United States Office of Air Quality EPA-450/4-79-003
Environmental Protection Planning and Standards OAQPS No. 1.2-108
Agency Research Triangle Park NC 27711 January 1979
Air
&ER& Guideline Series
Guideline for the
Interpretation of
Ozone Air Quality
Standards
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EPA-450/4-79-003
OAQPS No. 1.2-108
Guideline for the
Interpretation of Ozone
Air Quality Standards
Monitoring and Data Analysis Division
Prepared for
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air, Noise, and Radiation
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
January 1979
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OAQPS GUIDELINE SEMES
The guideline series of reports is being issued by the Office of Air Quality
Planning and Standards (OAQPS) to provide information to state and local
air pollution control agencies, for example, to provide guidance on the
acquisition and processing of air quality data and on the planning and
analysis requisite for the maintenance of air quality. Reports published in
this series will be available - as supplies permit - from the Library Services
Office (MD-35) , U.S. Environmental Protection Agency, Research Triangle
Park, North Carolina 27711; or, for a nominal fee, from the National
Technical Information Service, 5285 Port Royal Road, Springfield, Virginia
22161.
Publication No. EPA-450/4-79-003
(OAQPS No. 1.2-108
ii
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Table of Contents
1. INTRODUCTION 1
1.1. Background 2
1.2. Terminology 3
1.3. Basic Premises 4
2. ASSESSING COMPLIANCE 7
2.1. Interpretation of "Expected Nunber" 7
2.2. Estimating Exceedances for a Year 8
2.3. Extension to Multiple Years 12
2.4. Example Calculation 14
3. ESTIMATING DESIGN VALUES 17
3.1. Discussion of Design Values 17
3.2. The Use of Statistical Distributions 18
3-3. Methodologies 20
3.4. Quick Test for Design Values 28
3.5. Discussion of Data Requirements 29
3.6. Example Design Value Computations 31
4. APPLICATIONS WITH LIMITED AMBIENT DATA 35
5. REFERENCES 37
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1. INTRODUCTION
The ozone National Ambient Air Quality Standards
(NAAQS) contain the phrase "expected number of days per
calendar year." [1] This differs from the previous
NAAQS for photochemical oxidants which simply state a
particular concentration "not to be exceeded more than
once per year." [2] The data analysis procedures to be
used in computing the expected number are specified in
Appendix H to the ozone standard. The purpose of this
document is to amplify the discussions contained in
Appendix H dealing with compliance assessment and to
indicate the data analysis procedures necessary to de-
termine appropriate design values for use in developing
control strategies. Where possible, the approaches
discussed here are conceptually similar to the proce-
dures presented in the earlier "Guideline for
Interpreting Air Quality Data With Respect to the
Standards" (OAQPS 1.2-008, revised February, 1977). [31
However, the form of the ozone standards necessitates
certain modifications in two general areas: (1) ac-
counting for less than complete sampling and (2) incor-
porating data from more than one year.
Although the interpretation of the proposed stan-
dards may initially appear complicated, the basic prin-
ciple is relatively straightforward. In general, the
average number of days per year above the level of the
standard must be less than or equal to 1. In its
simplest form, the number of exceedances each year
would be recorded and then averaged over the past three
years to determine if this average is less than or
equal to 1. Most of the complications that arise are
consequences of accounting for incomplete sampling or
changes in emissions.
Throughout the following discussion certain points
are assumed that are consistent with previous guidance
[3] but should be reiterated here for completeness.
The terms hour and day (daily) are interpreted respec-
tively as clock hour and calendar day. Air quality
data are examined on a site by site basis and each in-
dividual site must meet the standard. In general, data
from several different sites are not combined or aver-
aged when performing these analyses. These points are
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discussed in more detail elsewhere. [3]
This document is organized so that the remainder
of this introductory section presents the background of
the problem, terminology, and certain basic premises
that were used in developing this guidance. This is
followed by a section which examines methods for deter-
mining appropriate design values. The final section
discusses approaches that might be employed in cases
without ambient monitoring data. This last section is
brief and fairly general, because it treats an aspect
of the problem which would be expected to rapidly
evolve once these new forms of the MAAQS become
established. In several parts of this document the ma-
terial is developed in a conversational format in order
to highlight certain points.
1.1. Background
The previous National Ambient Air Quality Standard
(NAAQS) for oxidant stated that no more than one hourly
value per year should exceed 160 micrograms per cubic
meter (.OSpprn). [2] With this type of standard, the se-
cond highest value for the year becomes the decision-
making value. If it is above 160 micrograms per cubic
meter then the standard was exceeded. This would ini-
tially appear to be an ideal type of standard. The
wording is simple and the interpretation is obvious-or
is it? Suppose the second highest value for the year
is less than 160 micrograms per cubic meter and the
question asked is, "Does this site meet the standard?"
An experienced air pollution analyst would almost
automatically first ask, "How many observations were
there?" This response reflects the obvious fact that
the second highest measured value can depend upon how
many measurements were made in the year. Carried to
the absurd, if only one measurement is made for the
year, it is impossible to exceed this type of standard.
Obviously, this extreme case could be remedied by re-
quiring some minimum number of measurements per year.
However, the basic point is that the probability of de-
tecting a violation would still be expected to increase
as the number of samples increased from the specified
minimum to the maximum possible number of observations
per year. Therefore, the present wording of this type
of standard inherently penalizes an area that performs
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more than the minimum acceptable amount of monitoring.
Furthermore, the specification of a minimum data com-
pleteness criterion still does not solve the problem of
what to do with those data sets that fail to meet this
criterion.
A second problem with the current wording of the
standard is not as obvious but becomes more apparent
when considering what is involved in maintaining the
standard year after year. For example, suppose an area
meets the standard in the sense that only one value for
the year is above 160 rnicrograms per cubic meter.
Because of the variability associated with air quality
data, the fact that one value is above the standard
level means that there is a chance that two values
could be above this standard level the next year even
though there is no change in emissions. In other
words, any area with emissions and meteorology that can
produce one oxidant value above the standard has a de-
finite risk of sometime having at least two such values
occurring in the same year and thereby violating the
standard. This situation may be viewed as analogous to
the "10 year flood" and "100 year flood" concepts used
in hydrology; i.e., high values may occur in the future
but the likelihood of such events is relatively low.
However, with respect to air pollution any rare viola-
tion poses distinct practical problems. From a control
agency viewpoint, the question arises as to what should
be done about such a violation if it is highly unlikely
to reoccur in the next few years. If the decision is
made to ignore such a violation then the obvious impli-
cation is that the standard can occasionally be
ignored. This is not only undesirable but produces a
state of ambiguity that must be resolved to intelli-
gently assess the risk of violating the standard. In
other words, some quantification is needed to describe
what it means to maintain the standard year after year
in view of the variation associated with air quality
data. The wording of the ozone standard is intended to
alleviate these problems.
