. BN 823. University of Maryland, College Park, Maryland, 1975,
Landsberg, H.H. The Urban Climate. Academic Press, New York, New York,
1981.
Ludwig, F.L., and J.H.S. Kealoha. Selecting Sites for Carbon Monoxide
Monitoring. EPA-450/3-75-077, U.S. Environmental Protection Agency,
Research Triangle Park, North Carolina, 1975.
Ludwig, F.L., J.H.S. Kealoha, and E. Shelar. Selecting Sites for Monitor-
ing Total Suspended Particulates. EPA-450/3-77-018, U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina, 1977.
Ludwig, F.L., and E. Shelar. Site Selection for the Monitoring of
Photochemical Air Pollutants. EPA-450/3-78-013, U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina, 1978.
Lyons, W.A., and L.E. Olsson. Mesoscale Air Pollution Transport in the
Chicago Lake Breeze. J. Air Poll. Contr. Asso. 22:876-81, 1972.
Maryland Air Management Administration. Maryland Air Quality Data
Report. Maryland Department of Health and Mental Hygiene, Baltimore,
Maryland, 1980 and 1981.
Massoglia, M.F., J.P. Wood, and K.H. Babb. Emission Reductions Study,
Stationary Sources of Air Pollution (1970-1979). EPA-340/l-81-001a,
U.S. Environmental Protection Agency, Washington, D.C., 1981.
McCormick, R.A., and G.C. Holzworth. Air Pollution Climatology. In:
Air Pollution (3rd edition), Volume I, A.C. Stern, ed. Academic Press,
New York, Mew York, 1976.
Oke, T.R., and F.G. Hannel. The Form of the Urban Heat Island in Hamilton,
Canada. In: Proceedings of the WHO/WMO Symposium on Urban Climates and
Building Climatology. WMO Tech. Note No. 168. Secretariat of the World
Meteorological Organization, Geneva, Switzerland, 1970.
102
-------
Pace, T.G. An Empirical Approach for Relating Annual TSP Concentrations
to Particulate Microinventory Emissions Data and Monitor Siting Character-
istics. EPA-45U/4-79-012, U.S. Environmental Protection Agency, Research
Triangle Park, North Carolina, 1979.
Pace, T.G. Ambient Parti cul ate Baseline Conditions—Sources and Concen-
trations. In: Proceedings of the Technical Basis for a Size Specific
Particulate Standard, Specialty Conference, Air Pollution Control
Association, 1980.
Pasquill, F. The Estimation of the Dispersion of Windborne Material.
Meteorol. Mag. 90:33-49, 1961.
Pickering, K.E., et al. A Study of Ambient Air Quality in the Vicinity
of Major Steel Facilities. GEOMET Report ES-793, EPA Contract
No. 68-U1-4144, Task 8. GEOMET Technologies, Inc., Rockville, Maryland,
1979.
Pickering, K.E., J.M. Vilardo, and H.E. Rector. A Study of Ambient TSP
Levels Near.Major Steel Facilities (1978-1980 Update), Volume II. Draft
Report £SF-9bi>, EPA Contract No. 68-01-6311, Tasks 6 and 10. GEOMET
Technologies, Inc., Rockville, Maryland, 1981.
Schakenback, J.S., and R.C. Koch. A Particulate Matter Study for the
Metropolitan Baltimore Intrastate Air Quality Control Region. GEOMET
Report No. EF-710. GEOMET, Incorporated, Gaithersburg, Maryland, 1978.
•
Snanon, L.J., J.P. Reider, and C. Cowherd. Emission Factors for Inhalable
and Fine Particulates. In: Proceedings of the Technical Basis for a
Size Specific Particulate Standard, Speciality Conference, Air Pollution
Control Association, 1980.
Suprenant, N., et al. Emissions Assessment of Conventional Stationary
Source Combustion Systems, Volumes I and II. EPA-600/7-79-029, U.S.
Environmental Protection Agency, Research Triangle Park, North Carolina,
1979.
Taoack, H.J., et dl. Fine Particulate Emissions from Stationary and Miscel-
laneous Sources in the South Coast Air Basin. Final Report to California
Air Resources Board, Sacramento, California, by KVB, Inc., 1979.
Trijonis, J., et al. Analysis of the St. Louis RAPS Ambient Particulate
Data. EPA-43U/4-«U-OU6a, U.S. Environmental Protection Agency, Research
Triangle Park, North Carolina, 1980.
