EPA-600/4-77-019
March 1977
Environmental Monitoring Series
DEVELOPMENT OF A METHODOLOGY FOR
DESIGNING CARBON MONOXIDE
MONITORING NETWORKS
Environmental Monitoring and Support Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Las Vegas, Nevada 89114
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into nine series. These nine broad cate-
gories were established to facilitate further development and application of en-
vironmental technology. Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface in related fields.
The nine series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5 Socioeconomic Environmental Studies
6. Scientific and Technical Assessment Reports (STAR)
7 Interagency Energy-Environment Research and Development
8. "Special" Reports
9. Miscellaneous Reports
This report has been assigned to the ENVIRONMENTAL MONITORING series.
This series describes research conducted to develop new or improved methods
and instrumentation for the identification and quantification of environmental
pollutants at the lowest conceivably significant concentrations. It also includes
studies to determine the ambient concentrations of pollutants in the environment
and/or the variance of pollutants as a function of time or meteorological factors.
This document is available to the public through the National Technical Informa-
tion Service, Springfield, Virginia 22161.
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EPA-600/4-77-019
March 1977
DEVELOPMENT OF A METHODOLOGY FOR
DESIGNING CARBON MONOXIDE MONITORING NETWORKS
Mei-Kao Liu, James Meyer, Richard Pollack,
Phillip M. Roth, John H. Seinfeld
Systems Applications, Incorporated
950 Northgate Drive
San Rafael, California 94903
and
Joseph V. Behar, Leslie M. Dunn, James L. McElroy,
Pong N. Lem, Ann M. Pitchford, Nancy T. Fisher
Environmental Monitoring and Support Laboratory
Las Vegas, Nevada 89114
Contract No. 63-03-2399
Project Officer
Edward A. Schuck
Environmental Monitoring and Support Laboratory
Las Vegas, Nevada 89114
ENVIRONMENTAL MONITORING AND SUPPORT LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
LAS VEGAS, NEVADA 89114
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DISCLAIMER
This report has been reviewed by the Environmental Monitoring and
Support Laboratory-Las Vegas, U.S. Environmental Protection Agency, and
approved for publication. Mention of trade names or commercial products
does not constitute endorsement or recommendation for use.
11
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FOREWORD
Protection of the environment requires effective regulatory actions which
are based on sound technical and scientific information. This information
must include the quantitative description and linking of pollutant sources,
transport mechanisms, interactions, and resulting effects on man and his
environment. Because of the complexities involved, assessment of specific
pollutants in the environment requires a total systems approach which trans-
cends the media of air, water, and land. The Environmental Monitoring and
Support Laboratory-Las Vegas contributes to the formation and enhancement of
a sound integrated monitoring data base through multidisciplinary, multimedia
programs designed to:
• develop and optimize systems and strategies for moni-
toring pollutants and their impact on the environment
• demonstrate new monitoring systems and technologies by
applying them to fulfill special monitoring needs of
the Agency's operating programs
This report discusses the theoretical bases for a method of designing
air quality monitoring networks. The method was developed for application to
reactive or nonreactive pollutant monitoring. Specific design steps in the
report, however, are illustrated for a carbon monoxide monitoring network.
•Regional or local agencies may find this method useful in planning or adjust-
ing their air quality monitoring networks. The Monitoring Systems Design and
Analysis Staff at the EMSL-LV may be contacted for further information on the
subject.
George B. Morgan
Acting Director
Environmental Monitoring and Support Laboratory
Las Vegas
lit
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PREFACE
This document is concerned with the development of a methodology for the
design of a monitoring network for carbon monoxide (CO). In actuality, the
methodology is generally valid for any airborne pollutant. CO was chosen
partially because it is a relatively inert pollutant for which the methodology
should be presentable in its basic, simplest form. In addition, CO is a pol-
lutant very susceptible to analysis at both the mesoscale and microscale
levels. Finally, the first application of the methodology is for CO and will
appear as a separate report.
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CONTENTS
Foreword , i i i
Preface iv
Abbreviations and Symbols vi
I. Monitoring of Ambient Air Quality 1
Review of Previous Work 1
Objectives of Air Quality Monitoring Programs .. 3
Factors Pertinent to Monitoring Network Design . 7
II. Design of a Network for Air Quality Monitoring 9
Components in the Design of an Air Quality
Moni tori ng System 9
Selection of Monitoring Sites 11
Approach Adopted in This Study 13
III. Analysis at the Mesoscale Level 20
A Description of the Mesoscale Air Quality
Simulation Model 20
Approximation of Turbulent Transfer by Eddy
Diffusivities 21
IV. Analysis at the Microscale Level 28
Overview 28
Representativeness of Measurements on the
Microscale 30
Solution Methodology 31
V. Field Measurement Program 35
Model ing Region 35
Historical Information 37
Sampling Rationale and Plan 37
Quality Assurance 39
VI. Application for Selection of Carbon Monoxide
Monitoring Sites 41
General Methodology 41
Model Input Requi rements 41
VII. Summary 48
References 49
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LIST OF ABBREVIATIONS AND SYMBOLS
ABBREVIATIONS
CAMP
CAT-P
CO
EPA
g/m3
g/m/s
HC
km
LDV
LST
m
m/s
m2/s
mg/rrr
NAAQS
NASN
NCC
NEDS
NOAA
N02
NWS
PHS
ppm
SAI
S°? 3
yg/m
UTM
VMT
Continuous Air Monitoring Program
concentration area time-product
carbon monoxide
U.S. Environmental Protection Agency
grams per cubic meter
grams per meter per second
hydrocarbon
kilometer
light duty vehicles
local standard time
meters
meters per second
square meters per second
milligrams per cubic meter
National Ambient Air Quality Standards
National Air Sampling Network
National Climatic Center
National Emissions Data System
National Oceanographic and Atmospheric Administration
nitrogen dioxide
National Weather Service
Public Health Service
parts per million
Systems Applications, Incorporated
sulfur dioxide
micrograms per cubic meter
Universal Transverse Mercator
vehicle miles traveled
SYMBOLS
A
c(x,y,t) -
max
con '
D
pollutant concentration averaging area
pollutant concentration as a function of distance
and time
maximum allowable contribution of any one source to the
concentration
vehicle emission factor
a specified downwind horizontal distance
exposure class
vi
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SYMBOLS, continued
dw -- wind speed class
E — roadway types
F(j»k) -- Figure of Merit
Gmax ~~ desired maximum concentration gradient away from the roadway
r — random variable
H — hourly percentage of daily traffic
h* -- average height of the roughness element within a grid element
k -- von Karman constant
Kx,Ky,Kz -- turbulent eddy diffusivities in the x,y and z directions
Kz. -- vertical diffusivity at height z-j
L -- Monin-Obukhov length
mon' ~~ adjusted annual travel distance
Pol -- temperature correction factor
Q(x,y,t) -- pollutant emission rate
%Tw' "~ cold start correction factor
p -- sensor location at some downwind horizontal distance
s -- downwind horizontal distance
S -- stability function which is a digital version of the Pasquill
stability category
s* -- exposure silhouette area
S* -- lot area
S-j -- downwind position of i-th particle
az -- standard deviation of a Gaussian plume in the vertical
t* — pollutant concentration time-averaging interval
9 -- angle between wind and line source
u — wind speed in the x-direction
U — horizontal wind speed
u* — friction velocity
Ur — reference horizontal wind speed
u'c1 — pollutant flux in the x-direction
v'c' -- pollutant flux in the y-direction
w'c1 — pollutant flux in the z-direction
v -- wind speed in the y-direction
vgs' -- speed correction factor
w — wind speed in the z-direction
2* — height of instrument inlet
Zj -- height of stable layer capping mixing layer or height above
which pollutant concentrations level off to background values
Z-; -- height of particle i
z0 -- surface roughness
Zr — reference height
vii
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CHAPTER I
MONITORING OF AMBIENT AIR QUALITY
The monitoring of ambient air quality is probably the single most
important activity in the study and control of air pollution. "Without
reliable measurements, a legitimate data base cannot be established for
assessing either the degree of deterioration of ambient air or for enforcing
Federal and local regulations, nor can a firm and valid basis be obtained for
examining the cause-effect relationship between the emissions from the pollut-
ant sources and the quality of ambient air. It is thus not surprising that
prior to the early sixties, when recognition of air pollution as a national
problem began to emerge, the primary concern of most air pollution studies
focused on the monitoring of ambient air quality.
In 1953, the U.S. Public Health Service (PHS) set up the National Air
Sampling Network (MASM) to measure particulates in air. In 1962, an intensive
effort, the Continuous Air Monitoring Program (CAMP), was initiated by the PHS
to measure gaseous pollutants in typical urban areas. Since then, numerous
governmental and private investigations related to air quality measurements
have been carried out. With the passage of the Clean Air Act in 1971, ambient
air monitoring programs became an indispensable part of State implementation
plans. Thus, there exists a need to set up guidelines for planning and siting
of established or prospective air monitoring stations. As discussed later,
there have been a number of attempts in the past to provide such guidelines.
However, a rigorous guideline with a sound theoretical basis that is opera-
tionally effective is lacking. In light of the recent advances in the theory
of systems design and in the current understanding of the distribution of the
various pollutants, an in-depth study that will lead to the development of an
objective methodology for siting monitoring stations is apparently feasible.
The present project is devoted to this goal.
