EPA-450/4-84-010
PROCEDURES FOR ESTIMATING PROBABILITY
OF NONATTAINMENT OF A PMiQ NAAQS USING TOTAL
SUSPENDED PARTICULATE OR INHALABLE PARTICULATE DATA
Thompson G. Pace
Air Management Technology Branch
and
Neil H. Frank
Monitoring and Reports Branch
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air Quality Planning and Standards
Monitoring and Data Analysis Division
Research Triangle Park, North Carolina 27711
February 1984
U.S. Envirorvnon-1- I Fr * ration Agency.
Region V, ! ; "
230 Soui:-. r--- . ,'. .. -.C3t
Chicago, li:;r.. 3 t^o04
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This report has been reviewed by The Office of Air Quality Planning and
Standards, U.S. Environmental Protection Agency, and has been approved
for publication. Mention of trade names or commercial products is not
intended to constitute endorsement or recommendation for use.
Technical Note
In order adequately to illustrate the procedure described
in this report, it was necessary to assume a cutpoint and
values for the annual and 24-hour NAAQS. The decision
concerning the appropriate values for the NAAQS has not
yet been made. We have arbitrarily chosen to illustrate
the procedure, assuming values at the lower edge of the
range of standards proposed by the Administrator. Should
the Administrator choose to promulgate NAAQS different
from those assumed in this report, several of the curves
(i.e., Figures A, B, 3, and 4, and Table 1) may have to
be revised. However, the procedure described herein would
be similar. We have also included sets of curves applic-
able for other values of the proposed range of NAAQS. In
applying the methodology, one would simply substitute these
curves for their counterparts as indicated in the text.
Environmental Protection Agency
ii
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TABLE OF CONTENTS Page
List of Figures .................................................. iv
List of Tables [[[ v
Executive Summary ................................................ vi
1.0 Introduction ................................................ 1
2.0 Available Ambient Parti cul ate Matter Data ................... 2
2.1 Total Suspended Particul ate (TSP) ...................... 2
2.2 Inhal able Parti cul ate (IP) ............................. 3
2.3 PM ...................... . ............................ 4
3.0 Use of Available Data to Draw Inferences About PMio Levels .. 5
3.1 Ratio of PMio and IP to TSP ............................ 5
3.2 Ratio of PMio to IP .................................... n
4.0 Methodology for Estimating the Probability of Nonattainment
for PMio NAAQS - Annual Standard ............................ 13
5.0 Methodology for Estimating the Probability of Nonattainment
for PMio NAAQS - 24-hour Standard ........................... 18
5.1 Attainment Assessment Based on Adequate PMio Data ...... 18
5.2 Attainment Assessment Without PMio Data ................ 22
5.2.1 Failing the Attainment Test for Sites Sampling
Less Frequently Than Once in Three Days .......... 24
5.2.2 Failing the Attainment Test Based on Sampling
Once in Three Days or More Frequently ........... 26
5.3 Attainment Assessment Based on Some PMio Data .......... 28
6.0 Estimating the Spatial Extent of Nonattainment Situations ... 33
6.1 Introduction ........................................... 33
6.2 Use of Acceptable Air Quality Data ..................... 34
6.2.1 Type of Monitor ................................. 34
6.2.2 Monitor Location ................................ 35
6.2.3 Data Quality .................................... 35
6.3 Determining the Boundaries of a Nonattainment Area ..... 36
6.3.1 Qualitative Analysis ............................ 36
6.3.2 Spatial Interpolation of Air Monitoring Data ---- 37
6.3.3 Air Quality Simulation by Dispersion Modeling ... 38
7.0 Acknowledgements ............................................ 39
8.0 References .................................................. 40
Appendi x A [[[ A
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LIST OF FIGURES
Figure No. Page
A Relationship Between the Probability of Exceeding
a 50 pg/m3 Annual PMjg Concentration and Observed
TSP Annual Arithmetic Mean Concentration ix
B Relationship Between the Probability of Exceeding
a 150 pg/m3 24-hour PMjo Concentration and Observed
TSP 24-hour Concentration xi
1 Distribution of IP/TSP Ratios for Site Average Data.... 9
2 Distribution of IP/TSP Ratios for Individual
24-hour Observations 10
3 Relationship Between the Probability of Exceeding a
50 pg/m3 Annual PMjo Concentration and Observed TSP
Annual Arithmetic Mean Concentration 15
4 Relationship Between the Probability of Exceeding a
150 pg/m3 24-hour PMio Concentration and Observed
TSP 24-hour Concentration 23
A1 Relationship Between the Probability of Exceeding
a 65 pg/m3 Annual PMjQ Concentration and Observed
TSP Annual Arithmetic Mean Concentration B-2
B' Relationship Between the Probability of Exceeding
a 250 pg/m3 24-hour PM^o Concentration and Observed
TSP 24-hour Concentration B-3
3' Relationship Between the Probability of Exceeding a
65 pg/m3 Annual PMio Concentration and Observed TSP
Annual Arithmetic Mean Concentration B-2
4' Relationship Between the Probability of Exceeding a
250 pg/m3 24-hour PMio Concentration and Observed
TSP 24-hour Concentration B-3
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LIST OF TABLES
Number Page
A Summary of the Appropriate Use of National Frequency
Distributions, Site Specific Ratios and Site Specific
Frequency Distributions xiv
1 Allowable Observed Exceedances as a Function of Sample
Size for a One Expected Exceedance Standard 21
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EXECUTIVE SUMMARY
The newly proposed National Ambient Air Quality Standards (NAAQS) for
Participate Matter (PM) specify ambient concentrations for particles smaller
than 10 micrometer aerodynamic diameter (PMio), as well as Total Suspended
Particulates (TSP). Unless measured PM^Q ambient concentrations are avail-
able, ambient measurements of other PM size fractions, such as Total
Suspended Particulate or Inhalable Particulate (IP), which is an earlier
terminology for PM < 15 pm aerodynamic diameter, must be used to provide
estimates of PMio concentrations. In this document, emphasis is placed on a
methodology for using available TSP or IP measurements to estimate whether
or not the annual and/or 24-hour NAAQS for PMio are likely to be exceeded
(probability of nonattainment). The document also suggests appropriate
methods for determining the apatial extent of the nonattainment situations.
It should be noted that further research is being done with the PMio data
which may affect the final version of this document.
The probability of nonattainment is defined by a series of calculations
which are based on data from a nationwide network of collocated ambient TSP
and IP samplers operated by or for the U.S. Environmental Protection Agency
(EPA) during 1980-82. These data include TSP as measured by the high
volume sampler and IP and PMio, both as measured by the dichotomous sampler.
Transition from IP to PMio is based on data from a limited number of sites
having collocated TSP, IP and PMiQ samplers. The calculated probability
represents the likelihood that either NAAQS for PMio was violated at the
sampling site. The probability of nonattainment will be one of the criteria
which may be used to specify action States are to take in developing
monitoring requirements and State Implementation Plans.
vi
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The following hierarchy is defined for using available ambient
measurements to determine attainment/nonattainment directly or to estimate
the probability of RMjo nonattainment. The first preference is to use
ambient PM^Q data, providing a site has complete sampling. PM^o data should
be used if sufficient (i.e., with sampling every day with a 75% data capture
[see Section 2.4 of proposed Appendix K to 40 CFR, Part 50]) data are
available. The second preference is to use IP measurements obtained with a
dichotomous sampler.* As described in this document, these measurements
times a correction factor of 0.8 may be assumed equivalent to PMio» and
sufficient for attainment/nonattainment determination, providing at least
one full year (every day sampling, 75% data capture) of IP data is available.