1.2. Terminology
The term 'daily maximum value' refers to the maxi-
mun hourly ozone value for a day. As defined in
Appendix H, a valid daily maximum means that at least
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75% of the hourly values from 9:01 A.M. to 9:00 P.M.
(LSI) were measured or at least one hourly value ex-
ceeded the level of the standard. This criterion is
intended to reflect adequate monitoring of the daylight
hou^s while allowing time for routine instrument
maintenance. The criterion also ensures that high
hourly values are not omitted merely because too few
values were measured. It should be noted that this is
intended as a minimal criterion for completeness and
not as a recommended monitoring schedule.
A final point worth noting concerns terminology.
The term "exceedance" is used throughout this document
to describe a daily maximum ozone measurement that is
above the level of the standard. Therefore the phrase
"expected number of exceedances" is equivalent to "the
expected number of daily maximum ozone values above the
level of the standard."
1.3- Basic Premises
By its very nature, the existence of a guideline
document implies several things: (1) that there is a
problem, (2) that a solution is provided, and (3) that
there were several alternative's considered in reaching
the solution. Obviously, if there is no problem then
the guideline is of limited value, and if there were
not some alternative solutions then the guidance is
perhaps superfluous or at best educational. The third
point indicates that the "best" alternative, in some
sense, was selected. With this in mind, it is useful
to briefly discuss some of the key points that were
considered in judging the various options. The purpose
of this section is to briefly indicate the criteria
used in developing this particular guideline.
The most obvious criterion is simplicity. This
simplicity extends to several aspects of the problem.
When someone asks if a particular area meets the stan-
dard they expect either a "yes" or "no" as the answer
or even an occassional "I don't know". Secondly, this
simplicity should extend to the reason why the standard
was met or violated. If a panel of experts is required
to debate the probability that an area is in compliance
then the general public may rightly feel confused about
just what is being done to protect their health. Also,
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the more clear-cut the status of an area is (and the
reasons why) the more likely it is that all groups in-
volved can concentrate on the real problem of maintain-
ing clean air rather than arguing over minor side
issues.
Whi?.e simplicity is desirable, if the problem is
complex the solution cannot be oversimplified. In oth-
er words, the goal is to develop a solution that is
simple, and yet not simple-minded. In order to do
this, the approach taken in this document is to recog-
nize that there are two questions involved in determin-
ing compliance: (1) was the standard violated? and (2)
if so, by how much? The first question is the simpler
of the two in that a "yes/no" answer is expected. The
second question implies both a quantification and a de-
termination of what to do about it. Therefore, it
seems reasonable to have a more complicated procedure
for determining the second answer.
In addition to the trade-offs between simplicity
and complexity another problem is to allow a certain
amount of flexibility without being vague. There are
several reasons for allowing some degree of
flexibility. Not only do available resources vary from
one area to another but the complexity of the air pol-
lution problems vary. An area with no pollution prob-
lem should not be required to do an extensive analysis
just because that level of detail is needed someplace
else. Conversely, an area with sufficient resources to
perform a detailed analysis of their pollution problem
to develop an optimum control strategy should not be
constrained from doing so simply because it is not war-
ranted elsewhere. Furthermore, a certain degree of
flexibility is essential to allow for modified monitor-
ing schedules that are used to make the best use of
available resources.
In addition to these points concerning simplicity
and flexibility, certain other considerations are of
course involved. In particular, the methodology em-
ployed cannot merely ignore high values for a particu-
lar year simply because they are unlikely to reoccur.
The purpose of the standard is to protect against high
values in a manner consistent with the likelihood of
their occurrence.
A final point is that the proposed interpretation
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should involve a framework that could eventually be ex-
tended to other pollutants, if necessary, and easily
modified in the future as our knowledge and understand-
ing of air pollution increases.
It should be noted that no specific mention is
made of measurement error in the following discussions.
While it would be naive to assume that measurement er-
rors do not occur, at the present time it is difficult
to allow for measurement errors in a manner that is not
tantamount to re-defining the level of the standard.
Obviously there is no question that data values known
to be grossly in error should be corrected or
eliminated. In fact the use of multiple years of data
for the ozone standards should facilitate this process.
The more serious practical problem is with the level of
uncertainty associated with every individual
measurement. The viewpoint taken here is that these
inherent accuracy limitations are accounted for in the
choice of the level of the standard and that equitable
risk from one area to another is assured by use of the
reference (or an equivalent) ambient monitoring method
and adherence to a required minimum quality assurance
program. It should be noted that the stated level of
the standard is taken as defining the number of signi-
ficant figures to be used in comparisons with the
standard. For example, a standard level of .12 ppm
means that measurements are to be rounded to two deci-
mal places (.005 rounds up), and, therefore, .125 ppm is
the smallest concentration value in excess of the level
of the standard.
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2. ASSESSING COMPLIANCE
This section examines the ozone standard with par-
ticular attention given to the evaluation of
compliance. This is done in several steps. The first
is a discussion of the term "expected number." Once
this is defined it is possible to consider the in-
terpretation when applied to several years of data or
to less than complete sampling data. An example calcu-
lation is included at the end of this section to sum-
marize and illustrate the major points.
2.1. Interpretation of "Expected Number"
The wording of the ozone standard states that the
"expected number of days per calendar year" must be
"equal to or less than 1." The statistical term
"expected number" is basically an arithmetic average.
Perhaps the simplest way to explain the intent of this
wording is to give an example of what it would mean for
an area to be in compliance with this type of standard.
Suppose an area has relatively constant emissions year
after year and its monitoring station records an ozone
value for every day of the year. At the end of each
year the number of daily values above the level of the
standard is determined and this is averaged with the
results of previous years. As long as this arithmetic
average remains "less than or equal to 1" the area is
in compliance. As far as rounding conventions are
concerned, it suffices to carry one decimal place when
computing the average. For example, the average of the
three numbers 1,1,2 is 1.3 which is greater than 1.
Two features in this example warrant additional
discussion to clearly define how this proposal would be
implemented. The example assumes that a daily ozone
measurement is available for each day of the year so
that the number of exceedances for the year is known.