REVIEW OF PREVIOUS WORK
Guidelines or rules previously used by designers of monitoring networks
were largely based on accumulated experience derived from practice. A brief
review of these efforts is presented below.
In September 1963, a symposium on environmental measurements was spon-
sored by the PHS at Cincinnati, Ohio. Several papers presented there dealt
in part with the objectives of measuring systems and the design of such
systems. However, information resulting from that symposium was unsuitable
for specific applications.
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Common practices in the design of air monitoring networks before 1969
were summarized in a survey made by Yamada (1970). Based on this study,
Yamada and Charlson (1969) found that differences in the measurement param-
eters of the air sampling devices between networks of stations and between
stations within a single network can be a potential source of error. They
thus pointed out the need for standardization of location and design of air
quality monitoring stations. Charlson (1969) further pointed out that the
siting criteria depend on the particular objective of interest and that the
question of "representativeness" of measured air quality was an important
consideration.
At a U.S. Environmental Protection Agency (EPA) workshop held in January
1970 (U.S. EPA, 1970), four major objectives of a regional air quality moni-
toring program were cited:
To measure and document a region's progress toward meeting ambient
air quality standards.
To determine ambient air quality in nonurban areas of the region.
To improve the reliability of dispersion models.
To provide air quality data during air pollution episodes.
Four criteria were recommended to ensure that data to be collected
satisfy all of the above stated objectives:
Monitoring stations must be pollution-oriented.
Monitoring stations must be population-oriented.
Monitoring stations must be located so as to provide areawide repre-
sentation of ambient air quality.
Monitoring stations must be source-category and/or source-magnitude
oriented.
With these qualitative criteria in mind, the following guidelines were
suggested for the distribution of monitoring stations:
Heavily polluted or "dirty" areas—in most cases 3 to 5 stations will
suffice.
Nonurban stations--2 to 4, depending upon size of the area.
Population-oriented stations —3 to 7.
Source-oriented stations--3 to 5.
Reference (center city) station—1.
Any available stations not accounted for by above should be placed
2
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where the concentration gradation is greatest (as predicted by
dispersion models).
The above recommendations were only speculative in nature and did not define
the criteria for locating monitoring stations.
More recently, guidelines on siting monitoring stations were provided in
U.S. EPA(1971) and U.S. EPA(1975). Quantitative rules for designing a mini-
mally adequate surveillance system were suggested in these documents. For
example, as shown in Figure 1-1, the number of stations that are required to
implement such a system can be determined once the total population and the
type of measuring systems are known. Also, formulae were proposed that use
previous air quality records as an aid in determining the number of stations
needed. Guidelines were provided for locating these stations. For the most
part, the conclusions drawn were based on the analysis of air quality from
stations sited with little knowledge of the essential ingredients that must
be considered in planning their locations.
Of more interest, however, are several theoretical studies carried out
(e.g., Morgan et al., 1970; Seinfeld, 1972; Darby et al., 1974) where the con-
cept of systems design was applied. The design of an air quality monitoring
network was treated in these theoretical studies as an optimization problem
where a set of well-defined constraints related to monitoring objectives were
prescribed.
OBJECTIVES OF AIR QUALITY MONITORING PROGRAMS
Reflection suggests that consideration of the objectives of a monitoring
program must precede the actual design of the monitoring network and the
siting of the stations. Various objectives of past and present air quality
monitoring programs can be classified into three categories:
General Air Quality Monitoring Programs
The goal of general monitoring programs is to provide the two-dimensional
air pollutant distribution near the ground in the region of interest. Judging
from the specific objectives stated below, it can be concluded that State or
local agencies are likely to be primarily concerned with this type of moni-
toring program.
Compliance with Air Quality Standards
As summarized in Table 1-1, the Clean Air Act Amendment of 1971 specified
two types of air quality standards: the primary standards for the public
health, and the secondary standards for the public welfare (Barth, 1970). In
addition to these Federal standards, many State and local agencies have set
up their own air quality standards. Compliance, progress toward compliance,
or lack of compliance with these standards can be determined from the measured
air quality. When excessive concentration levels are registered by the moni-
toring network, indicating the occurrence of an air pollution episode, the
collected data can be used in activating emergency control measures.
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10.000,000
2,000,000 _
1,000,000 -
500,000 I
•M
200,000 -
100,000 -
50,000 I
20,000 -
10,000
// / / /
/////
I/ / /
I'/ I / /
17 // /
" I/ /
[j : i /
N
II
/ /
MECHANICAL-INTEGRATING
(TSP,S02)
AUTOMATIC-CONTINUOUS
(S02,CO,HC,NOx,OXIDANTS)
r i i^ i i i i i i
0 5 10 15 20 25 30 35 40 45 50
NUMBER OF STATIONS
Figure 1-1. Number of stations per air quality control region as
a function of population (U.S. EPA, 1971).
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TABLE 1-1. NATIONAL AMBIENT AIR QUALITY STANDARDS
Pollutant
S00
Particulate
matter
CO
Photochemical
oxidants
HC
N02
Primary
Standards
80yg/m3
(O.OSppm)
o
365yg/m°
(0.14ppm)
75yg/m-"
Secondary
Standards
10mg/m
(9ppm)
40mg/m3
(35ppm)
160yg/m3
(O.OSppm)
160yg/m3
(0.24ppm)
1OOyg/m3
(O.OSppm)
1300yg/mJ
(0.5ppm)
60yg/m3
150yg/m3
1Omg/m3
(9ppm)
40mg/m3
(35ppm)
160yg/m3
(O.OSppm)
160yg/m3
(0.24ppm)
1OOyg/m3
(O.OBppm)
Averaging
Times
Annual
arithmetic mean
24 hours
3 hours
Annual
geometric mean
24 hours
8 hours
1 hour
1 hour
3 hours
Annual
arithmetic mean
ppm = parts per million
Source: Code of Federal Regulations, Title 40, Part 50, pp. 3-28, July 1, 1975.
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Determination of Long-Term Air Pollution Trends
The implementation of realistic air pollution control strategies will
inevitably take time. The effectiveness of these strategies, as reflected by
the gradual changes in air quality, can be evaluated through painstaking
comparisons of historical records of measured air quality data. In addition,
the effects of increases in population and changes in land use on air pollu-
tion can also be assessed by scrutinizing the long-term trends or patterns.
Perimeter Monitoring Programs
In the general air quality monitoring programs described above,
"areawide" measurements are being sought. By contrast, perimeter monitoring
programs are designed primarily to obtain source-oriented measurements. The
eventual goal is the calculation of pollutant fluxes. The need for this type
of program can arise out of the following concerns by State or local agencies.
Enforcement of Air Pollution Control Regulations
Federal or local regulations usually restrict the amount of air
pollutants emitted by any given industrial installation. Although in-stack
monitors can be used to ensure that these regulations are being met, enforce-
ment can also be accomplished by source-oriented monitoring. The emission
flux can be calculated from data obtained from simultaneous monitoring of the
concentration levels and wind speed and direction along the perimeter of the
industrial plant. This flux can in turn be used to estimate whether the
regulations are being violated. For example, see Sperling (1975).
Estimation of Regional Air Pollutant Fluxes
"Line-wise" types of measurements can also be used to settle legal dis-
putes regarding pollutant fluxes across the boundaries of regions under dif-
ferent jurisdictions. For example, a perimeter monitoring program can provide
the data necessary to quantify the transport of airborne pollutants into a
county from an adjacent upwind county.
Special Monitoring Programs
This type of monitoring program is topic- and project-oriented. The
measurements are made over a short time span, on the order of weeks or months,
and are of interest only to a special group. Some of the more important
goals of this type of program are:
Procurement of a Data Base for Regional Model Development
Regional models will eventually become an indispensable part of air
pollution control programs. Measured air qualities at a series of monitoring
stations comprise an important part of the data base for validating such
models. The monitoring data can also aid in the refinement of regional
models.
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Air Quality Impact Studies
A special monitoring program may be needed to establish the baseline
pollution levels when the construction of a new roadway or shopping center is
contemplated. The information can be used in transportation control or land
use planning.
Special Research Studies
Special measuring programs can be initiated to investigate certain specu-
lated cause-effect relationships. Examples are studies correlating indoor air
pollution with outdoor air pollution and carbon monoxide concentration on the
roadway with the driver's blood hemoglobin level.
FACTORS PERTINENT TO MONITORING NETWORK DESIGN
Once the objectives of a monitoring network have been ascertained, an
investigator will be confronted (in the process of planning and designing the
network) with a variety of factors that will affect the final network design.
As it turns out, many of these factors are not necessarily technical. Most
of the technical aspects will be considered in detail later in this report.
For an overview, it is of interest to briefly summarize both the technical
and nontechnical factors.
Technical Factors
Included here are factors that can be either evaluated or quantified;
these are:
Emissions
Because air pollution is a consequence of emissions from sources, this is
probably the most important parameter.
Atmospheric Dispersion and Transformation
The concentration levels of air pollutants at any receptor point are
determined not only by the emission sources, but also by transport, diffusion,
and chemical and physical transformations that take place in the atmosphere.
These factors can be extremely important in the selection of the monitoring
sites.
Costs
/
Both the fixed (capital investment) and variable (recurring) costs for
the construction and maintenance of the monitoring network can also figure
prominently in the decision-making.
Hardware and Software
A variety of factors concerning the instrumentation (such as precision,
response time, and operating conditions) and data reduction procedures may
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also affect the overall design of a monitoring network.