Use of this constant correction factor is an interim procedure pending
availability of more PM^o data. A third preference is to use PM^o or IP
data with less complete sampling in conjunction with TSP data to draw
inferences about PM^o nonattainment. The fourth preference is to use TSP
data alone to draw inferences about the probability of PMjo nonattainment.
Such inferences are drawn on the basis of IP and PM^g to TSP ratios observed
at selected sites in the National IP Monitoring Network.
For the annual NAAQS, IP/TSP ratios are computed from arithmetic mean
concentrations of IP and TSP at different sampling sites. These ratios are
multiplied by 0.8 to convert IP to PMio, thus obtaining site average PMjQ/TSP
* If size selective hi volume samples were collected on quartz fiber
filters, these concentrations may be treated as dichotomous sampler
measurements. Otherwise, the use of the term IP in this document refers
to those particles collected by the dichotomous sampler with a
15u size discriminating inlet and teflon filters.
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ratios. Frequency distributions of the resulting PMio/TSP ratios have been
plotted and used to derive figures such as Figure A. Using Figure A, the
probability of nonattainment of the annual PMio NAAQS can be estimated
directly from the average TSP concentration for the most recent three
complete years of sampling. An example is presented in Section 4.0.
In the case of 24-hour data, observed PMio/TSP ratios have been used
to derive a frequency distribution of ratios. This distribution is then
used in conjunction with TSP data to estimate the likelihood of not attaining
a 24-hour NAAQS for PMio. For example, at sites sampling TSP less frequently
than once every 3 days, these estimates are made using Figure B and equations
(a) and (b).
P0=TTqi (a)
1=1
where
P0 = probability of observing ££ PMio concentrations greater
than the level of the PMio NAAQS
Qi = (1-Pi) = the probability that an observed TSP value,
TSP-j, does not correspond with a PMio value greater
than the level of the PMio NAAQS
n = the number of TSP values greater than the level of the
PMio NAAQS
3
~[~[ = multiplication symbol such that TT Qi = (qi)(q2)(°.3)
1-1
and
Pp(0) = 1 - PO (b)
where
Pp(0) = probability of failing the attainment test (i.e.,
observing one or more PMio concentrations greater
than the level of the PMio NAAQS).
viii
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OI
Wd
OS
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As equation (a) suggests, for each 24-hour TSP concentration greater
than 150 ug/m3 there is an associated probability, p-j, that the corres-
ponding PMio concentration is also greater than the level of the NAAQS
(i.e., 150 ug/m3). This probability, Pi, is determined for each high TSP
value by using Figure B. For example, if a site has three 24-hour TSP
concentrations greater than 150 ug/m3, Figure B is used three times to
estimate the probabilities associated with each of the three high TSP
values. The pj determined from Figure B are then used in equation (a) to
estimate the probability of observing no PMio concentrations greater than
the level of the PMio NAAQS. For sites sampling less frequently than once
every 3 days over a 3 year period or less, there can be jio observed PMio
concentrations greater than the level of the PMio NAAQS if the NAAQS is to
be met. Hence, the probability of violating the PMio NAAQS at a site is
simply the probability of observing one or more PMjQ concentrations greater
than 150 ug/m3 (i.e., the level of the NAAQS) at the site. This is simply
the complement of observing no PMio values above 150 ug/m3, and is computed
using equation (b). This is illustrated by Example 3 in the text.
If samples are collected at a site at least as frequently as once
every 3 days over a 3 year period, the NAAQS does allow one or more PMio
concentrations greater than the level of the NAAQS to be observed. For
example, if sampling occurred once every 2 days over a 3 year period, one
observed exceedance would be allowed during the 3 year period. In this
case, the probability that a site is not in compliance with the NAAQS is
the probability of observing two or more PMio concentrations greater than
the level of the NAAQS, and is given by equation (c).
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091
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PF(I) = i - (PO + P!) (c)
where
Pp(l) = probability of observing more than one PM^o concentration
greater than the level of the NAAQS
P! = probability of observing one exceedance, as determined
using procedures described in Section 5.2.2
Although the methodology is slightly more time consuming for sites sampling
at least once every 3 days over a 3 year period, it is straightforward and
is described in Section 5.2.2 and illustrated by Example 4 in the text.
As with the annual NAAQS, the 24-hour procedure is simplifed somewhat
if ambient PM^o data exist. In this case, the estimated number of
exceedances in a given year, E-j, is calculated by equation (d).
Ei = 6i (N)/ni (d)
where
Ei = the estimated number of exceedances for year i
ej = the observed number of exeedances for year i
n-j = the number of data values observed for year i
N = total number of possible values in a year (e.g., 365)
The estimated number of exceedances over a 3 year period would be based on
the average of the Ei for each of the 3 years, as shown in Examples 1 and
2 in the text. If only one or two years of PM^g data are available, the
methodology is discussed in the text and illustrated in Examples 5 and 6.
If a statistically defensible site-specific frequency distribution of
to TSP ratios for the 24-hour NAAQS can be developed, it may be used.
xi i
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Otherwise, the national distribution should be used for the years with TSP
data. If IP (rather than PMio) data are used, the IP/TSP ratios comprising
the distribution are multiplied by the correction factor of 0.8. For both
annual and 24-hour data, a site specific ratio or distribution can be based
on a nearby, similar site. To do this, it must be demonstrated that the
two sites are similar and that the ratio or distribution would be more
applicable than the national distribution. Table A summarizes the use of
national and site specific ratios and frequency distributions.
Determining the spatial extent of a nonattainment situation area
requires subjective judgment. Three procedures are identified in Section 6.0
as useful in helping to arrive at this conclusion. These are:
(1) a qualitative analysis of the area of representativeness of
the monitoring site, together with consideration of terrain, meteorology
and sources of emissions;
(2) spatial interpolation of air quality monitoring data;
(3) air quality simulation by dispersion modeling.
Choice of which procedure or combination of procedures to use depends on
the available information and the complexity of the PM^o problem area.
xm
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Table A. Summary of the Appropriate Use of National Frequency Distributions,
Site Specific Ratios, Site Specific Frequency Distributions and
Direct Use of PMjo and IP Data.