On a practical basis this is highly unlikely and,
therefore, it will be necessary to estimate this
quantity. This is discussed in section 2.2. In the
example it is also assumed that several years of data
are available and there is relatively little change in
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emissions. This is discussed in more detail in section
2.3-
The key point in the example is that as data from
additional years are incorporated into the average this
expected nupiber of exceedances per year should
stabilize. If unusual meteorology contributes to a
high number of exceedances for a particular year then
this will be averaged out by the values for other
"normal" years. It should be noted that these high
values would, therefore, not be ignored but rather
their relative contribution to the overall average is
in proportion to the likelihood of their occurrence.
This use of the average may be contrasted with an ap-
proach based upon the median. If the median were used
then the year with the greatest number of exceedances
could be ignored and there would be no guarantee of
protection against their periodic reoccurrence.
2.2. Estimating Exceedances for a Year
As discussed above, it is highly unlikely that an
ozone measurement will be available for each day of the
year. Therefore, it will be necessary to estimate the
number of exceedances in a year. The formula to be
used for this estimation is contained in Appendix H of
the ozone standard. The purpose of this section is to
present the same basic formula but to expand upon the
rationale for choosing this approach and to provide il-
lustrations of certain points.
Throughout this discussion the term "missing
value" is used in the general sense to describe all
days that do not have an associated ozone measurement.
It is recognized that in certain cases a so-called
"missing value" occurs because the sampling schedule
did not require a measurement for that particular day.
Such missing values, which can be viewed as "scheduled
missing values," may be the result of planned instru-
ment -naintenance or, for ozone, may be a consequence of
a seasonal monitoring program. In order to estimate
the number of exceedances in a particular year it is
necessary to account for the possible effect of missing
values. Obviously, allowance for missing values can
only result in an estimated number of exceedances at
least as large as the observed number. From a practi-
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cal viewpoint, this means that any site that is in vio-
lation of the standard based upon the observed number
of exceedances will not change status after this
adjustment. Thus, in a sense, this adjustment for
missing values is required to demonstrate attainment,
but may not be necessary to establish non-attainment.
In estimating the number of exceedances in cases
with missing data, certain practical considerations are
appropriate. In some areas, cold weather during the
winter makes it very unlikely that high ozone values
would occur. Therefore it is possible to discontinue
ozone monitoring in some localities for limited time
periods with little risk of incorrectly assessing the
status of the area. As indicated in Appendix H, the
proposed monitoring regulations(CFR58) would permit the
appropriate Regional Administrator to waive any ozone
monitoring requirements during certain times of the
year. Although data for such a time period would be
technically missing, the estimation formula is struc-
tured in terms of the required number of monitoring
days and therefore these missing days would not affect
the computations.
Another point is that even though a daily ozone
value is missing, other data might indicate whether or
not the missing value would have been likely to exceed
the standard level. There are numerous ways additional
information such as solar radiation, temperature, or
other pollutants could be used but the final result
should be relatively easy to implement and not create
an additional burden. An analysis of 258 site-years of
ozone/oxidant data from the highest sites in the 90
largest Air Quality Control Regions showed that only 1%
of the time did the high value for a day exceed .12 pprn
if the adjacent daily values were less than .09 pprn.
With this in rnind the following exclusion criterion may
be used for ozone:
A missing daily ozone value may be assumed to
be less than the level of the standard if the daily
maxima on both the preceding day and the following day
do not exceed 75% of the level of the standard.
It should be noted that to invoke this exclusion
criterion data must be available fron both adjacent
days. Thus it does not apply to consecutive missing
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10
daily values. Having defined the set of missing values
that may be assumed to be less than the standard it is
possible to present the computations required to adjust
for missing data.
Let z denote the number of missing values that may
be assumed to be less than the standard. Then the fol-
lowing formula shall be used to estimate the number of
exceedances for the year:
e=v+(v/n)*(N-n-z) (1)
(* indicates multiplication)
Where N = the number of required monitoring
days in the year
n = the number of valid daily maxima
v = the number of measured daily values above
the level of the standard
z = the number of days assumed to be less
than the standard level, and
e = the estimated number of exceedances for
the year.
This estimated number of exceedances shall be
rounded to one decimal place (fractional parts equal to
. 05 round up).
Note that N is always equal to the number of days
in the year unless a monitoring waiver has been granted
by the appropriate Regional Administrator.
The above equation may
in the following manner.
ceedances is equal to the
increment that accounts for
were (N-n) missing daily
certain number of these, namely
below the standard. Therefore,
be interpreted intuitively
The estimated number of ex-
observed number plus an
incomplete sampling. There
values for the year, but a
z, were assumed to be
(N-n-z) missing values
are considered to be potential exceedances
tion of measured values that were above
The frac-
the level of
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11
the standard was v/n and it is assumed that the sarae
fraction of these candidate missing values would also
exceed t^e level of the standard.
The estimation procedures presented are computa-
tionally simple. Some data processing complications
result when missing data are screened to ensure a
representative data base, but on a practical basis this
effort is only required for sites that are marginal
with respect to compliance. Because the exclusion
criterion for missing values does not differentiate be-
tween scheduled and non-scheduled missing values it is
possible to develop a computerized system to perform
the necessary calculations without requiring additional
information on why each particular value was missing.
In principle, if allowance is made for missing values
that are relatively certain to be less than the stan-
dard then it would seem reasonable to also account for
missing values that are relatively certain to be above
the standard. Although this is a possibility, it will
probably not be necessary initially because such a si-
tuation would, of necessity, have at least two values
greater than the standard level. Therefore, it is
quite likely that this would be an unnecessary compli-
cation in that it would not affect the assessment of
compliance.
One feature of these estimation procedures should
be noted. If an area does not record any values above
the standard, then the estimated number of exceedances
for the year is zero. An obvious consequence of this
is that any area that does not record a value above the
standard level will be in compliance. In most cases
this confidence is warranted. However, at least some
qualification is necessary to indicate that it is poss-
ible that the existing monitoring data can be deemed
inadequate for use with these estimation formulas. In
general, data sets that are T5% complete for the peak
pollution potential seasons will be deemed adequate.
Although the general 75% completeness rule has been
traditionally used as an air quality validity criterion
the key point is to ensure reasonably complete monitor-
ing of those time periods with high pollution
potential. An additional word of caution is probably
required at this point concerning attainment status tie-
terminations based upon limited data. If a particular
area has very limited data and shows no exeeedances of
the standard it must be recognized that a more intense
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12
monitoring program could possibly result in a determ-
ination of non-attainment. Therefore, if it is criti-
cal to immediately determine the status of a particular
area and the ambient data base is not very complete,
the design value computations presented in section 3
may be employed as a guide to assess potential
problems. The point is, that as the monitoring data
base increases, the additional data may indicate non-
attainment. Therefore some caution should be used when
viewing attainment status designations based upon in-
complete data .