Nontechnical Factors
A number of nontechnical factors exist that can also be important in the
deliberation of site selection. These include:
Accessibility of the monitoring site.
Safety of personnel and security of equipment.
Availability of utilities (electricity, water, etc.).
Ability to accommodate future modifications or expansions.
Compatibility of purpose with other surveillance networks.
The important role played by these factors in the final decision of monitor-
ing sites should not be underestimated. In Yamada's (1970) statistical
survey of past practices in the design and siting of monitoring stations, the
reasons given for locating stations were as follows:
Locating Reason Percentage
Compatibility with station purpose 50.0
and availability of site
Compatibility with station purpose only 25.0
Availability of site only 12.5
Others 12.5
The importance of factors other than technical can be clearly seen from
analysis of the information presented in this table.
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CHAPTER II
DESIGN OF A NETWORK FOR AIR QUALITY MONITORING
Concentration levels of air pollutants are observed not only to fluctuate
with time, but also to vary significantly from one location to another even
within the same proximity. This variability in observed concentrations is
the consequence of both the complex emission pattern and atmospheric disper-
sion and transformation processes in a region. Since most conventional
measuring techniques allow only "point" sampling, the measured data at any
arbitrarily chosen site may or may not adequately represent the air quality
in a larger area surrounding the monitoring site. It is thus clear that a
critical problem in the siting of air quality monitoring stations is to estab-
lish the degree of "representativeness" of the station measurements. In the
first section of this chapter, major components in the design of an air
quality monitoring network are discussed. This is followed by a section
delineating the various issues regarding this question of representativeness
of station measurements. Also described is the methodology adopted in this
study for siting monitoring stations.
COMPONENTS IN THE DESIGN OF AN AIR QUALITY MONITORING SYSTEM
The establishment of an air quality monitoring system, either for
operational use or for special studies, is not a small undertaking. Usually,
it commits enormous monetary and human resources. Therefore, the planning and
design of such a system require the utmost in care.
The concentration of pollutants in a region and the flux of contaminants
into, within, and out of the region are highly variable quantities in space
and in time. The location of the limited number of stations permitted by
financial resources requires careful planning so that measurements that are
not typical of the region as a whole can be avoided. Errors due to local dis-
turbances or instrument malfunctions can be minimized by, an assessment of the
site characteristics, the limitations of the instrumentation, and by estab-
lishment of an adequate instrument calibration and maintenance program.
The design of a system can be conveniently divided into three parts:
Monitoring Station Selection
The success of a monitoring program depends heavily on the appropriate
design of the monitoring network. This design consists of the following
important components:
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Estimation of the Number of Monitoring Stations
This is one of the factors that must be decided early in the planning
stage. Quantitative rules for reaching this decision are extremely useful.
Ideally, the minimally required number of monitoring stations should be de-
termined from a cost-effectiveness analysis based upon optimization tech-
niques. This was apparently not the past practice. As we have discussed in
the review, Figure 1-1, which was based on statistical data, correlates the
number of monitoring stations required with the population of the region.
Despite the fact that people are admitted polluters, their distribution and
number may not reflect the distribution and strengths of problem pollutants.
Therefore, any judgment concerning the number of stations should be based on
more direct factors, such as emissions distribution patterns.
Determination of the Mode of Monitoring Stations
In the past, air pollution monitoring has been primarily accomplished by
fixed-station measurements. This practice is probably related to the fact
that instruments used in these measurements were mostly of a wet-chemical
type, requiring a stable and highly controlled environment. With the rapid
advances in instrumentation hardware, mobile monitoring stations have become
an important monitoring mode. Although mobile stations usually serve as
complementary methods in general air pollution monitoring programs, for perim-
eter monitoring or for special monitoring programs mobile stations may out-
perform fixed stations, either on economical or operational grounds. A
typical description of the design and test of a mobile station mounted on a
van can be found in a report by Ingram and Golden (1969). Other platforms,
such as airplanes or even satellites, have also emerged recently as good
candidates for the mobile monitoring of air pollution.
Selection of Monitoring Sites
This is, or course, the crucial part of the entire planning process.
How close the measurement will fulfill the intended mission of the monitoring
program will be judged by the "representativeness" of the measured data,
which is in turn critically dependent on the selection of monitoring sites.
This issue will be discussed in detail later.
Instrumentation System Selection
Proper selection of the instrumentation system plays an essential role in
assuring a successful monitoring program. Among the more important elements
are the following:
Determination of species to be measured.
Selection of measuring devices, i.e., choice between manual or
automated systems.
Scheduling of sampling frequencies and duration.
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Data Acquisition and Analysis
The final link in the measuring system is the transmission and transla-
tion of data registered by a sensor. The following considerations should,
therefore, be included:
Establishment of data acquisition and processing system.
Formulation of procedures for data reduction and analysis, including
quality assurance.
The foregoing constitutes a brief outline of the three major components
in the design of an air quality monitoring system. However, only the first
one, i.e., site selection, is addressed in this report.
SELECTION OF MONITORING SITES
A rational definition of "representativeness" can be made in relation to
the objectives of the monitoring program. Therefore, this chapter first
presents a set of quantitative definitions of the representativeness of
station measurements in terms of some of the objectives discussed in the first
chapter. Most air quality standards state that specified maximum concentra-
tion levels must not be exceeded more than a certain number of times during
specific time periods. Therefore, for air monitoring programs that have
compliance with air quality standards as their primary objective, a measure
of the representativeness of the data collected at a station would be defined
according to the ability of the measured data to reflect true peak concentra-
tions in the region being monitored. In this case, it is important to locate
monitoring stations at maxima in the spatial concentration distributions. On
the other hand, if the detection of long-term trends is a major monitoring
objective, the station measurements can be considered as representative if
they are sensitive to the effect of changes in regional source emissions on
air quality. It is clear that, to achieve this goal, the ability to measure
the rate of change of concentration levels with respect to time—rather than
the concentration maxima themselves—is most critical in assessing representa-
tiveness. Similarly, other criteria can be established for different monitor-
ing objectives. It should be noted, however, that only the first one—
compliance with, or progress toward attainment of, air quality standards—is
considered in this study.
In order to locate monitoring stations to identify concentrations which
exceed the national ambient air quality standards (NAAQS), one must first
make some extremely important interpretations of the standards. First, one
must decide what constitutes a violation. Clearly, if we measure close to the
tailpipe of an automobile, ambient air quality standards for several pollut-
ants are likely to be violated at all times, even if the vehicle is operating
well within emission standards. Therefore, one reaches the conclusion that
the standards do not apply directly to air spaces which are close to the tail-
pipe. In fact, there is no single definition of where standards should apply.
Similarly, if one were to measure concentrations at curbside along a busy
street, the chances are good that a large number of measurements exceeding the
standard could also be expected. Clearly we cannot measure all of them, nor
11
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is it necessary. It is thus important to clearly define under what situations
the NAAQS are exceeded. Obviously this question cannot be resolved without
other considerations, such as population exposure.
In the present study, the method used for identifying concentrations
which exceed the NAAQS can be expressed in terms of a Concentration Area Time-
Product (CAT-P).
CAT-P = J ( JJ c(x,y,
t)dxdy I dt
(II-l)
t*
where A represents an area and t* a time interval for averaging the concen-
trations. A concept similar to this was proposed by Duckworth (1967) for
describing the severity of air pollution episodes. The time intervals can be
selected to match those as specified in the ambient air quality standards
(for example, 1 hour and 8 hours for carbon monoxide). The choice of aver-
aging area A is dictated by considerations including the spatial resolution
of emissions inventory, the size of the region under consideration, and the
storage capacity of the computer to be used.
Once a clearly defined interpretation is obtained, the next problem in
the selection of monitoring sites is the determination of pollutant concen-
tration variations. Since air pollution is the direct consequence of
emissions from sources, an inventory of emissions, including magnitudes and
temporal variations, is clearly one of the most important inputs. Meteorolog-
ical parameters, such as wind speed, wind direction, and atmospheric stability
as derived from vertical temperature profiles, are also indispensable in
determining the distribution of air pollutants. With the application of a
mesoscale air quality simulation model, pollutant distributions on a regional
scale can be obtained for varying emission and meteorological conditions.
Finally, since pollutant concentrations are measured only at particular
points in a region under consideration, the measurements can be expected to be
strongly affected by environmental factors in the immediate vicinity of moni-
toring sites as selected by the mesoscale model. For example, the measure-
ments on a local scale can be affected by localized sources that can cause
local concentration levels to be significantly higher than the average for the
area. Local structures can also influence either the air flow field or
pollutant dispersion in the vicinity of the monitoring site. At this micro-
scale level, important parameters that are expected to influence the pollutant
distributions include:
Relative magnitude of the local sources.
Distance from local sources.
Microscale meteorology.
12
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Natural topography or man-made structures in the neighborhood of the
monitoring site.
Thus it is clear that the actual siting of a monitoring station requires the
knowledge of pollutant distributions on the microscale. This problem is dis-
cussed in detail in Chapter IV.
APPROACH ADOPTED IN THIS STUDY
The primary goal of the monitoring network to be designed is to identify
concentrations which exceed the NAAQS. Therefore, stations are to be located
in such a fashion as to minimize the probability of not detecting'a violation.
Carrying out this objective requires interpretation of a number of -related
Issues.