Data Available at Study Site for Year of Interest
Type of Data Annual 24- Hour
PMjo direct use of data* direct use of data*
IP convert data* to PMio convert data* to PMjo
using IP x 0.8 using IP x 0.8
TSP national frequency national frequency
distribution distribution
Data From Study Site in Different Year or Data From Similar Site
Type of Data Annual 24- Hour
convert data at study
site using TSP
PHin x (valid TSP mean used to develop site
TSP at study site specific frequency
for the year distribution to replace
of interest) national distribution**
IP 1L_ x 0.8 x (valid TSP IP_ x 0.8
TSP mean at study site TSP
for the year used to develop site-
of interest) specific distribution
to replace national
frequency distribution*
* Completeness Requirements for
# years of data NAAQS Attainment Test (I of observations)
Annual 24-hour
1 274 274
2 48 (12/Qtr) 183
3 48 (12/Qtr) 48 (12/Qtr)
** Provided a statistically defensible site-specific distribution can be
developed. If not, use national distribution.
xiv
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1.0 INTRODUCTION
The promulgation of the National Ambient Air Quality Standard (NAAQS) for
Particulate Matter (PM) will require the revision of State Implementation Plans
(SIPs) to account for the new standards. Along with a secondary NAAQS applicable
to Total Suspended Particulate (TSP), the revised standards include an annual
and a 24-hour NAAQS specified in terms of PM nominally 10 micrometers and
smaller in terms of aerodynamic diameter (PMio).* Unfortunately, there are few
measured data for this size fraction of PM. Other ambient data such as TSP and
Inhalable Particulate (IP), which include PMio but with larger particles as well,
are available. The purpose of this document is to describe a methodology for
using these data to estimate the probability of nonattainment of the annual and
24-hour NAAQS for PMio at various sampling sites in the country. The probability
estimates will be used in conjunction with the Environmental Protection Agency
(EPA) policy to help define where certain actions will be required.
This document first discusses various measurement methods used to obtain
the underlying rationale and methodologies for inferring ambient PMio levels
from available data. Methodologies for estimating the likelihood of not attain-
ing PMio NAAQS are presented, given ambient TSP data obtained with a high volume
sampler. A procedure for estimating PMio levels using IP data obtained with a
dichotomous sampler** is also described. Finally, limitations of the above
methodologies are identified.
*A method of specifying particle diameter which considers both physical
diameter and particle density.
** In this document, the term IP is used to denote particulate data collected
with a dichotomous sampler that has a 50% collection efficiency of 15 urn
particles. If size selective hi-volume samples were collected on quartz
fiber filters, these concentrations may be treated as dichotomous sampler
measurements.
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2.0 AVAILABLE AMBIENT PARTICULATE MATTER DATA
The most desirable way to determine nonattainment of the proposed
NAAQS is to measure PM^g directly. Several monitoring instruments are
currently under development and being tested by the EPA. These instruments
appear promising for future use. Unfortunately, sufficient data collected
by these instruments are not yet available at most locations. To minimize
discontinuity in PM abatement efforts in locations where attainment of the
new NAAQS may still be a problem, other ambient data must be used to direct
abatement programs.
2.1 Total Suspended Particulate (TSP)
The most common measurement of PM concentration available is TSP, as
measured by the high volume sampler (hi-vol).(l) The hi-vol is generally
considered to measure PM less than 100 urn aerodynamic diameter, but the
collection efficiency (ability to capture) the very large particles is
very poor. With average wind speeds, the sampler is about 50% efficient
in collecting particles of 25-45 urn aerodynamic diameter. Thus, the sampler
is said to have a DSQ of 30 urn, where OSQ is the particle diameter for 50%
collection efficiency. For the purpose of this discussion, the hi-vol is
considered to capture 100% or all particles smaller than 10 urn.
The hi-vol is generally considered to have several deficiencies which
can cause problems in data interpretation. The 059 is dependent on wind-
speed and the orientation of the sampler. Also, the glass fiber filter has
been shown to collect artifact sulfate of as much as 5 ug/m3 or higher
in high sulfate areas of the country.(2) Other artifact components such as
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nitrate and organic participates may be significant in some areas. Another
problem is the design of the hi-vol inlet which allows particles to be
blown into the shelter and settle onto the filter during periods when the
sampler is not operating.(3) Despite these problems, the hi-vol has been
the standard reference method for TSP for many years and a vast data base
is available for immediate use in screening potential nonattainment areas.
Basing PMjo estimates on empirically derived relationships between PM^o
and TSP lessens the degree to which these problems affect the validity of
the final designations.
2.2 Inhalable Particulate (IP)
The dichotomous sampler (DS) has been used in a national IP network
of 164 sites operated by the U.S. EPA since 1979.(4,5) This network
generally represents sites in urban areas with high concentrations. However,
since these high sites are of the most interest in estimating nonattainment
areas, the network is considered representative for the purpose of this
document. A total of 254 site years (5733 IP observations) were used in
this analysis.
IP data are collected by a dichotomous sampler whose inlet is
designed to collect particles of 15 jjm at 50% efficiency. The sampler
separates the particles which pass through the inlet into two flowstreams
(fine, <2.5 pm and coarse, 2.5-15 urn) and deposits them on two filters.
However, wind tunnel tests of the inlet have shown that this sampler had a
of less than 15 urn at most windspeeds.(6)
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Other potential problems which would bias reported IP results downward
include internal wall losses (believed to be small) and the loss of particles
from the coarse filter. This loss has been shown to occur on highly loaded
filters during handling and shipment but is not believed to be a problem
during routine network operation.(7)
2.3 PMin
The national IP network also operated 39 sites equipped with dichotomous
samplers measuring 10 urn. (Note: 19 site years of data (287 observations)
are presently available for analysis. Further analysis will be made as
data become available.) These samplers are identical to those IP samplers
described above, except for the inlet which is designed to collect particles
of 10 um at 50% efficiency. In some cases the TSP, IP and PMjo samplers have
been operated concurrently and in other cases the IP sampler has been
discontinued and only TSP and PM^o continue to be collected.
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3.0 USE OF AVAILABLE DATA TO DRAW INFERENCES ABOUT PMio LEVELS
The EPA Inhalable Parti cul ate Network mentioned previously offers an
excellent data base on TSP, IP and PMio at collocated sites. (8) These samplers
were operated at 164 sites nationwide in the IP network, beginning as early
as 1979 at some locations. The sites were located in urban and suburban
locations to reflect maximum concentration and population exposure due to
urban and industrial sources, and at nonurban sites to provide information
on background levels. The data from these sites are used, to draw conclu-
sions about relationships among PMio. IP and TSP. Individual dichotomous
sampler sites are listed in Appendix A.
The data used for investigation of the individual observations were
collected from January 1980 - December 1982 and were those available in the
data base on November 10, 1983. These data from the IP network were screened
and validated by the EPA's Environmental Monitoring Systems Laboratory
(EMSL). Six sites were deleted later, upon EMSL's recommendations. (9 ,10,11,12)
3.1 Ratio of PMm and IP to TSP
The ratio of IP/TSP and PMjo/TSP was examined at the sites comprising
the data base in the hope that a simple ratio could be calculated which
would permit the direct adjustment of TSP and IP to
In order to determine whether the variability in the number of
observations at any site is likely to introduce any bias to subsequent
calculations, observations in a given city were weighted by the proportion
of observations with high TSP values in each city. Thus, if city A had 200
observations of which 20 corresponded with high TSP and city B had 100
observations with 20 corresponding high TSP values, ratios used from city B
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to help derive a national distribution of IP/TSP ratios were weighted twice
as heavily as those from city A. The ratios of IP to TSP were calculated
for each 24-hour observation, and the mean of these 24-hour ratios was
calculated for each site. The weighting scheme did not make any difference
in the distribution of ratios, so it was not used in the preparation of the
final distributions. Unfortunately, there was a high degree of variation
in the value of the ratios. The high variability in these ratios is due in
part to inter-site variability.