Turner, u. B. Workbook of Atmospneric Dispersion Estimates. Report
No. AP-26, U.S. Environmental Protection Agency, Research Triangle Park,
North Carolina, 1970.
103
-------
Turner, D. B. Dispersion Estimate Suggestion No. 4. EPA National Environ-
mental Research Center, U.S. Environmental Protection Agency, Research
Triangle Park, North Carolina, 1974.
U.S. Environmental Protection Agency. Guide for Compiling a Comprehensive
Emission Inventory (Revised). No. APTD-1135. Research Triangle Park,
North Carolina, 1973.
U.S. Environmental Protection Agency. Air Monitoring Strategy for State
Implementation Plans. Standing Air Monitriong Working Group. EPA-45Q/2-
77-010. Research Triangle Park, North Carolina, 1977a.
U.S. Environmental Protection Agency. Guideline on Air Quality Models
(Revised). EPA-4bO/2-78-027R. Research Triangle Park, North Carolina, 1986.
U.S. Environmental Protection Agency. Ambient Air Duality Monitoring, Data
Reporting, and Surveillance Provisions for Particulate Matter.
Federal Register, Part III, 52:24736-750. 1987.
U.S. Environmeotal Protection Agency. NEDS National Emissions Data System
Information. EPA-4bU/4-80-013a. Research Triangle Park, North Carolina,
1984.
U.S. Environmental Protection Agency. Review of the National Ambient Air
Quality Standards for Particulate Matter: Revised Draft Staff Paper.
Reserch Triangle Park, North Carolina, I981a.
*
U.S. Environmental Protection Agency. Air Quality Criteria for Particulate
Matter and Sulfur Oxides. Volumes I and II. External Review Draft No. 2,
Research Triangle Park, North Carolina, 1981b.
U.S. Environmental Protection Agency. Compilation of Air Pollutant Emission
Factors. Volume 1: Stationary and Area Sources. Fourth Edition. AP-42,
Research Trianyle Park, North Carolina, 1985.
U.S. Environmental Protection Ayency. Supplement A to Compilation of Air
Pollutant Emission Factors, 1986.
U.S. Environmental Protection Agency. An Examination of 1982-1983 Particulate
Matter Ratios and Their Use in the Estimation of PM^y NAAQS Attainment. Status,
EPA-4bU/4-rfb-010. Research Triangle Park, North Carolina, 1985.
U.S. Environmental Protection Agency. Guideline for PM^Q Episode Monitoring
Metnods (Revised). EPA-4bO/4-d3-OUb. Research Triangle Park, North Carolina
1983.
Watson, J.G., J.C. Chow, and J.J. Shah. Analysis of Inhalable and Fine
Participate Matter Measurements. EPA-450/4-81-035, U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina, 1981.
104
-------
Whitby, K.T. Aerosol Formation in Urban Plumes, In: Aerosols, Anthro-
pogenic and Natural Sources and Transport, T.J. Kneip and P.J. Lioy, eds.
Ann. N.Y. Acad. Sci. 338:258-75, 1980.
Zoller, J., T. Bertke, and J. Janzen. Assessment of Fugitive Particulate
Emission Factors for Industrial Processes. EPA-450-/3-78-107, U.S.
Environmental Protection Agency, Research Triangle Park, North Carolina,
1978.
105
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APPENDIX A
METEOROLOGICAL DATA TABULATIONS FOR COM PROGRAM
Cities for which Stability Array (STAR) data tabulations are available
are listed alphabet!caTTy by date and by city within a state. This list was
compiled by Changery, Hodge, and Ramsdell (1977). Additional tabulations may
be available since this compilation, and others may be ordered. For assistance
on orders contact:
Di rector
National Climatic Center
Federal Building
Asheville, North Carolina 28801
106
-------
A-l. EXPLANATION OF ENTRIES
CITY is the city or town name for the location at which the original
observations were taken. It may also be the name of a military instal-
lation.
NAME-TYPE is usually the airport or field name and/or service which
operated the station. If these had changed during the period summarized,
the name and/or service valid for the longest portion of the summary is
used. A few stations may have no identifying information.