The first issue, as discussed in the previous section, concerns the
interpretation of the NAAQS. A Concentration Area Time-Product (CAT-P),
given by equation II-l, is used for identifying the concentrations which
exceed the NAAQS. Assuming that this is appropriate, the use of a mesoscale
model would be ideally suited for computing the CAT-P as follows:
CAT-P = c(x,y,t)AxAyAt (II-2)
where
c(x,y,t) = computed concentration
Ax,Ay = step size in the x- y- directions
At = step size in time.
Although, in principle, the mesoscale model can be used to obtain annual or
long-term averages, it is not practically feasible. Because it is implicitly
assumed that the monitoring system is to be designed for long-period opera-
tion, the question then arises as to how to include the long-term fluctuations
in the pollutant concentrations due to not only daily but also seasonal vari-
ations of the meteorological parameters in the area. The problem can be
treated by using a frequency-weighted concentration expressed as an index
called the Figure of Merit.
The Figure of Merit for a particular point can be defined in general as
the sum of the products of the ground-level concentrations and the associated
frequencies of occurrence. Two types of Figures of Merit are of particular
interest to the present study—one for exceeding ambient air quality standards
and one for general air quality monitoring.
For each of the grid points in the modeling region, the Figure of Merit
for exceeding standards can be defined as follows:
13
-------
1, if AAQS are exceeded at \ /probability of\
grid point j,k under ] . (meteorological I (II-3)
meteorological pattern £/ \ pattern £ /
0, if not ' \
Thus, this Figure of Merit is a measure of the probability that each grid
point is likely to indicate when an air quality standard is exceeded.
Similarly, a Figure of Merit for general air quality monitoring can be
defined as
/concentration at grid \ /probability of\
P (' b\ - y I point j,k under tneteor-1 • I meteorological I (II-4)
i- U,KJ - /_, lological pattern £ / \pattern £ /
* \ / \ /
This index represents an average pollutant concentration at each grid point
as weighted by the frequency of occurrence.
In order to implement the scheme, it is necessary to specify a set of
scenarios which completely describe the meteorological conditions in the area
of concern. This can be accomplished by examining climatological data in the
region of interest.
Once a set of meteorological scenarios is determined, the mesoscale model
can be exercised to provide the corresponding pollutant distributions. The
resultant ground-level concentration distributions, in conjunction with the
associated frequencies of occurrence, can then be used to compute the Figure
of Merit for siting potential monitoring stations. A computer program has
been prepared to perform these calculations. To illustrate the use of this
program, values for Figure of Merit defined by equation II-4 were computed
using simulated surface carbon monoxide (CO) distributions in ppm for a
1-hour period during peak traffic for two separate days (see Figures II-l and
II-2) for a hypothetical region with 1-by 1-km grid squares. Figure of Merit
values were calculated under the assumption that these situations have an
equal probability of occurrence. An isopleth plot of these values is
presented in Figure II-3. The computer program also takes the resultant
values for the Figure of Merit and searches for the highest values which are
not adjacent to other higher values without an intervening trough. As shown
in Figure II-4, locations for the 16 highest Figures of Merit were identified,
ranked, and plotted.
In conclusion, it should be emphasized that the procedures described
above are by no means definitive. They are proposed here only to illustrate
an idealized concept for the objective and systematic determination of air
quality monitoring stations as outlined in this chapter. Modifications of the
basic approach are certainly possible and probably desirable. For example,
14
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Figure II-l.
Simulated surface carbon monoxide distribution across a
hypothetical region for the hour of peak traffic for a
particular day. Grid squares are 1 km and isopleth in-
crement is 1 ppm. (Day 1)
15
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Figure II-2.
Simulated surface carbon monoxide distribution across a
hypothetical region for the hour of peak traffic for a
particular day. Grid squares are 1 km and isopleth in-
crement is 1 ppm. (Day 2)
16
-------
Figure II-3. Figure of Merit distribution for data in Figure II-l and II-2.
Isopleth increment is 1 ppm.
17
-------
m
Figure II-4. Ranking of the potential monitoring sites by Figure of Merit
for data in Figure I1-3.
18
-------
normalization of the Figure of Merit as defined by equations 11-3 and 11-4 or
inclusion of demographic information may yield new definitions which are
intuitively more appealing. On the other hand, a close examination of the
averaging time is required to clarify certain ambiguities in the definitions
of the Figure of Merit. These questions apparently warrant future considera-
tions.
19
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CHAPTER III
ANALYSIS AT THE MESOSCALE LEVEL
The approach adopted in this study for siting monitoring stations is
based on the concept of a CAT-P for identifying the locations of grid areas
in a region whose concentrations exceed the NAAQS. Since grid models normally
compute space- and time-averaged pollutant concentrations, it appears that
this type of air quality simulation model is ideally suitable for such an
application. The first section of this chapter is thus devoted to a discus-
sion of the mesoscale or regional model selected for this project. The
second section then addresses the very important question concerning the
incorporation of an improved vertical diffusivity algorithm into the model.
A DESCRIPTION OF THE MESOSCALE AIR QUALITY SIMULATION MODEL
The goal of the mesoscale analysis is the accurate determination of pol-
lutant distributions on the regional scale under a variety of emission and
meteorological conditions. This can be achieved by use of a mesoscale air
quality simulation model. The model selected for the present project was
developed by Systems Applications, Inc., (SAI) (Reynolds et al . , 1973; Roth
et al., 1974; Reynolds et al . , 1974). The model is based on the following
simplified form of the equation of mass balance for an inert pollutant
species like carbon monoxide (CO):
If *«£* v£* -if ^1), )(K2f) (III-l)
where: c is pollutant concentration; t is time; x,y, and z are the space
coordinates; u,v,w are wind speeds in the x,y, and z directions; and K , Ky,
and KZ are turbulent eddy diffusivities in the x,y, and z directions.
This partial differential equation in four dimensions (x,y,z,t) is solved
by a finite-difference technique. The method of fractional steps (Yanenko,
1971) is applied by splitting the original 4-dimensional equations into three,
2-dimensional equations in (x,t), (y,t), and (z,t), respectively. The solu-
tion is explicit and direct in the line (x,t) and (y,t) fragments and implicit
and iterative in the (z,t) fragment. The emissions from areal and point
sources within the region under consideration enter the model equation as
boundary conditions, via the following expression:
20
-------
-K2 ff = Q (x.y,t) (III-2)
1 z=0
where:
Q (x,y,t) is the pollutant emission rate.
The top of the modeling region is usually set at the base of an elevated
inversion or stable layer capping the atmospheric mixing layer. With the
modified diffusivity scheme, which is discussed in the next section, this con-
dition can be relaxed. For example, under a stably stratified atmosphere,
the top of the modeling region can be chosen at a height above which pollutant
concentrations level off to the background values.
The 3-dimensional wind field (u,v,w) as a function of time is considered
as an input that must be prescribed. In the present model, the 3-dimensional
wind field is calculated based upon surface wind measurements via an inter-
polation algorithm (Liu et al., 1973). The generation of ground-level wind
speed and direction at a particular grid cell in time is based on the inter-
polation of the field measurements using a weighted inverse square of the
distance between station locations and the grid cell under consideration.
Once the surface wind is computed, the wind speeds and directions in upper
levels can be calculated based on the continuity equation of mass.
ji
One of the important assumptions invoked in the derivation of the model
equation discussed above is the gradient transfer approximation, also known as
the K-theory. This approximation is tantamount to assuming that the turbulent
transfer of pollutants in the atmosphere is proportional to the gradient of
the mean concentration. For the SAI model, according to a sensitivity study
carried out by Liu, et al., (1975), an order-of-magnitude change in the hori-
zontal eddy diffusivity will only affect the predicted surface concentration
by less than 3 percent. Thus, a constant value of 50 m^/s is used which is
compatible with a grid square up to a few kilometers on a side.
In a previous study, a simple scheme incorporating a height and wind-
speed dependence was used for the prescription of the vertical eddy diffusivi-
ty (Reynolds, et al., 1973). It was found to be satisfactory for the
Los Angeles basin during daylight hours. However, the application of the
model to other regions of interest and for other diurnal periods of the day
requires the use of a more generalized diffusivity algorithm. The following
section is devoted to a discussion of the basic limitations of the K-theory
and development of such an algorithm.
APPROXIMATION OF TURBULENT TRANSFER BY EDDY DIFFUSIVITIES
Like many other related studies of the atmosphere, a difficult and also
crucial part in the simulation of pollutant dispersion is the attainment of a
reasonable scheme to represent the turbulent, processes. Theoretical studies
based on higher closure schemes for hierarchies of turbulence moment equations
21
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have recently been carried out (e.g., Deardorff, 1970, 1972). However, these
have been restricted in application to certain special cases and require con-
siderable computational effort. Thus, an alternative based on the simple
concept of turbulent eddy diffusivities—the K-theory—is adopted in most
atmospheric dispersion models of the type used here. Analogous to the molec-
ular diffusion, the K-theory speculates that a pollutant flux in the direc-
tion of decreasing concentration is established as a result of turbulent
fluctuations. The magnitude of this flux is proportional to the gradient of
the average concentration. Thus:
• - «* S£ (III-3)
(III-5)
The limitations of models based on the K-theory or the gradient transport
theory are well known. They can be generally grouped into the following two
categories:
Length and time-scale constraints;
Directional constraints.
The first type is related to the spatial and temporal homogeneity of the mean
concentration field. Corrsin (1974) summarized the conditions necessary for
satisfying such constraints:
The transport mechanism length scale must be much smaller than the
distance over which the curvature of the mean transported field
gradient changes appreciably.