2.3. Extension to Multiple Years
As discussed earlier, the major change in the
ozone standard is the use of the term "expected number"
rather than just "the number." The rationale for this
modification is to allow events to be weighted by the
probability of their occurrence. Up to this point,
only the estimation of the number of exceedances for a
single year has been discussed. This section discusses
the extension to multiple years.
Ideally, the expected number of exceedances for a
site would be compared by knowing the probability that
the site would record 0,1,2,3,... exceedances in a
year. Then each possible outcome could be weighted ac-
cording to its likelihood of occurrence, and the appro-
priate expected value or average could be computed. In
practice, this type of situation will not exist because
ambient data will only be available for a limited
number of years.
A period of three successive years is recommended
as the basis for determining attainment for two
reasons. First, increasing the number of years in-
creases the stabiity of the resulting average number of
exceedances. Stated differently, as more years are
used, there is a greater chance of minimizing the ef-
fects of an extreme year caused by unusual weather
conditions. The second factor is that extending the
number of successive years too far increases the risk
of averaging data during a period in which a real shift
in emissions and air quality has occurred. This would
penalize areas showing recent improvement and similarly
reward areas which are experiencing deteriorating ozone
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13
air quality. Three years is thought by EPA to repre-
sent a proper balance between these two considerations.
This specification of a three year time period for com-
pliance assessment also provides a firm basis for pur-
poses of decision-making. While additional flexibility
is possible for developing design values for control
strategy purposes, a more definitive framework seerns
essential when judging compliance to eliminate possible
ambiguity and to clearly identify the basis for the
decision.
Consequently, the expected number of exceedances
per year at a site should be computed by averaging the
estimated number of exceedances for each year of data
during the past three calendar years. In other words,
if the estimated number of exceedances has been com-
puted for 1974, 1975, and 1975, then the expected
number of exceedances is estimated by averaging those
three numbers. If this estimate is greater than 1,
then the standard has been exceeded at this site. As
previously mentioned, it suffices to carry one decimal
place when computing this average. This averaging rule
requires the use of all ozone data collected at that
site during the past three calendar years. If no data
are available for a particular year then the average is
computed on the basis of the remaining years. If in
the previous example no data were available for 1974,
then the average of the estimated number of exceedances
for 1975 and 1976 would be used. In other words, the
general rule is to use data from the most recent three
years if available, but a single season of monitoring
data may still suffice to establish non-attain-nent.
Thus, this three year criterion does not mean that
non-attainment decisions must be delayed until three
years of data are available. It should be noted that
to establish attainment by a particular date, allowance
will be permitted for emission reductions that are
known to have occurred .
One point worth commenting on is the possibility
that the very first year is "unusual." While this could
occur, in the case of ozone most urbanized areas
already have existing data bases so that some measure
of the normal number of exceedances per year is
available. Furthermore the nature of the ozone problem
makes it unlikely that areas currently well above the
standard would suddenly come into compliance.
Therefore, as these areas approach the standard addi-
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tional years of data would be available to determine
the expected number of exceedances for a year.
2.4. Example Calculation
In order to illustrate the key points that have
been discussed in this section it is convenient to con-
sider the following example for ozone.
Suppose a site has the following data history
for 1973-1980:
1973: 365 daily values; 3 days above the
standard level.
1979: 285 daily values; 2 days above the
standard level; 21 missing days satisfying
the exclusion criterion.
1980: 287 daily values; 1 day above the stan-
dard level; 7 missing days satisfying the ex-
clusion criterion.
Suppose further that in 1980 measurements were not
taken during the months of January and February (a to-
tal of 60 days for a leap year) because the cold
weather minimizes any chance of recording exceedances
and a monitoring waiver had been granted by the appro-
priate Regional Administrator.
Because the three year average number of ex-
ceedances is clearly greater than 1, there is no compu-
tation required to determine that this site is not in
compliance. However, the expected number of ex-
ceedances may still be computed using equation 1 for
purposes of illustration.
For 1973, there were no missing daily values and
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therefore there is no need to use the estimated ex-
ceedances formula. The number of exceedances for 1973
is 3.
For 1979, equation 1 applies and the estimated
number of exceedances is:
2 +(2/2S5)*(365 - 285 - 21) =
2 + 0.4 = 2.4
For 1980, the sane
due to the monitoring
the number of required
therefore the estimated
estimation formula is used but
waiver for January and February
monitoring days is 306 and
number of exceedances is:
1 + (1/287)*(306 - 287 -7) =
1 + (1/287)*(12) = 1.0
Averaging
gives 2.1 as
ceedances per
calculations.
these three numbers (3, 2.4, and 1.0)
the estimated expected number of ex-
year and completes the required
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3. ESTIMATING DESIGN VALUES
The previous section addressed compliance with the
standard. As discussed, it suffices to treat questions
concerning compliance as requiring a "yes/no" type
answer. This approach facilitates the use of relative-
ly simple computational formulas. It also makes it
unnecessary to define the type of statistical distribu-
tion that describes the behavior of air quality data.
The advantage of not invoking a particular statistical
distribution is that the key issue of whether or not
the standard is exceeded is not obscured by which par-
ticular distribution best describes the data. However,
once it is established that an area exceeds the
standard, the next logical question is more quantita-
tive and requires an estimate of by how much the stan-
dard was exceeded. This is done by first examining the
definition of a design value for an "expected
exceedances" standard and then discussing various
procedures that may be used to estimate a design value.
A variety of approaches are considered such as fitting
a statistical distribution, the use of conditional
probabilities, graphical estimation, and even a table
look-up procedure. In a sense each of these approaches
should be viewed as a means to an end, i.e., meeting
the applicable air quality standard. As long as this
final goal is kept in mind any of these approaches are
satisfactory. As with the previous section discussing
compliance, this section concludes with example calcu-
lations illustrating the more important points.
3.1. Discussion of Design Values
In order to determine the amount by which the
standard is exceeded it is necessary to discuss the in-
terpretation of a design value for the proposed
standard. Conceptually the design value for a particu-
lar site is the value that should be reduced to the
standard level thereby ensuring that the site will meet
the standard. With the wording of the ozone standard
the appropriate design value is the concentration with
expected number of exceedances equal to 1. Although
this describes the design value in words it is useful
-------
18
to introduce
quantity.
certain notations to precisely define this
Let P(x < c) denote the probability that an obser-
vation x is less than or equal to concentration c.
This is also denoted as F(c).
Let e denote the number of exceedances of the
standard level in the year, e.g., in the case of ozone
this would be the number of daily values above .12 ppm.