Several attempts have been made to find an explanatory site descriptor
which could account for the disparity in the ratios among sites (i.e.,
inter-site variability). In the first attempt, such site descriptors as
urban versus suburban were compared; however, no statistically significant
difference was found. Geographic area (East, Southwest, West Coast, etc.)
and site type (industrial, commercial or residential) likewise revealed
insignificant differences in the ratios.(10) A more extensive investiga-
tion of geographic differences was then performed on the complete data
base described above. Some geographic differences among individual sites
were found, but the differences among larger groupings of sites were
generally very small and difficult to explain on a physical basis.
Geographic differences based on climatological factors could not be found.
Because there were more IP sites and they have been in operation longer,
the studies of geographic and climatological differences were made using
IP/TSP ratios instead of the more limited PMio/TSP data base. The results
of attempts to identify geographical differences in the IP/TSP relationship
on the complete data base have been documented in References 11 and 12.
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These investigations conclude that unless sufficient data to calculate a site
specific PMio/TSP ratio are available, the existing data base does not justify
use of different distributions of ratios for different parts of the country.
It was first suggested by Watson that the ratios may be dependent on
TSP concentration. That is, sites or days with higher TSP concentrations
have lower ratios.(12) This finding is potentially significant, because
the days with high concentrations and the sites with high annual arithmetic
mean concentrations are of most concern. Because ambient TSP must be
greater than PMio» only those sites with relatively high annual arithmetic
mean TSP and/or 24-hour concentrations may exceed the PMjQ NAAQS. Because
the IP/TSP ratio may be dependent on the ambient level of TSP, and because
only the high concentrations are of concern, Thrall and Burton made some
preliminary calculations in which only sites with annual arithmetic mean
TSP greater than 55 ug/m3 were used in the site average analysis and only
24-hour concentrations greater than 150 ug/m' were used in the individual
day analysis.(11) For concentrations above these values, the ratios appeared
to stabilize and certaintly did not decrease appreciably over the range of
concentrations available. In the most recent analysis, including another
year of data and with additional screening of suspect high ratios at low
concentrations, Thrall and Hudischewskyj concluded that there was no sub-
stantial difference associated with concentration and recommended that the
larger (undifferentiated) data base be used.
The previously described investigations of geographic, climatological,
concentration range or site type classifiers was an attempt to reduce or
account for part of the variability in PMio °r IP to TSP ratios. As discussed
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in Section 2.0, there are several issues associated with the precision of
the TSP, IP and PM^o measurements which affect intra-site variance. These
factors include windspeed dependence, weighing problems, artifact formation
and sampler wall losses. Thus, the inter-site variance can potentially be
eliminated by the use of site specific data, but the intra-site variance
can only be partially reduced by careful operating procedures.
The previously described variance among IP/TSP or PMio/TSP ratios suggests
the need to examine the frequency distribution of ratios rather than relying
on a single value for the ratio. The cumulative frequency distribution of
IP/TSP is plotted in Figure 1 for site average (arithmetic mean) ratios.
Unfortunately, there are not enough sites reporting PMio data through 1982 to
develop a distribution for the ratio of annual mean PM^o to annual mean TSP
(i.e., PMio/TSP). Section 3.2 provides an adjustment factor from IP to PM^o
which allows us to derive a distribution for PMjQ/TSP indirectly. The data
are sufficient however to allow us to derive a distribution for PMio/TSP for
individual days. This distribution is presented in Figure 2. In Figures 1
and 2, the cumulative frequency of observed ratios below a given value may
be read along the bottom axis. For example, from Figure 1 we see that the
IP/TSP ratio is less than .69 in 90% of the cases observed. The upper axis
in these figures represents the frequency of occurrence of ratios above a
given value, which is the same as saying the frequency of exceeding a given
ratio (PMio/TSP). Upper axis values are simply complements of the correspond-
ing lower axis values.
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Another factor to consider is the development and use of site specific
ratios or distributions for both annual and 24-hour cases. It seems logical
that, if an area can justify a statistically different site or area specific
distribution , its use should be encouraged, A site or area specific ratio
of PMio/IP or frequency distribution of PMio/TSP may be developed if 1 year
of PM}0 or IP dichotomous sampler data is available. (Note: if the site
specific data are IP instead of PMjQ. each ratio of IP/TSP should be multi-
plied by an adjustment factor to obtain a PM^o/ISP ratio before plotting
the site specific distribution.) A distribution based on another site in
the area may be used only if it is demonstrated by an appropriate statis-
tical procedure that the sites are similar and the specific distribution is
a better representation of the data at that site than is the national
distribution.
3.2 Ratio of PMm to IP
Watson investigated the theoretical ratio of PMjg to IP collected by
the dichotomous sampler for typical wind speeds. He found the ratios to be
0.9 for the dichotomous sampler rounded to one significant figure, based on
a size distribution curve by Lundgren.(lO) Actual data from collocated
operation of 19 dichotomous samplers with 10 (jm DSQ inlets and 19 samplers
with DSQ 15 pm inlets were obtained from the EPA-IP network. Operation of
these samplers began in January 1982. Analysis of data available thus far
shows that the ratio of PMio/IP is 0.8.(12) Ultimately, it may be possible
to develop an annual mean PMjg/TSP frequency distribution for annual means
by combining the IP/TSP and PM^o/DS distributions. However, this can not
be done until the PMjg data base is much larger. As an interim,
11
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concentrations may be approximated by multiplying IP values by 0.8 in the
absence of measured PMio data. In the event that ambient measurements were
obtained using a size selective hi-volume sampler with quartz fiber filters,
these measurements may be treated as IP dichotomous sampler measurements.
The frequency distributions for IP/TSP in Figure 1 must be multiplied by
0.8 to give an estimate of the PMio/TSP frequency distribution. This is in
contrast to Figure 2 which depicts a distribution for PMio/TSP ratios derived
directly from a relatively large number of observed 24-hour concentrations
of PMio ancl TSP at collocated monitors.
12
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4.0 METHODOLOGY FOR ESTIMATING THE PROBABILITY OF NONATTAINMENT FOR
NAAQS - ANNUAL STANDARD
It is preferable to have sufficient ambient PMio or IP data. The
data may be used directly and the IP data (from a dichotomous sampler with
a 15 pm inlet or a 15 urn size selective hi-vol with quartz filters) may be
converted to PMio by multiplying the IP by 0.8. However, the probability
of nonattainment of one or both PMio NAAQS can also be estimated for any
location, given observed TSP data. The probability of not attaining the
proposed annual standard, given annual arithmetic mean TSP data, is determined
in a straightforward manner. A brief explanation and example are provided
herein. Calculating the probability of not attaining the proposed 24-hour
standard is more complicated. This requires a more detailed explanation, and
will be discussed in Section 5.0.