The transport mechanism time scale must be much smaller than the time
during which the mean transported field gradient changes appreciably.
The transport mechanism length scale must be essentially constant
over a distance for which the mean transported field changes
appreciably.
The second constraint arises when, for example, the Reynolds stress, a
second-order tensor, is replaced by an inner product of a second rank tensor
and a vector. The conditions for satisfying this constraint are more
difficult to delineate. However, qualitative estimates for the validity of
22
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the mesoscale model based on the equation of mass balance for a pollutant
species were obtained by Lamb and Seinfeld (1973). The result seemed to indi-
cate that the gradient-transport approach is plausible under a variety of
conditions.
Based on a comprehensive review of the literature, an algorithm for pre-
scribing the eddy diffusivity in the vertical was adopted. In the surface
layer, the following general formula was used:
(z0() = a functional relationship to be specified.
This formula is the result of the similarity theory for the constant-flux
surface layer (Businger et al., 1971). For the neutral case, the -function
equals unity. For the stable or unstable case, the ^-function is greater or
less than one respectively. The following empirical expressions for the
<|>-function were proposed by Businger et al., based on observational data.
For the stable case (L > 0)
(f) = 1 + 4.7 t^\ (III-7)
For the unstable case (L < 0)
-1
(f) = (] - 15 f)
The friction velocity was determined by the following equation,
kU
u* = T^ (III-9)
23
-------
where U denotes a reference horizontal wind speed measured at a reference
height, Zf, and
f = In
or
f = In
+ 2 tan
-1
4.7 Zr ' zo
*fe
1
- In
- 2 tan
'1
(111-10)
(stable)
(III-ll)
(unstable)
Above the surface layer (|L| <_ z <. Z^), a second-order interpolation
formula first proposed by O'Brien (1970) was utilized:
(z -
(111-12)
where Z. = height of stable layer capping mixing layer or height above which
pollutant concentrations level off to background values (stable
conditions).
K = vertical diffusivity of height i.
K(|L|)=dKz
__
= IL.
The implementation of the proposed diffusivity scheme requires an esti-
mate of the Monin-Obukhov length. The Monin-Obukhov length can be in general
related to the Richardson numberjwhich can be, in turn, determined experimen-
tally (McElroy, 1969). The estimate of this length was, however, accomplished
in the present study via the following formula which relates the Monin-Obukhov
24
-------
length to the surface roughness, ZQ, and the stability function, S,
L =
(a^ + a2S ) z
-1
(111-13)
where
al =
a2 =
bl -
bo =
bo =
0.004349
0.003724
0.5034
0.2310
0.0325
This formula is a result of the best-fit of observational data reported by
Colder (1972). The stability function, S, a digital version of the Pasquill
stability category (see Table III-l), can be calculated as follows:
S = \ (3 - dw + |de|) 'Sign (de)
(111-14)
where
sign (d ) =
1 de > 0
0 d_ = 0
-1 d < 0
e
and dw and d are the wind speed class and exposure class. These are defined
as follows:
r 0 <_ U < 8 m/s
2 r
4 U > 8 m/s
I
25
-------
3
2
1
0
-1
-2
strong
moderate
slight
heavy overcast
daytime insolation
day or night
nighttime cloudiness.
TABLE III-l. PASQUILL CATEGORY AND STABILITY FUNCTION
Pasquill Category*
A
B
C
D
E
F
Stability Function, S
-3
-2
-1
0
+1
+2
*Turner (1969)
The prescription for the computation of the vertical eddy diffusivity is
now completed. The primary inputs are the wind speed, the reference height
Z-, the exposure class, and the surface roughness. The result of a sample
calculation is included in Figure III-l. Note that an approach for the deter-
mination of vertical eddy diffusivities generally similar to the above was
suggested by Myrup and Ranzieri (1975).
26
-------
ro
ZOO
I 100
002 0.04
0.2 0.4
DIFFUSIVITY (m2/$M)
WIND SPEED =2 in/sec
STABILITY CLASS =E
REFERENCE HEIGHT =2BOm
0
O
O
O
O
O
O
0
O
1 1
O
O
O
O
O
O
0
O
O
1 1
O
0
O
O
O
0
0
O
0
1 1
2.0 4.0
Figure III-l. Calculated vertical diffusivity as a function of height.
-------
CHAPTER IV
ANALYSIS AT THE MICROSCALE LEVEL
The primary objective of the microscale analysis is to pinpoint the
monitor in a microscale environment within the area identified by the meso-
scale analysis. The major task is to find a sensor location at which the
sensor's readings are not unduly influenced by local sources.
OVERVIEW
The problem of selecting monitoring sites on the microscale arises
because the mesoscale model predictions only provide an average estimate of
the pollutant concentration over a large geographical area (typically this
area represents a 1-km by 1-km square), while a conventional sensor measures
only a point value at some arbitrary location within the grid square. Depend-
ing on the emissions and meteorology pattern and the relative sensor location,
the sensor may or may not provide a measurement which can be considered as a
representative value for the grid square. Clearly, what is needed is an anal-
ysis at the microscale to establish a methodology for locating the sensor on
the microscale and a method for relating its readings to a mesoscale average
value.
The only previous work related to the siting of monitors on a local scale
was that of Ludwig and Kealoha (1975) which forms the basis of the current EPA
siting guidelines for CO (U.S. EPA, 1975). The essential feature of their
analysis consisted of defining a variety of locations at which a monitor could
conceivably be placed (street, canyon, corridor, neighborhood, etc.) and the
various types of measurement one could make (peak, average, etc.). As a
criterion for siting for corridor and neighborhood stations, Ludwig and
Kealoha characterized the roadway as a Gaussian line source,
c- -=® exp
Uozsin 9
where:
c = concentration (g/nr)
Q = source strength (g/m/s)
U = horizontal wind speed (m/s)
28
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c?z = standard deviation of a Gaussian plume in the vertical (m)
9 = angle between wind and line source (degrees)
z* = height to instrument inlet (m).
The. dispersion coefficient, a , was represented by the equation
a = as
(IV-2)
where a and b are functions of the Pasquill stability class and s is downwind
horizontal distance. When the conditions that either the contribution from
any one source be small or that the gradients of such contributions be small
were imposed, the following inequalities were derived,
2Q
? Ua sin 6 < max
1
Ac
c sin 6 As
< 6.
max
where Cmax is the maximum allowable contribution of any one source to the con
centration and Gjnax is the desired maximum concentration gradient away from
the roadway. With the following typical values for the various parameters,
6 =40 degrees
U = 1 m/s
= °-001 /m3
Gmax = 0.002/m
Q .= 0.07 g/m/s
they were able to determine the minimum distance between a large roadway and
a neighborhood monitoring site. Given the uncertainties in the Gaussian model
and the assumptions involved in its implementation, this approach may have
furnished a qualitative analysis, but it did not provide quantitative informa-
tion on pollutant concentrations at such site locations under a variety of
atmospheric and emission conditions. In addition, the method failed to pro-
vide information on the representativeness of data obtained from stations
located at places other than their suggested locations.
29
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REPRESENTATIVENESS OF MEASUREMENTS ON THE MICROSCALE
Fundamental to the nricroscale analysis is the concept of the
representativeness of the measurements; i.e., given a point-measurement value,
at what monitor location can the measured value be considered as representa-
tive of an average concentration value over a predetermined length scale?
Schematically, this situation is illustrated in Figure IV-1 for a single
roadway with emission rate, Q, and sensor at distance, p, downwind and height,
z*, above the roadway.
ROADWAY'
SENSOR
i
it
yX
Q
>**
c
T
z*
Figure IV-1. Schematic diagram of roadway and sensor location.
To simplify the analysis, a steady state condition was assumed for an infinite
line source normal to the s-direction. The wind was assumed to blow in the
s-direction and have a speed varying with height. For this case, the equation
of mass balance for a species reduced to:
3?
31
i^ 9c
Kz 31
(IV-3)
in which both wind speed, U, and vertical diffusivity profiles must be
specified a priori. In addition, the emission rate enters the equation as a
boundary condition:
K
KZ 3Z
z=0
* -Q.
(IV-4)
30
-------
Since most sensors are located at a recommended height of 3 meters above
the ground (z* = 3 meters), variations in the vertical were eliminated in the
final analysis by merely examining those concentration profiles at the
3-meter level.
To mathematically quantify the meaning of representativeness, the mean
value theorem of calculus expressed as
c(p,3) D =
c(s,3) ds
0 < s < D
D = distance downwind
of roadway
(IV-5)
was used, from which the mean value of the concentration in the interval
[0,D] can be computed and the location of the sensor p predicting this value
obtained. Graphically, these two quantities are illustrated in the following
figure:
Figure IV-2. Relationship of sensor location to measured concentration,
For various emissions rates, wind profiles, and Pasquill stability
classes, a series of charts can be constructed relating p to D.