Then the expected value of e denoted as E(e) may be
written as:
E(e) = P(x > .12) * 365
F( .12)] * 365
For a site to be in compliance the expected number
of exceedances per year E(e), must be less than or
equal to 1. From the above equation it follows that
this is equivalent to saying that the probability of an
exceedance must be less than or equal to 1/365.
As indicated, the appropriate design value is that
concentration which is expected to be exceeded once per
year. Alternatively, the design value is chosen so
that the probability of exceeding this concentration is
1/365. If an equation is known for F(c) then the de-
sign value may be obtained by setting 1-F(c) equal to
1/365 and solving for c. If a graph of F(c) is known
then the design value may be determined graphically by
choosing the concentration value that corresponds to a
frequency of exceedance of 1/365. Obviously in prac-
tice the distribution F(c) is not really known. What
is known is a set of air quality measurements that may
be approximated by a statistical distribution to deter-
mine a design value as discussed in the following
section.
3.2. The Use of Statistical Distributions
The use of a statistical distribution to approxi-
mately describe the behavior of air quality data is
certainly not new. The initial work by Larsen [4] with
the log-normal distribution demonstrated how this type
-------
19
of statistical approximation could be used. The pro-
posed form of the ozone standard provides a framework
for the use of statistical distributions to assess the
probability that the standard will be met. An impor-
tant point in dealing with air pollution problems is
that the main area of interest is the high values. The
National Ambient Air Quality Standards are intended to
limit exposure to high concentrations. This has a
direct impact on how statistical distributions are cho-
sen to describe the data. [5] If the intended applica-
tion is to approximate the data in the upper concentra-
tion ranges then obviously it must be required that any
statistical distribution selected for this purpose has
to fit the data in these higher concentration ranges.
Initially this would appear to be an obvious truism
but, in many cases, a particular distribution may
"reasonably approximate" the data in the sense that it
fits fairly well for the middle 80% of the values.
This may be satisfactory for some applications but if
the top 10% of the data is the range of interest it may
be inappropriate.
Over the years various statistical distributions
ha've been suggested for possible use in describing air
quality data. Example applications include the two-
parameter lognormal[ij ] , the three-parameter
lognorrnal [6 ] , the Weibull[5 ,7 ], and the exponential
distributiont5,8]. Despite certain theoretical reser-
vations concerning factors such as interdependence of
successive values these approaches have been proven
over time to be useful tools in air quality data
analysis. The appropriate choice of a distribution is
useful in determining the design value. Viewed in
perspective, however, the selection of the appropriate
statistical distribution is a secondary objective
the primary objective is to determine the appropriate
design value. In other words, the question of interest
is "what concentration has an expected number of ex-
ceedances per year equal to 1?" and not "which distri-
bution perfectly describes the data?" Therefore, it is
not necessary to require that any particular distribu-
tion be used. All that is necessary is to indicate the
characteristics that must be considered in determining
what is meant by a "reasonable fit". In fact it will
be seen later that a design value may be selected with-
out even knowing which particular distribution best de-
scribes the data.
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20
There are certain points that are implicit in the
above discussion which are worth commenting upon. One
possible approach in developing this type of guidance
is to specify a particular distribution to be used in
determining a design value. This approach is not taken
here for a variety of reasons. There is no guarantee
that one family of distributions would be adequate to
describe ozone levels for all areas of the country, for
all weather conditions, etc. It may well be that
different distributions are needed for different areas.
Secondly, as control programs take effect and pollution
levels are reduced the so-called "best" distribution
may change. Another point that should be emphasized
involves the distinction between determining compliance
and determining a design value. Suppose, for example,
that a statistical distribution is selected and ade-
quately describes all but the highest five values each
year. However, these five values are always above the
standard and consequently the number of exceedances per
year is always five. Such a site is not in compliance
even if the design value predicted from the approximat-
ing distribution is below the standard level. In such
a case the expected number of exceedances per year is 5
(with complete sampling) and therefore the site is in
violation. The design value is an aid in determining
the general reduction required, but it some cases it
may be necessary to further refine the estimate because
of inadequate fit for the high values.
3.3- Methodologies
The purpose of this section is to present some ac-
ceptable approaches to determine an appropriate design
value, i.e., the concentration with expected number of
exceedances per year equal to 1. As discussed, this
may be alternatively viewed as determining the concen-
tration that will be exceeded 1 time out of 365.
Throughout this discussion it is important to re-
cognize that the number of measurements must be treated
properly. In particular, missing values that are known
to be less than the standard level should be accounted
for so that they do not incorrectly affect the empiri-
cal frequency distribution. For example, if an area
does not monitor ozone in December, January, and
February, because no values even approaching the stan-
-------
21
dard level have ever been reported in these months then
these observations should not be considered missing but
should be assigned some value less than the standard.
The exact choice of the value is arbitary and is not
really important because the primary purpose is to fit
the upper tail of the distribution.
In discussing the various acceptable approaches
several different cases are presented. This is in-
tended to illustrate the general principles that should
be applied in determining the design value. Throughout
these discussions it is generally assumed that more
than one year of data is available. The difficulty
with using a single year of data is that any effect due
to year to year variations in meteorology is obviously
not accounted for. Therefore, any results based upon
only one year of data should be viewed as a guide that
may be subject to revision.
(1) Fitting One Statistical Distribution to
Several Years of Data
One of the simplest cases is when several years of
fairly complete data are available during a time of re-
latively constant emissions. In this situation the
data can be plotted to determine an empirical frequency
distribution. For example, all data for a site from a
3-5 year period could be ranked from smallest to larg-
est and the empirical frequency distribution plotted on
semi-log paper. This type of plot emphasizes the be-
havior of the upper tail of the data as shown in Figure
1. A discussion of this plotting is contained
elsewhere. [5] Figure 2 illustrates how different
types of distributions would appear on such a plot.
The data may also be plotted on other types of graph
paper, such as log-normal or Weibull. The ideal situa-
tion is when the data points lie approximately on a
straight line. The next step is to choose a statisti-
cal distribution that approximately describes the data
and to fit the distribution to the data. This may be
done by least squares, maximum likelihood estimation,
or any method that gives a reasonable fit to the top
10% of the data. An obvious question is "what consti-
tutes a reasonable fit?" This can be judged visually by
plottins' the fitted distribution on the sane graph as
the data points. Because of the intended use of the
distribution the dep.ree of approximation for the top
-------
22
10-2
V
10-3
100 200 300
S02 CONCENTRATION, ppb
400
Figure 1. Sulfur dioxide measurements for 1968 (24-hour) at CAMP station
in Philadelphia, Pa., plotted on semi-log paper.