It is possible to obtain an estimate of the probability of
nonattainment of a 50 ug/m3 level of the annual PMio NAAQS by using annual
arithmetic mean TSP data and the information in Figure 1. First, as
previously discussed, we assume that the IP frequency distribution curve
adjusted downward by 0.8 (TP/TSP ratio x 0.8) is a reasonable approximation
of the PMio/TSP frequency distribution. We can define TSP as:
TSP = IP concentration
IP/TSP ratio
substituting PMio f°r IP»
TSP = PMio concentration
IP/TSP x 0.8
13
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For any fixed level of PMiQ, such as a proposed NAAQS for PMio of
50 ug/m3, the value of TSP which would correspond to a given probability
of exceedance can be calculated. For example, in Figure 1 there is a 70%
probability that the IP/TSP ratio will be greater than .50. Substituting
into the above equation, a TSP concentration of 125 ug/m3 is found. This
is the TSP value that, if measured, would correspond to a 70% probability
that the proposed PM^o NAAQS of 50 ug/m3 would be exceeded. A series of
these calculations was made to develop the plot in Figure 3.
The relationship in Figure 3 can be used to estimate the probability
of nonattainment at any site with annual arithmetic mean TSP data. To use
Figure 3, the average annual arithmetic mean TSP concentration is calculated
for the site. The figure is entered for that TSP value and a corresponding
probability of nonattainment is read. For example, if the average annual
mean TSP were 150 ug/m3, the probability of nonattainment would be .90 or
90%.
For the purpose of estimating the probability of nonattainment at a
specific site, the average of the annual arithmetic means of the most
recent three year's data should be used, if available. For example,
TSP = (Tsp)a? + (TSP)RI + (Tsp)an (D
3
where (TSP)s2 is the arithmetic mean TSP concentration observed
during 1982, ug/m3, etc.
14
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As an example, if the arithmetic mean TSP concentrations for the
years 1980, 81 and 82 were 135, 142 and 158, the TSP would be (135 + 142 +
158)/3 = 145 pg/m3. Figure 3 would indicate a 88% likelihood of exceeding
an arithmetic mean PMio NAAQS of 50 pg/m3. This is quite different from a
determination of the attainment status for the current annual TSP NAAQS.
The current TSP NAAQS considers the geometric rather than arithmetic mean.
Further, no probability calculation is required since direct measurements
of TSP are available.
If a full year of valid data (i.e., at least 12 samples per quarter)
of PMio or IP is available, it may be used to develop a site specific ratio
for PMiQ to TSP. The IP data would be multiplied by 0.8. For example, if
the IP measured 50 pg/m3 annual arithmetic mean, the PMio estimate would be
0.8 x 50 or 40 pg/m3. The annual arithmetic mean should be computed by
taking the mean of the quarterly mean concentrations as described in
Appendix K to Part 50, Code of Federal Regulations (CFR).
The PM^o annual mean, divided by the annual mean of collocated and
concurrent TSP data gives a site specific ratio which can be used to esti-
mate PMjo f°r tne tw° previous years. The mean of these PMio estimates
would be compared directly to the NAAQS and no probability estimate would
be needed.
i = PMloi/TSP
i = TSPi.ixSSRi
PMiQi-2 = TSPi_2xSSRi
16
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where
SSR-j = site specific ratio for the year available (i)
i-i 2 = ^e estimated PM^g annual mean for the preceding year(s),
based on TSP in those years and the SSR
Thus, the PM^o f°r the three year's data would be
+ PMiOi-1 + PMiOi-2)/3
In summary, the annual PMjQ NAAQS attainment status may be estimated
directly using PM^o data or by using IP data and multiplying the arithmetic
mean by 0.8. The probability of nonattainment may be estimated using TSP
data and the frequency distribution method described above.
The following steps apply in inferring PMio levels at sites in which
only TSP data are measured:
(1) calculate the average arithmetic mean TSP, as described in
Appendix K to Part 50, Code of Federal Regulations;
(2) enter Figure 3 (for TSP) and read the corresponding
probability of nonattainment of the annual arithmetic mean NAAQS for PMjo«
17
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5.0 METHODOLOGY FOR ESTIMATING THE PROBABILITY OF NONATTAINMENT FOR
NAAQS - 24- HOUR STANDARD
The 24-hour NAAQS for participate matter (PM) specifies that the
expected number of exceedances must be less than or equal to one per year.
The attainment test consists of using monitoring data to estimate the
average number of exceedances expected with complete sampling over a
multi-year time period. The test specifies that the average number of
estimated exceedances be rounded to the nearest tenth (.05 rounds up).
Thus, an estimated number of 1.05 (which becomes 1.1) exceedances per year
would be required in order to fail the attainment test. Although 3 years
is recommended for the time period, 1 or 2 years may also be used if
3 years of data are not available.
During the transition stages of implementation of the PMio NAAQS when
actual PMio monitoring data may not be available, assessment of attainment
(probability of nonattainment) can be based on available TSP data. As
monitoring is initiated, these data would also be incorporated into the
nonattainment probability assessment. The following discussion addresses
attainment/nonattainment assessment for three cases: (1) adequate
data, (2) no PMio data, and (3) some PMio data.
5.1 Attainment Assessment Based on Adequate PM-^ Data
If 3 years of PMio data are available, the application of the
attainment test is relatively straightforward. The approach consists of
estimating the number of exceedances per year from the observed monitoring
data and then averaging these estimates over a 3-year period. If 2 years
18
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of PMio data are available, this approach may also be considered. The data
requirements for application of this procedure are described in Appendix K
to CFR Part 50.
The formula for estimation of exceedances, E^ from a year of PM^g
monitoring data is as follows:
E1 = e1 x N / n1 (2)
where
E.J = the estimated number of exceedances for year i,
assuming complete sampling
e.j = the observed number of exceedances for year i
n.j = the number of data values observed in year i, and
N = the total number of possible values in year (e.g., 365)
Note that E\ is also called the estimated exceedance rate,
Example 1
In 1980, a hypothetical site measured 49 PM^o values. Two exceedances
of the level of the NAAQS were observed. The recorded concentrations were
220 and 260 ug/m3. The estimated number of exceedances is calculated
using equation (2) as
E80 = (2) x (365) / 49 = 14.9
Note that the concentration magnitudes of the observed exceedances were not
considered. The magnitudes would be important, however, when the amount of
required control is evaluated.
19
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The estimated number of exceedances over a 3-year period would be
based on the average of the estimated number of exceedances for each year.
If the numbers of estimated exceedances (E-j) for three successive years
were 7.1, 0, and 14.9, respectively, then the average number of estimated
exceedances, rounded to the nearest tenth, would be 7.3. Since 7.3 is
greater than 1.0, this site would fail the attainment test.
Although attainment of the 24-hour expected exceedance NAAQS can be
determined in terms of the average number of estimated exceedances (as in
the above example), the attainment test can also be done in terms of an
allowable number of observed exceedances for a specific number of sampling
days.