Under NAME, commonly used abbreviations are:
J\PT - Airport
ATL - Air Terminal
BD - Building
CAP - County Airpor*t
CO - County
FLD -- Field
GEN - General
GTR - Greater
INL - International
MAP - Municipal Airport
MEM - Memorial
METRO- Metropolitan
MN - Municipal
RGL - Regional
TERM - Terminal
Under TYPE, commonly used abbreviations are:
AAB - Army Air Base
AAF - Army Air Field
AAF3 - Auxiliary Air Force Base
AEPG - Army Energy Proving Ground
AF - Air Force
AFB - Air Force Base
AFS - Air Force Station
ANG3 - Air National Guard Base
ASC - Army Signal Corn
CAA - Civil Aeronautics Administration
FAA - Federal Aviation Administration
FSS - Flight Service Station
LAWR - Limited Airways Weather Reporting (Station)
MCAF - Marine Corps Air Facility
MCAS - Marine Corps Air Station
NAAF - Naval Auxiliary Air Facility
NAAS - Naval Auxiliary Air Station
MAP - Naval Air Facility
NAS - Naval Air Station
NAU - Naval Air Unit
NF - Naval Facility
NS - Naval Station
PG - Proving Ground
SAWR - Supolementary Airways Weather Reporting (Station)
WBAS - Weather Bureau Airoort Station
WBO - Weather Sursau Office
107
-------
ST is a two-letter code identifying each of the fifty states.
WBAN ? refers to the five-digit number identifying stations operated by
United States Weather Services (civilian and military) currently or in
the past. A few stations have had no number assigned.
WMO ? refers to the five-digit block and station numbers assigned to U. S.
stations as authorized by the World Meteorological Organization. Many
stations with a WBAN I will have no corresponding WMO number.
LAT. LONG are the latitude and longitude of the station in degrees and
minutes. If the station changed coordinates during the period summarized,
the location reflects the site with the longest record.
ELEV is the elevation (above sea level) of the station in meters. Reported
station elevation was used if the barometric height above sea level was not
available. If an elevation*change occurred during the period summarized,
the elevation reflects the station height for the longest period of record.
PERIOD OF RECORD is the first and last month-year of the summarized period.
As an example, 01 38 - 12 44 is read as January 1938 through December 19*4.
SUMMARY TYPE identifies each summary according to its format. Each format
is^similar to one of the 16 types presented in detail beginning on page 1-13.
SUMM FREQ is the summary frequency or the time period in which the summarized
data are presented. Abbreviations used are:
M - Monthly. Data for each calendar month combined and presented on
a monthly basis.
S - Seasonal. Data for the months December through February of the period
of record are combined into a winter season, summarized and
presented on a seasonal basis. The months March-May, June-
August, and September-November are similarly summarized.
A - Annual. All data for the period summarized together.
MA - Monthly and Annual.
SA - Seasonal and Annual.
MS - Monthly and Seasonal.
MSA - Monthly, Seasonal, and Annual.
IYM - Individual Year-Month. Data are presented for individual months
of record.
SP - Special Period. The special period presented is described further
in the given summary's Tab T/Remarks column.
108
-------
TAB f/REMARKS column contains additional identifying or explanatory
information. Many of the summaries produced by the Climatic Center
and Air Weather Service for a specific project are identified by a
tabulation number. A "T" followed by a 4 or 5 digit number identifies
a summary produced by the NCC. Similarly, a "TCL" with a number indi-
cates an AWS summary. Not all summaries can be so identified. This
number is provided as an aid in requesting a specific tabulation.
Numbers following or in place .of a tabulation number refer to remarks
listed beginning on page 1-9. These remarks are provided if additional
information describing a summary is necessary. Examples are summaries
with data for hourly or 3-hour periods, specified hours only, combined
stations, etc.
109
-------
A-2. REMARKS
•
This is a list of descriptive remarks coded by number in the Tab
column of the index. Numbers missing were not used.