10-1
10-2
10-3
x\
T I I I I
10-4
\
II _ I
\
I I I
I I
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70
CONCENTRATION, pphm
Figure 2. Examples of lognormal, exponential, and Weibull distributions
plotted on semi-log paper. A Weibull distribution may also curve upward
for certain parameter values.
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23
10/5, 5*, 1? and even .5$ of the data must be examined.
The mos*" obvious check is to examine departures of the
actual data points from the fitted distribution. As a
general rule there should be no obvious pattern to the
lack of fit in terms of under- or over-prediction ,or
trend. For example, if the fitted distribution un-
derestimates all of the last eight data points by more
than 5?, then it must be established that the fitted
distribution is reasonable. Such an argument might in-
volve showing that the majority of these data points
all occurred in the same period and that the meteorolo-
gy for these particular days was extremely unusual.
The claim that this meteorology was unusual would also
have to be substantiated by examining historical me-
teorological data. It should be noted that this extra
effort is not routinely required and would only be ne-
cessary when the fit appears inadequate. The design
value corresponds to a frequency of 1/365 and in some
cases the empirical frequency distribution function
will be plotted in this range. In such cases, the fit-
ted distribution should be consistent with the empiri-
cal distribution in this range. This can be examined
graphically by locating the concentration on the em-
pirical frequency distribution function corresponding
to a frequency of 1/365. By construction, there will
be measured data points on either side of this value.
The two measured concentrations below this value and
the two measured concentrations above this value will
be used as a constraint in fitting a distribution. If
the fitted distribution results in a design value that
differs by more than 5% from all four of these measured
concentrations, some explanation should be presented
indicating the reasons for this discrepancy. It should
be noted that in some cases there may be only one,
rather than two, measured values on the empirical fre-
quency distribution with frequencies less than 1/365.
In these cases the upper constraint would consist of
one rather than two data points.
(2) Using the Empirical Frequency Distribution of
Several Years of Data (Graphical Estimation)
It should be noted that if several years of fairly
complete data are available it is not necessary to even
fit a statistical distribution. The concentration val-
ue corresponding to a frequency of 1/365 may be read
directly off the graph of the empirical distribution
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Table 1
TABULAR ESTIMATION OF DESIGN VALUE
Number of Daily
Values
365
730
1095
1460
1825
to
to
to
to
to
729
1094
1459
1824
2189
Rank of Upper
Bound
1
2
3
4
5
Rank of Lower Data Point Used
Bound for
2
3
4
5
6
Design Value
highest value
second highest
third highest
fourth highest
fifth highest
-------
25
function and used as the design value.
If the data records are not sufficiently complete
then the empirical distribution function will not be
plotted for the 1/365 frequency and it will be neces-
sary to fit a distribution to estimate the design
value. However, whenever sufficient data are
available, this technique provides a convenient means
of graphically estimating the design value.
(3) Table Look-up
An obvious point that can initially be overlooked
in the discussion of these techniques is that the final
choice of a design value is primarily influenced by the
few highest values in the data set. With this in mind,
it is possible to construct a simple table look-up
procedure to determine a design value. Again, it is
important to treat the number of values properly to en-
sure that the data adequately reflects all portions of
the year.
To use this tabular approach it is only necessary
to know the total number of daily values, and then de-
termine a few of the highest data values,. For
example, if there are 1,017 daily values then the ranks
of the lower and upper bounds obtained from Table 1 are
3 and 2. This means that an appropriate design value
would be between the third- highest and second-highest
observed values. In using this table the higher of the
two concentrations may be used as the design value.
Therefore in this particular case, it suffices to know
the three highest measured values during the time
period .
This look-up procedure is basically a tabular
technique for determining what point on the empirical
frequency distribution corresponds to a frequency of
1/365. By construction, the table look-up procedure
overestimates the design value. For instance, in the
example with 1,017 values an acceptable design value
would lie closer to the lower bound. This could be
handled by interpolation between the second and third
highest values. However, rather than introduce interpo-
lation formulas it would be simpler to merely use the
previously discussed graphical procedure.
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26
For the cases that are 15% complete but still have
less than 365 days the maximum observed concentration
may be used as a tentative design value as long as the
data set was 75% complete during the peak times of the
year. In this case it must be recognized that the de-
sign value is quite likely to require future revision.
In principle, if statistical independence applied, this
maximum observed concentration would equal or exceed
the 1/365 concentration about half the time. However,
the failure to adequately account for yearly variations
in meteorology makes any estimate based on a single
year of data very tentative.
(4) Fitting a Separate Distribution for Each Year
of Data (Conditional Probability Approach)
The previous method required grouping data from
several years into a single frequency distribution. In
some cases data processing constraints may make this
cumbersome. Therefore, an alternate approach may be
used that allows each year to be treated individually.
In considering this alternate approach it is useful to
briefly indicate the underlying framework. This parti-
cular approach uses conditional probabilities and in
most cases it would probably be more convenient to use
one of the previous methods. However, the underlying
framework of this method has sufficient flexibility to
warrant its inclusion.
Suppose that the air quality data at a particular
site may be approximated by some statistical distribu-
tion F(x|0), where 9 denotes the fitted parameters.
Suppose further that the values of the fitted parame-
ters differ from year to year, but that the data may
still be approximated by the same type of distribution.
Intuitively this would mean that while the same type of
distribution describes each year of data, the values of
the parameters would change from year to year re-
flecting the prevailing meteorology for the year. In
theory it could be possible to define a set of meteoro-
logical classes, say rn(i), so that the distribution
function of the air quality data could be defined for
each one of these meteorological classes. Then for
each meteorological class, m(i), there would be an as-
sociated air quality distribution function denoted as
F(x|m(i)), the distribution function for x given the
meteorological class m(i). Using the standard rules of
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27
conditional probability the distribution function F(x)
may be written as:
F(x) = z {FCxlra(i))} P[in(i)]
-i
where P[m(i)] is the probability of meteorological
class m(i) occurring.
Continuing this approach the expected number of
exceedances may be written as:
E(e) = Z P[x > s |m(i)] * P[m(i)]
•i
where s denotes the standard level.
Initially the above framework may seem to be too
theoretical to have much practical use. However, it
will be seen in Section 4 that this approach may afford
a convenient means of determining the expected number
of exceedances per year when limited historical data is
available. For the present discussion it suffices to
indicate how this approach may be used when ambient
data sets are available.