SOLUTION METHODOLOGY
Two basic difficulties were encountered in trying to solve the governing
equation:
31
-------
u l£- At r (IV-8)
> 32
-------
where
K = vertical diffusivity at height z.
r = normally distributed random variable between ± °°
with mean zero and unit standard deviation
Wind speed profiles near the ground may be represented by a power law
model of the form
U
U
in which the exponent n is a function of stability and r refers to parameter
values at a reference height. The n values calculated as a function of atmos-
pheric stability can be related to Pasquill stability classes. Table IV-1
lists stability dependent values of mass provided by DeMarrais (1959). The
n values were assumed applicable to Pasquill classes in the manner shown in
this table. Since automobiles generate a wake as they move, it could be
anticipated that the wind speed does not smoothly decay to zero as the sur-
face is approached as predicted by the power law model. According to a study
carried out by the California Department of Transportation (Ranzieri and Ward,
1975), the movement of a vehicle gives rise to a 4-meter-high mixing cell
"above the roadway" in which both dispersion and meteorological parameters
are roughly uniform. For this reason, the wind speed profile was modified in
such a way that in the range between 0 and 4 meters above the ground the
speed would be set to the 4-meter value.
TABLE IV-1. EXPONENTS FOR WIND PROFILE
Pasquill
Stability Class Exponent n
A 0.1
8 0.15
C 0.20
D 0.25
E 0.35
F 0.30
33
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Finally, estimates of the eddy diffusivities were computed from
algorithms discussed earlier in Chapter III as based on the semi-empirical
theory discussed earlier which includes the effects of wind speed, surface
roughness, stability class, and vertical height.
To facilitate the computations in the microscale analysis, the Monin-
Obukhov lengths for typical values of surface roughness around a roadway were
estimated. These lengths as a function of prevailing atmospheric stability
class are listed in Table 1V-2. As was the case with the wind speed profiles,
the diffusivity values were assumed to be uniform in the interval between zero
and 4 meters above the surface and equal to the 4-meter value.
TABLE IV-2. MONIN-OBUKHOV LENGTH VERSUS PASQUILL STABILITY CLASS
Stability Class Monin-Obukhov Length L (m)
A -9.09
B -22.9
C -133.3
D «>
E 159.2
F 16.0
Although the methodology developed here was for the simple case of a
single roadway, this method may be extended to more complex situations in
which the same type of analysis is possible. For example, if several road-
ways have to be dealt with, the contributions of each roadway must be super-
imposed.
34
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CHAPTER V
FIELD MEASUREMENT PROGRAM
Application of mesoscale analysis requires that the validity of the
model used be demonstrated for the region to which it is applied.
Accordingly, an observational program must be developed to support the verifi-
cation effort. One approach toward establishing such a program proceeds as
follows:
Various attributes of the area are considered such as the size of the
region, land use, population density, emission patterns, topography, etc.
Examinations of historical information and current data collection activities
are made in order to determine their completeness. Based on this examination,
any supplemental monitoring needs can be identified. Thus, pollutants to be
monitored and meteorological conditions to be observed can be specified. A
decision is made on the number of meteorological and air quality monitoring
sites, the specific locations, and the instrumentation required. Other
requirements are specified such as resolution, frequency, duration, and
extent of observations and required accuracy, specificity and precision of
the measurements.
MODELING REGION
To provide a reference system for measurement site locations, a grid
structure should be developed for the modeling region selected. The bound-
aries of the modeling region must be defined. A grid size is to be specified
for the area which would be oriented on the Universal Transverse Mercator
(UTM) grid system. The UTM coordinate system is suggested in order to con-
form to the format of available data. This grid also provides a reference for
topographic information and emission source locations.
In a modeling region such as the Las Vegas Valley, the ridgelines of the
surrounding mountains define the boundaries for which a grid structure could
be developed. A map of this region (Figure V-l) shows the flat, gradually
sloping valley surrounded by the Las Vegas Range to the north, Frenchman and
Sunrise Mountains to the east, the Spring Mountains to the west, and the
McCullough Range to the south. The urban area consists of vacant desert
scattered among the residential developments. The population of over 300,000
people is distributed over a larger area than other urban communities of
equivalent population. Note that a limited access interstate highway trav-
erses the city and major highways crisscross the valley providing access from
Arizona to northwestern Nevada, and from California to Utah. A large grid of
four-and-six-lane arterial streets and an intermeshed network of secondary
roadways accommodate local traffic in the valley.
35
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-• *A
/ "^T^-.
Figure V-l. Map of Las Vegas Valley.
36
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HISTORICAL INFORMATION
A survey and preliminary analysis of available aerometric data are
necessary for planning of the field measurement program. For example, this
will provide guidance for locating field sampling equipment, developing
meteorological scenarios, and determining periods of field-data collection
for both continuous monitoring and intensive measurement efforts.
Region-wide aerometric data may be available from Federal, State, and
local agencies, particularly with regard to the monitoring and other
provisions of the Clean Air Act. These agencies and private concerns may
also have such data for specific areas in the region regarding environmental
impact assessments. Many of the programs may be ongoing and yield quantita-
tive information useful in developing pollutant emission inventories.
Usually, meteorological data are collected routinely by the National
Weather Service (NWS) at a local airport station or by the military at their
installations. In some regions, the NWS has data available for locations
in metropolitan areas. In addition, weather stations are operated in cooper-
ation with the NWS by other Federal agencies often at small airports and
military establishments. Data from these and other sources are compiled and
stored at and are available from the National Oceanic and Atmospheric Admini-
stration's (NOAA) National Climatic Center (NCC) in Asheville, North Carolina.
Inquiries should be made to the NCC regarding such data including climatic
and other summaries and results of special studies using the data.
NWS airport stations providing near-surface weather information are
located about 90 km apart. They nominally report at 1-hour intervals weather
information including that on cloud cover, visibility, barometric pressure,
air temperature and moisture, precipitation, and wind speed and direction.
Some NWS, military, and cooperative stations operate on less than a 24-hour
per day schedule and/or report only certain of the above weather elements.
NWS stations providing upper air data on temperature, moisture, and wind
speed and direction are situated about 370 km apart. Vertical soundings of
these parameters are made nominally at 0000 and 1200Z (international standard
time). Some military installations also take such soundings on this time
schedule. Additionally, some NWS and military weather stations, including
many of the above, take vertical soundings of only wind speed and direction;
the frequency and schedules for these soundings are often dependent upon the
local station responsibilities and, hence, may be irregular.
SAMPLING RATIONALE AND PLAN
A period of sampling must be chosen both for the continuous monitoring
and the intensive measurement programs. The examination of historical
aerometric data may allow a determination of both the primary season(s) of
interest and the meteorological situations for which abnormally high levels
of air quality are likely to occur in the region under consideration.
In a region such as the Las Vegas Valley, the period for full scale
sampling of CO both for the routine and intensive programs would be November
37
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through February, inclusive. From a meteorological standpoint the conditions
potentially conducive for high CO concentrations occur most frequently during
this period. This might be expected since CO is emitted primarily from near
ground-level sources and the ventilation rate, which is comprised of the
products of mixing depth and the average wind speed through the depth, is
smallest during this period. The available historical data on CO levels in
the Las Vegas Valley indicate that the CO values are in phase with this mete-
orological pollution potential.
Las Vegas, which is situated in semi-arid terrain, experiences a desert-
type climate characterized by nearly clear skies, a large diurnal temperature
change, and a strong nocturnal surface-based inversion. Periods of minimal
pollutant dispersal, both horizontally and vertically, are associated with
the occurrence of the nocturnal inversion, especially with the normally
decoupling of air from upper levels with higher wind speeds from the air
within the inversion layer. The inversion begins to dissipate and its base
begins to rise shortly after sunrise. Because of the relatively large tem-
perature increase in the portion of the inversion nearest the ground, its
base rises slowest in the first few hours after sunrise. The inversion
normally reforms at the surface beginning around sunset. The times of sunrise
and sunset on an annual basis for the local area are shown graphically in
Figure V-2. As is shown in this figure, sunrise occurs later and sunset
earlier during the November through February period than during the rest of
the year. Where it is demonstrated that motor vehicles generate the major
portion of the emissions of CO, times of peak emission are critical. Locally,
these occur between 0630-0830 and 1600-1800 local standard time (LST). Thus,
the times of peak emissions coincide most often with diurnal periods of mini-
mal pollutant dispersal in the November through February period. Hence,
highest short-term (on the order of a few hours) concentrations of CO can
reasonably be expected to occur most frequently during this part of the year.
Periods for short term intensive sampling can usually be chosen on the
basis of forecasts of synoptic scale weather patterns over the region of
interest. In the Las Vegas Valley, days with particularly limited atmospheric
dilution could be selected. Such situations generally occur when the valley
is under the direct influence of a slowly moving or stagnant high pressure
area. A major constraint on intensive sampling in the valley would be the
elimination of weekends for which an adequate CO emission inventory cannot be
established from available data.
Weather forecasts for intensive sampling can be made using facilities
available at the nearest NWS station. Here, surface and upper air data
described previously and special aircraft and ship weather reports for at
least the contiguous U.S. are routinely collected by teletype. Usually,
charts are available covering this area plus portions of the adjacent oceans.
They contain observed and forecast weather information for ground level and
various constant pressure surfaces above ground. Additionally, special fore-
cast information is available, generally resulting from simulations with
synoptic-scale meteorological models.
Specific forecasts in this manner are developed for periods up to 72
hours in advance. In reality, forecasts for periods 48 to 72 hours in advance
38
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0400 .
w
es
2
ao
55
m
0700
1ST
1600
vt
ts
V*
3
1900
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Figure V-2. Sunrise and sunset times for Las Vegas
(Local Standard Time).
normally provide guidance regarding general circulation periods. Those for
periods less than 24 hours can usually be expected to provide reasonably
accurate information on details and timing of specific weather events. Hence,
only one day's notice can normally be expected for intensive periods. Such a
constraint requires personnel on a standby status based on the longer term
forecasts.