The number of allowable observed exceedances over 3 years as a function
of sample size, (i.e., combined 1, 2 or 3 year sample size), is shown in
Table 1. With the use of this table, it is assumed that the sampling rates
are similar in each year. For the once in 6-day sampling rate historically
applied to TSP, 0 exceedances would be allowed in 1, 2 or even 3 years of
sampling data. This follows because a site with a sample size as small as
183 (i.e., 3 x 61 samples/year) would fail the attainment test if it had
1 or more observations greater than the level of the NAAQS, according to
Table 1.
Example 2
As stated in Example 1, two exceedances were observed for a site in
1980 that sampled 49 PM^o values. Suppose that in the two preceding years,
20
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TABLE 1. Allowable Observed Exceedances As A Function Of Sample Size
For A One Expected Exceedance Standard.
Allowable Number of Sample Size, Observations
Observed Exceedances in 1-3 Years
0 < 347
1 348- 695
2 696-1042
3 1043-1096
21
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1 and 0 PMio exceedances were observed and that the number of sampling days
was 55 in both of these years. For the 3 years, there was a total sample
size of 159 observations and from Table 1, we see that no exceedances are
allowed at this sampling rate. Thus, the three observed exceedances cause
a failure of the attainment test.
5.2 Attainment Assessment Without PMio Data
Unlike the 'yes-no' situation with actual PMio monitoring data, the
failure of the PMio attainment test using TSP data will be expressed as a
probability. This probability will take into account the chance of a PMio
NAAQS exceedance on each TSP sampling day. The probability of failure is
defined in terms of the likelihood of observing more than the number of
allowable PMio NAAQS exceedances. The conditions specifying failure of the
attainment test depend on TSP sampling frequency as outlined in Section 5.1
(see Table 1).
The chances of a PMio NAAQS exceedance on each TSP sampling day is
derived from the estimated probability distribution of the relative PMio
portion of TSP (Figure 2). This distribution specifies the probability
that the PMio portion of the TSP would have exceeded a stated fraction.
For a specific TSP concentration, these ratio probabilities translate into
the probability that the concentration of the PMio portion of the TSP would
have exceeded a given PMio concentration level. A curve of "exceedance"
probabilities for a PMio concentration >^ 150 ug/m3 is shown in Figure 4.
22
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-------
5.2.1 Failing the Attainment Test for Sites Sampling Less Frequently
Than Once in Three Days
The probability of failing the attainment test is defined as the
probability of observing more than the allowable number of exceedances.
Typically, TSP monitoring sites sample on a once in 6 day schedule and thus
the number of TSP samples is usually less than 61 per year or 183 over a
3 year period. For these and other sites producing fewer than 348 observa-
tions in 1 to 3 years, the probability of failing the attainment test is
the probability of observing zero PMio exceedances over the sampling time
period (from Table 1). If p^ represents the PMio exceedance probability
for the ith TSP sample, then the probability, P0, of observing no allowable
exceedances is
PO " TT qi (3)
where qi = 1-pi (the probability that an observed TSP value,
TSP-j, does not correspond with a PMio value
greater than the level of the 24-hour PMio
standard), and
n = the number of TSP values greater than the level
of the 24-hour PMio standard.
The probability of failing the attainment test, based on no allowable
exceedances, is
PF(0) = 1-P0
24
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The formulas for the probability of exactly two exceedances, P2, and
exactly three exceedances, PS, are
1
P2 = ?[P1C1-P0C2], and (8)
J^
P3 = 3 [P2Ci-PiC2+PoC3L (9)
n £i r
where Cr = qi , r = 1, 2 or 3 (10)
The probability of failing the attainment test for two or three
allowable exceedances is
PF (2) = l-(P0+Pi+P2) (11)
Pp (3) = l-(P0+Pl+P2+P3) (12)
The computational form of equations 5, 6, 8, 9 and 10 follows from the
probability generating function of a Bernoulli process with variable
probabilities and have been derived elsewhere.(15,16)
5.3 Attainment Assessment Based on Some PMm Data
If 1 or 2 years of PMjQ data are available, then a 3-year attainment
test can still be applied, using TSP data for the remaining years. This
would be of interest if (1) the PMjo data did not meet the data requirements
specified by Appendix K to Part 50, and (2) there had been no significant
change in the emission sources contributing to PMjo and it is also felt
that the TSP data can be properly used to estimate the PMjQ situation. The
use of the additional data can have a stablizing effect on the expected
exceedance estimates and reduce the effect of anomalous meteorology.
28
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The probability of failing the attainment test with 1 allowable
exceedance is the probability of not observing 0 or 1 exceedances, and
is given by
PF(1) = l-(P0+Pl) (7)
Example 4
Suppose that the sampling rates were triple those of Example 3, but
the observed TSP concentrations greater than 150 pg/m3 were the same. With
180+150+150=480 TSP samples, Table 1 indicates that 1 exceedance would be
allowed. Using equation (5), the probability of observing 1 exceedance,
n f 2±
Pi - po E v 9i / > where P0 is the same as derived in Example 3
X
[ .82 + . 34 + ^ + _.J.6 + .03 ]
Thus, Pj = (0.07) [.18 .66 .70 .84 .97]
= (.07) (5.72)
= .40
The probability of failing the attainment test, given by equation (7) is
MD = I-(PO + PI)
= 1-(.07 + .40)
= .47
Thus, with more TSP data (compared to Example 3), the same observed high
TSP concentrations translated into a lower failure probability of .47,
i.e., 47% probability of nonattainment.
27
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The approach is based on the idea that the total number of exceedances
over the 3-year period is a sum of the PM^o exceedances estimated from the
observed PM^o data and the PM^o exceedances estimated from the TSP data.
With each year of actual PM^Q data, we can use observed exceedances to
estimate the annual number of PM^o exceedances. For the remaining years
for which there are TSP data, the expected exceedances are estimated using
the probability that the PMjo portion of each observed TSP value exceeds
the NAAQS. In this case, a site specific frequency distribution of ratios
should be used to develop a site specific version of Figure 4, providing a
statistically defensible site-specific frequency distribution is available.
Otherwise, the national distribution is used. The distribution should be
used to estimate the probabilities (i.e., the Pi ) for use in equation (3).
If a partial year of PM^o or OS data is available, then actual or
estimated PM^Q concentrations may be substituted for concurrent, collocated
TSP measurements. The PM^o exceedance probabilities would be 1.0 of the
PMjo measurement (rounded to the nearest 10 ug/m3, as specified by Appendix K)
was greater than the level of the standard and 0.0 otherwise.
The estimated PMjo exceedances derived from the actual PMjo data are
viewed as being fixed, while the estimated PMjg exceedances derived from
the TSP data are viewed as a random variable. Thus, the probability of
failing the attainment test can be defined solely in terms of the additional
exceedances estimated from the TSP data.
Equations 4 or 7 or 11 or 12 will again be used to estimate the
probability of failing the attainment test. For the case with a mixture
29
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of data, however, a revised number of allowable exceedances will be required.
This will account for the number of PM exceedances observed from actual PM data.
The revised number of allowable exceedances will be defined as
A' = A - E (13)
When PM and TSP are sampled with about the same frequency (i.e., using
the 3-year ranges in Table 1), then
A is the allowable number of exceedances based on the total
number of PM and TSP samples, and
E is the observed number of actual PM exceedances.