Suppose that five years of ambient measurements
are available. An approximating statistical distribu-
tion may be determined as discussed previously for each
year, denoted as Fi(x). This would be analogous to the
F(x,m(i)) in the above discussion. Then the distribu-
tion function of F(x) may be written as:
F(x)= .Z Fi(x) * 1/5
where Fi is analogous to F[xjm(i)] and P[m(i)] is as-
sumed to be 1/5. The design value may then be deter-
mined by setting 1-F(d) = 1/365 and solving for d, the
design value. This is equivalent to determining the
concentration d so that:
5
.?- [1-Fi(d)] * 1/5 = 1/365.
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28
In general it may not be possible to explicity
solve this equation for d, but the answer may be ob-
tained iteratively by first guessing an appropriate de-
sign value.
The use of this equation can perhaps best be il-
lustrated by a simple example with two years of data.
Suppose the data for each year may be approximated by
an exponential distribution although the parameter is
different for the two years. In particular let
F1(x) = 1 -EXP(-'43.4x) and
F2(x) = 1 -EXP(-37.6x).
Using the previous equation, the design value (d) must
be determined so that
1/2 EXP(-'l3.4d) + 1/2 EXP(-37.6d) = 1/365 or
365 * (1/2 EXP(-43.4d) + 1/2 EXP(-37.6d)} = 1.
If .15 is used as an initial guess for d this
equation gives a value of .92 rather than 1. If .145
is used the resulting value is 1.12 indicating that the
design value is between .145 and .15. Guessing .148
gives a value of .99,i.e.
365(1/2 EXPC-43.4 * .148) + 1/2 EXP(-37.6 * .148)}=
.99
This is sufficiently close to 1 and is a reason-
able stopping place in determining the design value.
3.4. Quick Test for Design Values
All of the approaches in the previous section have
one thing in common; namely, their purpose. Each tech-
nique is intended to select an appropriate design
value, i.e., a concentration with expected number of
-------
29
yearly exceedances equal to 1. With this in mind a
quick check may be made to determine how reasonable the
selected design value is. This may be done by counting
the number of observed daily values that exceed the se-
lected design value and computing the average number of
exceedances per year. For example, if the selected de-
sign value was exceeded 4 times in 3 years, then the
average number of exceedances per year is 1.3-
Ideally, this average should be less than or equal to
1, but for a variety of reasons somewhat higher values
may occur. However, if this average is greater than
2.0 the design value is questionable. In such cases
the design value should either be changed or, if not
changed, careful examination should be performed to
substantiate this choice of a design value.
3.5., Discussion of Data Requirements
The use of the previous approaches presupposes the
existence of an adequate data base. Both approaches
were presented in the context of having several years
of ambient data. In many practical cases the available
data base may not be so extensive. Although these sta-
tistical approaches may be used with less data, some
caution is still required to ensure a minimally accept-
able data set. In general, statistical procedures per-
mit inferences to be made from limited data sets.
Nevertheless, the initial data set must be
representative. For example, if no data is available
from the peak season, then any extrapolations would re-
quire more than merely statistical procedures.
Therefore, the input data sets should be at least 50$
complete for the peak season with no systematic pattern
of missing potential peak hours. This 50^1 completeness
criterion should be viewed in the context of the type
of aionitoring performed. A continuous monitor that
fails to produce data sets meeting this criteria has in
effect a down-time of more than 50$. With such a high
percentage of down-time for the instrument even the re-
corded values should be viewed with caution.
In employing approaches that group data from all
years into one frequency distribution, it should be
verified that all years have approximately the same
pattern of missing values. Furthermore, if the number
of measurements during, the oxidant season differs by
-------
30
more than 20* from one year to another, then the condi-
tional probability approach should be used. The reason
for this constraint is to ensure that variations in
sample sizes do not result in disproportionate
weighting of data from different years.
Another point of concern is how many years of data
should be used. Intuitively it would be reasonable to
use as many years of data as possible as long as emis-
sions have not changed "appreciably". Obviously this
suggests that some guidance be provided on what percent
change in emissions is permissible. To some degree any
such specification is arbitrary. However, the more
relevant point is that the specified percentage be
reasonable. The reason for a cut-off is to ensure that
the impact of increased emissions is not masked by the
use of air quality data occurring prior to these emis-
sion increases. If an area is in violation of the
standard, then emission changes should be expected as
control programs take effect. Also, the design value
serves as a guide to achieving the standard and is, in
a sense, merely the means to an end rather than an end
in itself. Therefore, no more than a 20% variation be-
tween the lowest and highest years is recommended. It
should be noted that a total variation of 20% may
translate into a + or - 10% variation around the
average.
If emissions have increased by more than 20% then
additional years should not be incorporated unless the
air quality values can be adjusted for the change in
emissions. For cases in which emissions have decreased
by more than 20* the earlier data may be used after ad-
justment or used without change knowing that the design
value will consequently be conservative. Although this
document does not discuss methods for performing this
adjustment, it is useful to mention the basic principle
involved. The selection of a design value inherently
implies the existence of an acceptable model for taking
an air quality value and determining the emission
reduction required to reduce this value to the
standard. In principle, then, this same model may be
used in reverse to take the emission change known to
have occurred and use the model to scale the previous
data sets. Attempting to adjust older historical data
may initially seem to be an unnecessary complication
but the more data that can be used to estimate the de-
sign value the more likely it is that a proper design
-------
31
value is selected. Because considerable effort could
be expended in revising a control strategy this addi-
tional c-i"fort may be warranted.
3.6. Example Design Value Computations
As in the previous discussion of compliance
assessment, it is convenient to conclude this section
with examples illustrating the main point involved in
applying these various techniques. For purposes of il-
lustration all four techniques are used on the same
data set. Figures 3,^, and 5 display semi-log plots of
daily ozone values for 197^, 1975 and 1976 at a sample
site. These data are plotted using previously dis-
cussed conventions. [5] The horizontal axis is concen-
tration (in pprn) and the vertical axis is the fraction
of values exceeding this concentration. A horizontal
dotted line is shown at a frequency of 1/365 and the
dotted line represents a Weibull distribution approxi-
mating the data. This particular fit was done by
"eye-balling" the data, but suffices for the purposes
of illustration. Figure 6 is a similar plot for all
three years of data grouped together. The high and se-
cond high values for the three years are: (.13 and
.12), (.16 and .16), and (.15 and . 1'4).
Method 1: Fitting a single distribution to data fron
all three years.
The Weibull distribution plotted in Figure 6 for
the three years of data is described by the equation:
2. on
F(x) = 1 - EXP[-(x/.0609) ].
Setting F(x) = 1 - 1/365 and solving for x gives
. 1J47 which is the design value because it corresponds
to a frequency of exceedance of 1/365. Using this
quick check, there are three values above . UI7 so the
average number of yearly exceedarices is 1.