Based on predominant synoptic-scale and locally induced wind patterns
(i.e., upslope and downslope winds), topography, and traffic data, initial
sampling sites can be selected to provide data for model verification and
supplementary data for use in the design of a CO monitoring network. If coop-
erative arrangement can be made with other monitoring agencies, only a few
new sites may be required to supplement those existing.
QUALITY ASSURANCE
Quality assurance takes on several aspects in the field project. First,
if field data are collected by several agencies working cooperatively, it
will be necessary to ensure that measurements within the individual programs
are comparable. Second, data and data handling procedures must be compatible
and as error-free as possible.
39
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A significant way to ensure uniformity in air quality measurements is to
require that all span gases used in field instrumentation be calibrated
against the same National Bureau of Standards standard reference gas. A lead
agency should provide a cylinder of known concentration to be used as a blind
sample for each agency for data correlation purposes.
Routine and preventive maintenance and frequent instrument calibrations
should also be carried out by each agency. Standardized check lists and log
books should be kept for all instrumentation. Routine maintenance schedules
should be specified in an operator's manual developed for the project. To
ensure proper identification, strip charts should be labeled at each calibra-
tion with the site name, the span value, time, date, and other information as
is applicable.
A complete quality assurance plan should be developed for the field
program following procedures and format suggested in the EPA (1976b) publica-
tion, Quality Assurance Handbook for Air Pollution Measurement Systems.
40
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CHAPTER VI
APPLICATION FOR SELECTION OF CARBON MONOXIDE MONITORING SITES
An air quality simulation model is an essential component of planning
studies in air pollution control because there commonly exists a need to
establish a quantitative relationship between effluent emissions rates and
the magnitude of ground-level contaminant concentrations that result from
these emissions. Because each region may have unique topographical and mete-
orological conditions, and specific pollutant emissions patterns, the model
is a useful tool in the design of a monitoring network to detect concentra-
tions exceeding the NAAQS. This chapter discusses the development of a
design for an ambient air monitoring network for carbon monoxide using the
techniques developed in earlier chapters.
GENERAL METHODOLOGY
Since the primary objective of the air monitoring network is to document
compliance with or progress toward meeting of the NAAQS, the CAT-P is used to
identify the locations where concentrations in excess of the ambient CO
standards are most likely to be found. These are computed with the use of
the mesoscale air quality simulation model. A field measurement program is
established to provide air quality and meteorological data for model valida-
tion, and for simulation to yield projected space-time average concentration
distributions of CO.
Prevailing meteorological patterns as well as the frequency of
occurrence associated with each can be applied, with the projected concentra-
tion distributions, to yield a frequency weighted average of concentrations
called Figure of Merit. A mapping of these values provides the basis of
selection of the locations and number of sites.
MODEL INPUT REQUIREMENTS
The SAI model, developed as a nonlinear, second-order partial
differential equation, requires a numerical solution with the aid of a digit-
al computer. The overall and specific operational characteristics of the
computer programs which make up the model are discussed in a separate docu-
ment as a User's Guide (EPA, 1977). The program package consists of a data
preparation portion and a simulation portion. As a general use model, inputs
to the programs are parameters which specify the characteristics of the region
to be modeled. Because of the number and types of parameters involved, only
a major set of input requirements is discussed. The User's Guide should be
consulted for further details on specific input parameters.
41
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Emissions Inventory for Carbon Monoxide
The emissions inventory can be developed in accordance with the EPA
(1973) publication, Guide for Compiling a Comprehensive Emissions Inventory.
Emission factors are compiled in EPA (1976a) AP-42. Compilation of Air
Pollutant Emission Factors, second edition, and its five supplements.
Essentially, the inventory involves the summing of emissions from point
sources, area sources, and mobile sources, resulting in emissions per hour
per grid square.
The emissions factors given in AP-42 can be used to compute total
emissions. How the emissions might be spatially distributed throughout the
day, week or year, must be developed from other data such as traffic patterns
and densities.
An emissions inventory depends upon quantifiers such as the number of
kilowatts produced per hour or the number of automobiles traveling at a
certain speed. For light duty vehicles, emissions have also been shown to
depend on ambient temperature. Thus, a temperature correction factor and a
cold-start correction factor which is a function of temperature must be
applied to their emissions. For example, over the temperature range from
0° to 20°C the temperature correction changes from 1.57 to 1.06 and the cold-
start correction factor from 1.3 to 1.1 (for 30% cold starts). This depend-
ence on temperature suggests that any one inventory cannot completely charac-
terize emissions. A special inventory is necessary for each different set of
temperature conditions. The result is an inventory of emissions organized
into separate parts with emissions for which possible temperature dependence
has not been quantified, making up one subtotal and temperature-dependent
emissions making up the other. For this latter part, a computer code can be
developed to apply temperature and cold-start corrections for the given series
of temperatures that describe each hour of the day chosen. After the correc-
tions are applied, the various subtotals can be added, giving an emissions
inventory specific to a given day.
For an emissions inventory in the Las Vegas Valley, the following would
be considered: point sources (power plants and industrial plants); area
sources (space heating); and mobile sources (railroads, airplanes, and auto-
mobiles). It was noted that total mobile sources in the Las Vegas Valley
were responsible for 93 percent of the total emissions, with 84 percent
attributed to gasoline-powered vehicles.
Much of the point source data required for most regions is available
through the National Emissions Data System (NEDS). For industrial sources,
the assumption is usually made that emissions are uniform over all operating
hours. For power plants, emissions can be estimated from data obtained on
hourly fuel oil and natural gas use from several chosen consecutive days
during the CO season, the first of which is chosen randomly. These values,
averaged for each hour and with emission factors applied, yield emissions per
hour. Natural gas not supplied to power plants and industrial users can be
assumed to be used for space heating. Local natural gas suppliers can provide
estimates of residential, commercial, and industrial sales. Using these data,
calculated emissions can then be parceled out to each grid square according to
42
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population density.
Aircraft emissions can be calculated for local airports with scheduled
commercial flights from data listed in the Official Airline Guide. North
American Edition. This semimonthly publication contains up-to-date airline
schedules, including times, flight numbers, and aircraft used. Information
on unscheduled charters and light plane traffic may be available through the
local Federal Aviation Agency. Application of emission factors by engine
type and number of landing- takeoff cycles per hour determines the actual
emissions.
Data on actual vehicle miles traveled (VMT) at different speeds, on each
type of roadway per grid square, and on diurnal traffic variations can often
be supplied by the State Department of Highways. Heavy duty vehicle emis-
sions are not considered to be temperature-dependent and are handled accord-
ing to procedures outlined in AP-42.
For light duty vehicles, the equation (from EPA, 1976a, AP-42) for CO
emissions is
1000
[Hl
TOO
1975
(VI-1)
(VMTE LDVE ^ c;n, yjs, m*nl
0=1963
where
Q. DV = total carbon monoxide emissions (kg/hour/grid square)
H = hourly percentage of daily traffic
E = roadway types
VMTr = vehicle miles traveled for each roadway type E
LDVE = percent light duty vehicles for roadway type E
c* , = Federal test procedure average emission factor for model year o,
calendar year n
v* , = speed correction factor for speed s', model year o
m* , = adjusted annual travel for model year o, calendar year n1
p T = temperature correction factor for model year o, temperature T
43
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q T , = percent cold start correction factor for model year o,
temperature T and percent cold starts w1.
There are two sets of temperature and cold-start correction factors for
the above equation. One set applies to model years 1963 through 1974 which
do not use catalytic converters. The other set applies to model years 1975
and later. The equation then becomes
Q =
1
1000
100
(pq)
(p'q')
^
1974
(VMT£ LDVj
0=1963
(VI-2)
vos
o '
where
p,q are the correction factors for the years 1963 through 1974
p',q' are the correction factors for 1975
With this equation, emissions from light duty vehicles can be calculated
for a reference temperature. Adjustments for specific temperatures can be
made during simulations, using data collected at the nearest NWS or military
weather observing station.
Topography
Ground elevation information can be derived from standard U.S. Geological
Survey topographic maps (1:62,500 scale). A map mosaic can be prepared using
a clear plastic grid overlay of some specified grid size oriented over the
area using UTM coordinate designations. Grid square elevations can be visual-
ly estimated taking into account features of the terrain.
Initial and Boundary Conditions
The model program takes measured and specified air quality data at a
number of sites throughout the modeling region and, via an interpolation/
extrapolation procedure, computes both initial and boundary concentration dis-
tributions. The input data requirements for the program are:
a. The grid coordinates for each measurement site.
b. Hourly air quality data for each pollutant of interest at each
station.
Initial conditions are given for 1 hour prior to the hour of simulation while
boundary conditions are input for each hour of simulation.
44
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Atmospheric Thermal Structure
Data on the diurnal character of reference height Zj, i.e., the
atmospheric mixing height, are required at hourly intervals. This informa-
tion is generally determined through analysis of vertical profiles of air
temperature. Such profiles can be obtained from soundings using free or
tethered radiosondes, or from vertical spirals using fixed wing, helicopter,
or drone aircraft as measurement platforms. Data collected using remote
sounding devices such as a LIDAR or monostatic acoustic sounder can often be
used for this purpose. Vertical profiles of air quality, dewpoint
temperature, visual range (with an integrating nephelometer), and wind speed
and direction can often be useful as supplementary information.