If E is greater than A, the attainment test is automatically failed and
probability calculations are not required. Specifically, for combined PM
and TSP sample sizes less than 347, no exceedances are allowed (Table 1).
Thus, 1 PMio exceedance causes a site to fail the attainment test. With
more than 347 samples, a single observed PM exceedance would be allowed.
The following example illustrates the calculations needed when PM^Q and TSP
have the same sampling rates.
Example 5
Suppose there are 180 PMio samples in 1982 showing 1 NAAQS exceedance
and 180 TSP samples collected annually during 1980-1981. Based on 540 PMio
plus TSP samples, the allowable number of exceedances, A, is equal to 1 (from
Table 1, Section 5.2). Since 1 actual PM^o NAAQS exceedance was observed,
the revised allowable number, A1, is 1-1 = 0. Therefore for this case,
failure of the attainment test is defined as the probability of observing 1
or more exceedances (equation 4).
30
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E = (1 x 180) / 360 = 0.5
Now A1 = 1.5 - 0.5 = 1.0 [from equation (13)]
In effect, with more PMiQ samples, we are applying less weight to the
single observed PMio NAAQS exceedance and thus the standard still allows
1 additional exceedance. An alternative way of explaining this is that
with low PM^o sampling rates there is a larger penalty for missing data.
When PMio and TSP sampling rates were both 180 samples per year, the single
PMio NAAQS exceedance carried more weight and no additional exceedances
would have been permitted during the TSP sampling time period.
32
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When PMio and TSP are sampled at different rates (based on the entries
of Table 1), then the allowable number of PMio NAAQS exceedances and the
observed number of exceedances must be adjusted to the TSP sampling rate.
To do this we must first adjust the PMiQ sample size and observed PMio
NAAQS exceedances according to the TSP sampling rate. After this step, the
revised number of allowable exceedances and the corresponding probability
calculations are determined.
Example 6
In the previous example, PMiQ was sampled 180 times in 1982, showing
1 exceedance, and TSP was sampled 180 times per year in 1979 and 1980.
Suppose that PMio was sampled 360 times in 1982 (instead of 180) and still
showed 1 exceedance. We will see that this has the effect of raising
the allowable number of observed exceedances. We will first adjust the
parameter "A" on the basis of the 3 years of data. Using the required TSP
sampling rate, the total number of observations in 3 years would be 540
(i.e., 3 x 180). The adjusted number of allowable exceedances, A, based
on the TSP sampling rate, is defined by the following equation.
A = 3.1 x 540 = 1.5
3 x 365
The number "3.1" is based on the number of allowable exceedances
with complete sampling in 3 years (before rounding). The observed number
of PMio exceedances, based on 360 samples in 1 year, must also be adjusted
to the TSP sample rate of 180 samples per year, so
31
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6.0 ESTIMATING SPATIAL EXTENT OF NONATTAINMENT SITUATIONS
6.1 Introduction
As described in earlier sections, assessing attainment/nonattainment
of the National Ambient Air Quality Standards (NAAQS) for PM^o requires the
use of ambient monitoring data. If the data and the assessment procedures
described earlier in coordination with EPA policy result in the requirement
for control strategy development, the question remains as to what is the
spatial extent of the nonattainment problem. Even though there will be no
designation of PM^o nonattainment areas, the extent of air quality violations
still must be determined for control strategy development. Defining the
spatial extent of the problem is not a simple, straightforward technical
matter, as is evidenced by the differences in the size of boundaries for
nonattainment areas for the other criteria pollutants and the original TSP
NAAQS. For example, some nonattainment area boundaries are county or city-
wide, some include entire townships or parishes, while others encompass the
central business district or an area bounded by designated streets.
Such differences occur because the size of the boundaries are
influenced by a variety of technical factors such as the pollutant itself,
its reactivity, type and density of emissions, meteorology, topography,
etc. In addition to these technical considerations, final boundaries are
also influenced by nontechnical factors such as the amount of time and
resources available to effectively define their limits, as well as the
jurisdictional borders of the areas surrounding the nonattainment
monitoring site.
33
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States have used several techniques, including dispersion modeling,
isopleth analysis, source receptor models, and monitoring site scales of
representativeness in defining nonattainment boundaries for other pollutants.
These techniques are also used for other purposes and are fairly complex
and detailed. Since they are not unique to nonattainment boundary defini-
tions, and are adequately described and discussed elsewhere in the literature,
they are not covered here in any great detail; rather, they are listed as
techniques or approaches that are recommended for use as guidance in defining
the extent of a nonattainment problem.
6.2 Use of Acceptable Air Quality Datj
The use of acceptable air quality data is required in determining the
attainment/nonattainment status of a monitoring site. In determining data
acceptability, three items which need to be evaluated are: the type of sampler
used, sampler location, and quality of the data.
6.2.1 Type of Sampler
When using TSP data for estimating the probability of
nonattainment for PMiQ, the TSP sampler must be a reference method, as
defined in Appendix B to 40 CFR Part 50. For those situations where inhal-
able particulate (IP) data will be used for estimating the probability of
nonattainment for PMiQ, the determination of the acceptability of the type
of IP sampler will have to be done on a case-by-case basis, as there is no
existing designated reference method for IP. Data collected from dichoto-
mous samplers used in EPA's national sampling network for inhalable
particulates are considered acceptable. As a general rule when using IP
data, the sampler should be similar to those used in the EPA IP network
34
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which were based on the principles of inertia! separation and filtration.
The Environmental Monitoring Systems Laboratory (EMSL), Research Triangle Park,
North Carolina, will provide guidance to assist in making this determination.
6.2.2 Sampler Location
Appendices D and E of 40 CFR 58 included network design and
siting criteria for TSP samplers and proposed criteria for PMjo samplers,
but not for IP. If TSP data are to be used in the assessment of attainment/
nonattainment for PM^o. then these samplers must conform to the requirements
of Appendices D and E.
6.2.3 Data Quality
The Agency's quality assurance policy is that all environmental
data generated, processed, or used for implementing Clean Air Act require-
ments, will be of known precision and accuracy and, to the extent possible,
be complete, comparable, and representative.
Consistent with this policy, TSP samplers must conform to the
reference method requirements and the data must be collected in accordance
with the quality assurance criteria contained in Appendix A of Part 58.
For IP data, the samplers must be similar to those used in the EPA national
sampling network for inhalable particulates, except that size-selective hi-
volume samples collected with glass fiber filters should not be used.
Minimum quality assurance activities that should have been conducted during
the IP measurement process are quality control checks, data review and
validation activities. The quality control activities include regularly
scheduled flow calibrations where the flow measurement devices used to
35
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measure sampling rate were also calibrated. Data review and validation
procedures should be similar to those established for the other criteria
pollutants.
6.3 Determining the Boundaries of a Nonattainment Area
As noted in Section 6.1, several techniques have been used by States
to define the spatial extent of NAAQS violations expressed as boundaries of
nonattainment areas. Basically, the approaches used can be placed into
three categories:
1. a qualitative analysis of the area of representativeness of
the monitoring site, together with consideration of terrain, meteorology
and sources of emissions;
2. spatial interpolation of air monitoring data;
3. air quality simulation by dispersion modeling.