-------
32
CO
z
o
10"
DC
Ul
X
< 10 "2-
a
u.
o
z
o
10
0 . °-05 CONCENTRATION, ppm °-10 °-15
Figure 3. Semi-log plot of daily maximum ozone for 1975 (365 daily values).
10
0.05
0.15
0.10
CONCENTRATION, ppm
Figure 4. Semi-log plot of daily maximum ozone for 1976 (303 daily values).
0.20
-------
33
i2
3
^
<
10-
0 0.05 0.10 0.15
CONCENTRATION, ppm
Figure 5. Semi-log plot of daily maximum ozone for 1977 (349 daily values).
10
0.20
CONCENTRATION, ppm
Figure 6. Semi-log plot of daily maximum ozone for three years: 1975, 1976, 1977
(1,017 daily values).
-------
Method 2: Graphical estimation
Referring to Figure 6 it may be seen that the em-
pirical frequency distribution function crosses the
line plotted at 1/365 at a concentration of .15 and,
therefore, this is the design value selected by this
method.
Using the quick check there are only two data val-
ues above .15 and, therefore, the average number of
yearly exceedances of the design value is .67 which is
acceptable.
Method 3: Table look-up
A total of 1,017 data values were recorded during
the three year period. Using Table 1, this method says
that the second highest value may be used as the design
value. Therefore this method yields .16 as the design
value. The quick check gives 0 as the average number of
yearly exceedances of the design value although there
are two values exactly equal to this estimated design
value. As indicated earlier, this procedure is some-
what conservative in that it tends to overestimate the
design value.
Method 4: Conditional probabilities
Separate two parameter Weibull distributions were
fitted to each yearly data set as shown in the graphs.
Using the form of equation 5 gives the equation:
1.835
1/365 = 1/3 EXP{-(d/.0467) ) +
2.139
1/3 EXP{-(d/.0705) } +
1/3 EXP{-(d/.06292)J*°}
Solving for d (by successive guesses) gives .15 as the
design value. Using the quick check gives two values
above the design value and therefore an average yearly
exceedance rate of 2/3.
-------
35
4. APPLICATIONS WITH LIMITED AMBIENT DATA
Virtually all of this discussion has focused upon
the use of ambient data. Historically, air quality
models have been quite useful in providing estimates of
air quality levels in the absence of ambient data. The
proposed wording of the standard does not preclude- the
use of such models. As models that provide frequency
distributions of air quality are developed their use
with the proposed standard will be convenient.
Another potential means of estimating air quality
data involves the use of conditional probabilities.
While the use of conditional probabilities was dis-
cussed earlier in terms of combining different years of
data, a more promising use of this technique would in-
volve the construction of historical air quality data
sets from relatively short monitoring studies. Very
limited ambient data or air quality models may be used
to develop frequency distributions for certain types of
days or meteorological conditions. Then past histori-
cal meteorological data may be used to determine the
frequency of occurrence associated with these meteoro-
logical conditions. This information may then be com-
bined using conditional probabilities to obtain a gen-
eral air quality distribution. This particular ap-
proach could even be expanded to allow for changes in
emissions.
No matter what approach is chosen the two quanti-
ties of interest are: (1) the expected number of ex-
ceedances per year and (2) the design value, i.e., that
concentration with expected number of yearly ex-
ceedances equal to 1. However, these modelling and
conditional probability constructions may make it poss-
ible to assess the risk of violating the standard in
the future based upon limited historical data.
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37
5. REFERENCES
1. 40CFR50.9
2. Fed. Reg.,36(8M):8186(April 30,1971)
3. "Guidelines for Interpretation of Air Quality
Standards," Office of Air Quality Planning
and Standards Publ. 1.2-008 U.S.
Environmental Protection Agency, Research
Triangle Park, North Carolina, February,
1977.
4. Larsen, R.I. A Mathematical Model for
Relating Air Quality Measurements to Air
Quality Standards. U.S. Environmental
Protection Agency Research Triangle Park,
North Carolina Publication Number AP-89,
1971.
5. Curran, T.C. and N.H. Frank. Assessing the
Validity of the Lognorrnal Model When
Predicting Maximum Air Pollutant
Concentrations. Paper Mo. 75-51.3, 68th
Annual Meeting of the Air Pollution Control
Association, Boston, Massachusetts, 1975.
6. Mage, D.T. and W.R. Ott An Improved
Statistical Model for Analyzing Air Pollution
Concentration Data. Paper No. 75-51.4, 68th
Annual Meeting of the Air Pollution Control
Association, Boston, Massachusetts, 1975.
7. Johnson,T. A Comparison of the Two-Parameter
Weibull and Lognorrnal Distributions Fitted to
Ambient Ozone Data, Quality Assurance in Air
Pollution Measurement Conference, New
Orleans, Louisiana, March, 1979.
8- Breiman,L. et al. Statistical Analysis and
Interpretation of Peak Air Pollution
Measurements. Technology Service
Corporation, Santa Monica,California. 1973.
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1 REPORT NO.
EPA 450/4-79-003
3. RECIPIENT'S ACCESSI ON-NO.
4. TITLE AND SUBTITLE
5. REPORT DATE
Guideline for the Interpretation of Ozone
Air Quality Standards
January,
>. PERFORMING (
ORGANIZATION CODE
7. AUTHOR(S)
Thomas C. Curran, Ph.D
8. PERFORMING ORGANIZATION REPORT NO,
9. PERFORMING ORGANIZATION NAME AND ADDRESS
10. PROGRAM ELEMENT NO.
U.S. Environmental Protection Agency
Office of Air, Noise and Radiation
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
11. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS
13. TYPE OF REPORT AND PERIOD COVERED
14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
Special mention should be made of the contributions of William M. Cox,
Thomas B. Feagans, William F. Hunt, Jr. and Sherry L. Olson
16. ABSTRACT
This document discusses the interpretation of the National Ambient Air
Quality Standards (NAAQS) for ozone that were promulgated by the U.S.
Environmental Protection Agency in 1979. These standards differ from
previous NAAQS in that attainment decisions are based upon the expected
number of days per year above the level of the standard. The data
analysis implications of this statistical formulation of an air
quality standard are presented for both compliance assessment and
design value estimation purposes. Example calculations are included.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS C. COSATI Field/Group
Air Pollution Standards
Design Values
Ozone
18. DISTRIBUTION STATEMENT
Release Unlimited
19. SECURITY CLASS (This Report)
f led
21. NO. OF PAGES
20. SECURITY CLASS (Thispage)
Unclassified
22. PRICE
EPA Form 2220-1 (9-73)
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