1. Broken period
2. 3-hourly groups
3. Day-night
4. 0600-1800 LSI only
5. 10-12 observations per day, all daylight hours
6. By hours 00, 03,- 06, 09, 12, 15, 18, 21 1ST
7. See microfilm for broken periods and format
8. Includes flying weather conditions
9. Part "C" only
10. Hours 0600-1200 1ST only
11. May-November only
12. Broken period - pre-11/45 data from Point Hope (Stn #25601)
13. Broken period by hourly groups
14. Less 12/59
15. Pre-1939 data from Tin City (Stn #26634)
16. Less 12/70
17. 0500-1600 LST only
18. 2-13 observations daily
19. 0700-1900 LST only
20. Combined data for Douglas AAF (Stn #23001) for 11/42-11/45
and Douglas Apt (Stn #93026) for 11/48-12/54
21. Part "A" only by hourly groups - combined data for Kinaman CAA
(Stn #93167) for 01/34-12/41 and Kingman AAF (Stn #23108) for
03/43-06/45
22. For hours 0800, 1400, 1700 LST only
23. Direction and speed by visibility, relative humidity >_ 90% and/
precipitation, and relative humidity >_ 90% and no precipitation
August, October, and December only
24. Part "A" only
25. By 2-hourly groups
26. Daylight hours only
27. September-December only
28. By hourly groups
29. For 0900-1600 and 1700-0800 LST
30. Period 01/37-03/38 for Indio (Stn #03105)
31. Precipitation-wind tabulation for April-October
32. By day and night hours on microfilm
33. Periods: July 15-31, August 1-15 for 1000 and 1400 LST
34. No data for 27 months
35. See Edwards AFB
36. Some data from Paso Robles (Stn #23231)
37. All observations by various stability classes
38. See Moffett Field
39. Also contains a contact wind rose
40. Eight directions and calm
41. Includes a percentage graph'
110
-------
42. 1200 LSI observations only
43. Some missing data
44. Contains all weather, precipitation, and visibility <_ 6 miles
wind tabulations for day and night hours
45. Also called 94A
46. See Farallon Island SE
47A. 0100-0400 LST
47B. 0700-1000 LSI
47C. 1300-1600 LSI
470. 1900-2200 LSI
47E. 0600-2200 LST
47F. 0700 LST
47G. 1600 LSI
47H. 0600-0900 LST
471. 1600-1800 LST
47J. 0700-0900 LST
47K. 1900-0600 LST
47L. 1000-1500 LST
47M. 1200-2000 LST
47N. 0800-2100 LST
47P. 1100-1300 LST
48. Also contains bimonthly summaries
49. Located in city file
50". Three speed groups
51. June, July, August - daylight hours only
52. Special tables
53. Pre-1944 data from Boiling AAF (Stn 113710)
54. Also known as Chantilly, VA, FAA (pre-Oulles)
55. See Andrews AFB, MO
56. Data for 01/74 from Herndon Apt (Stn £12841)
57. See also Cape Kennedy AFB
58. Tower data - 8 levels (3-150 m)
59. June-August only
60. Data for 09/42-09/45 from Carlsbad AAF (Stn #23006)
61. Data after 07/53 from Key West NAS (Stn #12850)
52. Data thru 1945 from Marianna AAF (Stn #13851)
63. Contains 14 months of data from Morrison Field (Stn #12865)
64. Contains graphical wind rose
65. Tabulated by temperature and relative humidity intervals
66. Seasonal by day and night hours
67. Closed and instrument weather conditions only
68. Less 01/49
69. 24 observations daily
70. 8 observations daily
71. 1 of 3 parts
72. Tabulation by day and night hours for May.! - September 30 and
October 1 - April 30
73. Tabulated for December-March and April-November
74. Data prior to 10/42 and after 10/45 from Sioux City Apt (Stn #
.. Ill
-------
75. For day - clear and cloudy and night - clear and cloudy conditions
76- Also contains a ceiling-visibility tabulation
77. 0700-1900 LSI only
78. All weather and 2 relative humidity classes
79. Summer season only - 1957 missing
80. May, August-November only
81. Includes separate wind rose for WSO
82. Four speed categories
83. Monthly tabulation for 0400 and-1400 LSI, seasonal tabulation for
all observations
84. Some data from Presque Isle AFB (Stn #14604)
85. Four observations per day
86. Semi-monthly periods
87. 1935 data from Boston WBAS (Stn #14739)
88. VFR, IFR, closed conditions
89. Pre-03/1952 data from Paso Robles (Stn #23231)
90. August 1-15 only for hours 1000 and 1400 1ST
91. Partial SMOS
92. June, July only for hours 2200L - 0200L
93. April thru December only
94. Less April .1958 and 1960