Special measurement programs may be required to ascertain the necessity
of allowing for spatial variability in mixing height across the modeling
region. This is especially true for areas which contain large urbanized
sections or severe topographical features, or are under the direct influence
of large bodies of water.
Surface Wind Speed and Direction
Information on the 3-dimensional wind for each of the grid cells in the
model region is required at hourly intervals. The interpolation algorithm
described in Chapter III provides this data from values of horizontal wind
speed and direction reported for the grid locations of each of the surface
wind stations in the field network.
Vertical Eddy Diffusivity Requirements
Profiles of vertical eddy diffusivity for each grid column are computed
by model algorithms (see Chapter III) from values of near surface horizontal
wind speed, roughness parameter, height of reference level (i.e., mixing
height), cloud cover and exposure class. The information on cloud cover may
be obtained from hourly weather observations made at the nearest NWS or mili-
tary station. Exposure class may be determined in daytime from the sun angle
which is in turn obtained by an algorithm using latitude, longitude, time of
day, and Julian date as input parameters.
Values of the surface roughness parameter, z0, for inclusion into the
diffusivity scheme can be estimated using the aerodynamic technique developed
by Lettau (1969). Basically, the technique considers the height, surface
area, and distribution of roughness elements exposed to the wind. This tech-
nique was developed from field measurements of the aerodynamic properties and
wind profiles for various geometric configurations of roughness elements
placed on a frozen lake surface. The explicit formulation is
(VI-3)
45
-------
where h* is the average height of the roughness elements within a grid
element; s* is the silhouette area of the elements exposed to the wind; and
S* is the lot area. The lot area is the quotient of the total surface area
over which the elements are being considered divided by the number of ele-
ments within the area.
Average values of z0 can be computed for each of the horizontal area!
grid elements of the model area within the region of interest. Percentages
of the land-use types within each of the grid elements can be established
using street maps, real estate maps, aerial photographs, and results of
visual ground surveys, etc. A value of z0 for each of the grid elements can
then be computed by appropriate weighting of the percent coverage of land-use
type existing within the grid elements.
Variations in silhouette area and hence of z0 as a function of season of
the year and of wind direction may need to be considered. The former is
especially true for areas with significant deciduous tree cover and/or agri-
cultural activity. The latter is particularly important for areas with rela-
tively heterogeneous land-use tracts and the above-mentioned tree cover.
Scenario Selection
Selection of optimal locations for CO monitoring stations by the Figure
of Merit technique is accomplished using simulations with the mesoscale air
quality simulation model as a data base. The simulations can be run on mete-
orological scenarios developed from historical weather data supplemented by
historical air quality data and current meteorological information collected
during the field sampling program.
Analysis of historical air quality data in relation to the prevailing
meteorological situation or pattern should provide definitive information on
the types of such situations likely to produce high pollutant concentrations.
This is particularly important since the function of the CO monitoring net-
work is for monitoring to detect concentrations exceeding the NAAQS.
Classification ,of meteorological situations into types may be
accomplished through statistical analysis of sea level pressure charts and
upper air constant pressure charts applicable for the area as available from
the NCC of the NWS. Objective classification may be accomplished in the
manner outlined by such investigators as Lund (1963) and Roach and MacDonald
(1975). Basically, this is done for a large sample size, on the order of 5
to 10 years of data, by linear correlation of the heights on a constant pres-
sure surface (or pressures on a constant height surface) at regular grid
intervals. The maps correlating highest with each other are grouped together
and form classes. The percentage frequency of each class establishes its
probability of occurrence. Alternatively, visual examination of such charts
may allow a subjective classification of meteorological situations with the
frequency of occurrence of each establishing its probability of occurrence.
Meteorological information on winds, surface air temperature, mixing
depths, and atmospheric stability for input into the model for each such class
may be obtained from historical surface and upper air data for reporting
46
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stations within the region. Where the appropriate stations are not available
locally, selective use may be made of data for stations normally within the
same air mass. Diurnal mixing depths, for instance, may be estimated from
the 1200Z radiosonde profile and hourly surface air temperature values in the
manner of Holzworth (1972).
Statistical analysis of the resultant meteorological data for the
particular dates corresponding to the various classes can lead to the selec-
tion of the scenarios to be utilized. For example, averages and/or percen-
tiles of frequency distributions of the various parameters may be selected.
Alternatively, typical cases for subsets within the basic classes may be
chosen.
In regions containing significant topographic features, under the direct
influence of large bodies of water or containing large urbanized areas, it
may well be necessary to use supplementary weather data to assist in estab-
lishing meteorological input for the model. Here, large spatial variations
in certain of the parameters may persist across the region. Objective analy-
sis or other statistical models may be used to develop fields of such varia-
bles based upon the historical and supplementary data. In areas where the
meteorology for the relevant scenarios is determined almost exclusively by
local effects due to large bodies of water or complex topography, it may even
be necessary to use meteorological simulation models to develop the relevant
inputs.
47
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CHAPTER VII
SUMMARY
This document has presented a methodology for designing an air
monitoring network which has as its objective the identification of pollutant
concentrations that exceed the NAAQS. Emphasis has been placed on the theo-
retical development of mathematical expressions which describe the behavior
of pollutants that have been emitted in an urban environment. Computer
solution of these equations under specified meteorological conditions form
the data base from which predicted pollutant concentrations can be mapped,
which in turn provide the bases for the selection of monitoring sites for a
network.
A procedure has been described for applying this methodology to a design
for selecting carbon monoxide monitoring sites. Application of the methodol-
ogy requires the establishment of a field measurement program, the develop-
ment of an appropriate historical data base, the assembly of a complete emis-
sions inventory, and the development of other pertinent data required as
input to the solution of the mathematical model.
This document contains only the rationale behind the air monitoring
network design methodology and a procedure for applying this methodology.
The instructions for implementing and executing a computer solution of the
mathematical model is published separately as a User's Guide. A complete
example of the application of the methodology including details of an executed
field program, emissions inventory development, meteorological scenarios
development, simulation results, and recommended network design for the
Las Vegas Valley are presented in a separate publication.
48
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52 *U.S. Government Printing Off ice: 1977-784-677/84 Region No. 9-1
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
. REPORT NO.
EPA-600/4-77-019
2.
3. RECIPIENT'S ACCESSION-NO.
4. TITLE AND SUBTITLE
DEVELOPMENT OF A METHODOLOGY FOR DESIGNING CARBON
MONOXIDE MONITORING NETWORKS
5. REPORT DATE
March 1977
6. PERFORMING ORGANIZATION CODE
7.AUTHOR(S)M> K> Liu> j^ „ ^ R> j i > j
J. H. Seinfeld, J. V. Behar, L. M. Dunn, J. L. McElroy,
P. N. Lem, A.M. Pitchford. N. T. Fisher
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Systems Applications, Incorporated
950 Northgate Drive
San Rafael, California 94903
10. PROGRAM ELEMENT NO.
1HD620
11. CONTRACT/GRANT NO.
68-03-2399
12. SPONSORING AGENCY NAME AND ADDRESS
U.S. Environmental Protection Agency-Las Vegas, NV
Office of Research and Development
Environmental Monitoring and Support Laboratory
Las Vegas, NV 89114
13. TYPE OF REPORT AND PERIOD COVERED
Interim Report
14. SPONSORING AGENCY CODE
EPA/600/07
15. SUPPLEMENTARY NOTES
This report was jointly authored by personnel of the Systems Applications, Incorporated
and the Environmental Monitoring and Support Laboratory, Las Vegas, Nevada.
16. ABSTRACT
A methodology is presented for designing a carbon monoxide monitoring network
based on the objective of identifying concentrations that exceed the national ambient
air quality standards (NAAQS). The basis for identifying concentrations in excess of
the NAAQS is the Concentration Area Time-Product, where the concentrations are inte-
grated over an area (i.e., a grid square in a gridded system) and integrated over a
time interval for averaging the concentrations. These are computed with a mesoscale
air quality simulation model formulated as a 4-dimensional (x,y,z,t), partial differ-
ential equation of mass balance for the pollutant species which yields space-time
average concentration distributions. A frequency-weighted average of concentrations
called Figure of Merit is determined from these projected concentration distributions,
prevailing meteorological patterns, and the frequency of occurrence associated with
each of the meteorological patterns. A mapping of these Figure of Merit values
provides the basis of selection of the locations and number of sites in the network.
The methodology was applied in a design of an ambient air monitoring network for
carbon monoxide. The establishment of a field measurement program is described which
would provide air quality and meteorological data for model validation and simulation
as required in development of the specifications for the number and location of sites
in the network design. Discussions are limited to the design methodology. Actual
field data, simulation exercises, pollution concentration isopleths, and mappings are
in d sapdrdlti
WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.IDENTIFIERS/OPEN ENDED TERMS
c. COS AT I Field/Group
Air Pollution
Mathematical Models
Systems Analysis
Air Monitoring Networks
Air Quality Monitoring
Monitoring Sites
05H
13B
14A
3. DISTRIBUTION STATEMENT
RELEASE TO PUBLIC
19. SECURITY CLASS (ThisReport)
UNCLASSIFIED
21. NO. OF PAGES
64
20. SECURITY CLASS (Thispage)
UNCLASSIFIED
2,2. PRICE
EPA Form 2220-1 (9-73)
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