95. January, April, July, and October only
96. Winter season only
97. Part "C11 and "E" only
98. 36 compass points
99. Les^ October-December 1945 for a 2-hour period.after sunrise
100. November 1951 substituted for November 1955
102. For hour groups 07-09, 10-15, 16-18, and 19-06 LSI and all
hours combined
103. For hours 0100, 0700, 1300, and 1900 LSI (individual and all
hours combined)
104. Day and night hours, clear and cloudy conditions
106. Pre-02/33 data from Albuquerque WBO (Stn #23073)
108. Precipitation wind rose tabulation
109. ATI observations by 6 hourly groups
HO, For ceiling less than 600 feet and/or visibility less than 1-1/2
miles - also an annual hourly summary
111. Also summarized by month-hour for hours 0200 and 1400 LSI
112. Summarized by days 1-15 and 16 to end of month for day and
night hours
USA. 1300 LSI
115B. 0400 LSI
115C. 1000 LSI
USD. 1600 LSI
USE. 2200 LSI
115F. 0700 LSI
115G. 0100 LSI
115H. 1900 LSI
117. See Covington, Kentucky
118. .Pre-04/32 data from Oklahoma City WBO (Stn #93954)
119- May to October only
112
-------
120. . Monthly for 1961-63, individual months 1-4/64
121. Also contains day and night summaries
124. Summary titled Scranton
125. See W.ilkes-Barre
126. December-February for 0730 and 1930 LSI only
128. Pre-12/44 data from Galveston AAF (Stn #12905)
129. Data for 10/62-12/63 for Greenville-Spartanburg Apt (Stn #03870)
132. February-April and June-September only
133. Pre-03/43 data from English Field (Stn #23047)
134. Post-10/66 data from Fort Wolters
135. Less 6/68
136. For hours 00-23 and 07-22 LSI
140. Also contains annual ceiling/visibility tabulation
141. Less 0000 and 0300 LSI
142. See Killeen
143. See Dugway PG
144. Data for 1943-49 for Wendover AF3 (Stn 1241.11)
145. 0400-1800 LSI
146. See Washington, DC - Dulles International Apt WBAS
147. See Washington, DC - National Apt WBAS
149. 0700-1200 LSI
150. Tower data, year-month-level, month-level, and month-level-hour
151. Pre-11/41 data from Paine Field CAA (Stn #24222)
152. 10 observations per day - closed on weekends
153. 10 observations per day - wind speed estimated
155. By 5°F temperature intervals - with and without thunderstorms
157. One speed group - greater than 14 knots
158. Speed classes in Beaufort Force - mean speed by direction in mph
159. Hourly groups for 0600-1600 LSI
160. Post-05/55 data from Forest Sherman (Stn #03855)
161. By speed classes and 5°F temperature classes
162. For all hours combined and for hours 0030 and 1230 individually
113
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO. ' 2.
EPA-450/4-87-009
4. TITLE AND SUBTITLE
NETWORK DESIGN AND OPTIMUM SITE EXPOSURE CRITERIA
FOR PARTICULATE MATTER
7. AUTHOR(S)
R. C. Koch and H. E. Rector
9. PERFORMING ORGANIZATION NAME AND ADDRESS
GEOMET Technologies, Inc.
1801 Research Boulevard
Rockville, Maryland 20858
12. SPONSORING AGENCY NAME AND ADDRESS
U.S. Enviro'nmental Protection Agency
Research Triangle Park,
North Carolina 27711
15. SUPPLEMENTARY NOTES
3. RECIPIENT'S ACCESSION NO.
5. REPORT DATE
May 1987
6. PERFORMING ORGANIZATION CODE
81-01-044-1
8. PERFORMING ORGANIZATION REPORT NO.
GEOMET Report No. ESF-1185
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
68-02-3584
13. TYPE OF REPORT AND PERIOD COVERED
Final
14. SPONSORING AGENCY CODE
16. ABSTRACT
This report presents procedures and criteria for selecting appropriate locations
for particulate matter (PM1Q) monitoring stations. Background on sources of
particulate matter, monitoring objectives, spatial relationships and various
meteorological considerations used in site selection are provided.
17. KEY WORDS AND DOCUMENT ANALYSIS
a DESCRIPTORS b. IDENTIC
particulate matter
site exposure criteria
site selection
monitoring objectives
air pollution
ERS/OPEN ENDED TERMS C. COSATI Held/Group
18. DISTRIBUTION STATEMENT i 19. SECURITY CLASS (This Report) 21. NO. OF PAGES
RELEASE TO PUBLIC UNCLASSIFIED 132
KCLLMOC IU rUDLIU J JO. SECURITY CLASS OVi« pofe/ 22. PRICE
UNCLASSIFIED
EPA Form 2220-1 (Re». 4-77'
PREV:OUS EDITION IS OBSOLETE
126
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