In determining the extent of a PMiQ nonattainment situation, the use of
any one or a combination of the above categories would be considered accept-
able to the EPA. The choice of which technique to use depends on the
complexity of the RMjo problem area.
6.3.1 Qualitative Analysis
This approach, unlike the others discussed below, is not intended
to define any single analytical procedure for defining the extent of a non-
attainment problem. On the contrary, it is intended to recognize as acceptable
various approaches that consider such factors as ambient monitoring data,
the spatial scales of representativeness of the monitoring station, the number
36
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of areas in the community similar to that being measured by the monitoring
station, the type of terrain, meteorology, and sources of PM^o emissions.
Proposed revisions to Appendix D of Part 58 describe the topic of spatial
scales of repesentativeness for PMio stations, as well as procedures for
locating such stations. The predominant spatial scales for PMjo stations
include micro, middle and neighborhood, with a fewer number of stations
represented by the urban and regional scale. Properly located stations
that are specifically classified according to their spatial scale could, in
certain instances, be solely used to define the limits of the nonattainment
area. Other situations obviously will require a more detailed review and
analysis of sources, pollutant transport and receptor.
6.3.2 Spatial Interpolation of Air Monitoring Data
Although it would be desirable to ensure that the entire area of
a designated nonattainment area is actually nonattainment, air monitoring
costs are so high as to prohibit full coverage of a large nonattainment
area. There are, however, two methods available to arrive at refined
estimates of the spatial variation of air quality. One method is spatial
interpolation of air monitoring data, the other which will be discussed in
Section 6.3.3, is air quality simulation by dispersion modeling.
The use of spatial interpolation of air monitoring data is the
method most appropriate for situations in which monitors are located at
relatively close proximity to one another. Over the past years, most
cities and urban areas have established fairly dense air monitoring net-
works which enabled the technique to become more widely applicable. A
complete description of the method is described in the publication,
37
-------
"Guideline on Procedures for Constructing Air Pollution Isopleth Profiles
and Population Exposure Analysis," U.S. Environmental Protection Agency,
Office of Air Quality Planning and Standards, Research Triangle Park,
North Carolina 27711, EPA-450/2-77-024a, October 1977 (OAQPS No. 1.2-083).
The basic procedure involves the plotting of station locations
and measured concentrations from these stations. For those areas of the
map not covered by monitoring stations, a spatial interpolation scheme is
used to estimate air quality concentrations. The technique can be done
manually or through the use of computer mapping programs.
6.3.3 Air Quality Simulation by Dispersion Modeling
Determining the extent of the PM NAAQS nonattainment can also be
accomplished by using dispersion models to simulate the spatial distri-
bution of air quality under various conditions. Dispersion modeling is
more appropriate than spatial interpolation of air monitoring data in areas
where actual monitoring data are scarce. In order to use a dispersion
model, source data, air quality data, and meteorological data are required.
For dispersion modeling purposes, PM^g is treated as a nonreactive gas.
The type of source (point, area, mobile, or stationary), type of standard
(short term or annual), type of terrain (flat or rough), and the type of
area (urban or rural) will of course affect the decision as to which model
to use. The document, "Guideline on Air Quality Models," U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina 27711, OAQPS No.
1.2-080, April 1978, includes specific recommendations concerning air
quality models, and also describes circumstances for which models, data
and techniques other than those recommended in the guideline may be applied.
38
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7.0 ACKNOWLEDGEMENTS
The authors wish to acknowledge the contribution of Mr. Stanley Sleva
of the Monitoring and Reports Branch, OAQPS, who prepared Section 6.0
which discusses ways to estimate the size of nonattainment areas. Technical
review by Dr. Edwin L. Meyer of the Air Management Technology Branch, OAQPS,
Dr. William P. Smith of the Statistical Policy Staff of OPRM, Mr. Jack Suggs
of the EMSL, and Dr. William F. Biller is also greatly appreciated. Special
thanks is given to Mr. Roger Powell of the Control Programs Development
Division, OAQPS, for his ongoing responsibility and advice in ensuring the
consistency of this document with the overall regulatory effort. Finally,
the excellent typing and clerical support by Mrs. Carole Mask is greatly
appreciated.
39
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8.0 REFERENCES
1. U.S. Environmental Protection Agency, "National Primary and Secondary
Ambient Air Quality Standards. Appendix B - Reference Method for the
Determination of Suspended Particulates in the Atmosphere (high volume
method)," 40 CRF 50: 12-16, July 1, 1979.
2. R. W. Countant, "Effect of Environmental Variables on Collection of
Atmospheric Sulfate." Environmental Science and Technology 11:
873-878, 1977.
3. Hardial S. Chalal and David 0. Romano, "High Volume Sampling: Effect
of Windborne Parti cul ate Matter Deposited During Idle Periods."
Journal of the Air Pollution Control Association, Volume 26, No. 9,
pages 885-886, 1976.
4. A. R. McFarland and C. E. Rodes, "Characteristics of Aerosol Samplers
Used in Ambient Air Monitoring." Presented at 86th National Meeting,
American Institute of Chemical Engineers, Houston, Texas, April 2,
1979.
5. B. W. Loo, R. S. Adachi, C. P. Cork, F. S. Goulding, J. M. Jaklevic,
D. A. Landis, and W. L. Searles, "A Second Generation Dichotomous
Sampler for Large Scale Monitoring of Airborne Particulate Matter,"
LBL-8725, Lawrence Berkeley Laboratory, Berkeley, California,
January 1979.
6. J. B. Wedding, M. Weigand, W. John, and S. Wall, "Sampling Effectiveness
of the Inlet to the Dichotomous Sampler." Environmental Sciences and
Technology, l±: 1367-1370, 1980.
7. Kenneth Axetell, Jr. and Chatten Cowherd, Jr., Improved Emission
Factors for Fugitive Dust from Western Surface Coal Mining Sources.
Final Report to U.S. Environmental Protection Agency, Cincinnati,
Ohio by PEDCo Environmental under Contract Number 68-02-2924, Volume I,
page 4-1, July 1981.
8. Jack C. Suggs, Charles E. Rodes, E. Gardner Evans, and Ralph E.
Baumgardner, Inhalable Particulate Network Annual Report: Operation
and Data Summary (mass concentrations only), April 1979 - June 1980.
U.S. Environmental Protection Agency Report Number EPA-600/4-81-037,
Research Triangle Park, North Carolina, May 1981.
9. Memo from Barry Martin to T. G. Pace, "Limitations on the Use of
Inhalable Particulate Network Data 1979-82," October 25, 1982.
10. Thompson G. Pace, "Estimating PMiQ Concentrations from IP and TSP
Data," APCA Paper 82-45.2, Presented at Annual Meeting of the Air
Pollution Control Association, New Orleans, Louisiana, June 1982.
40
-------
11. Anthony D. Thrall and C. Shepard Burton, Characterizing Ratios of
Particulate Concentrations: A First Step in Assessing Likely
Attainment Status Under a PMip National Ambient Air Quality Standard
(Final Report), Systems Applications, Incorporated, Publication
No. 82316, April 4, 1983.
12. A. D. Thrall and A. B. Hudischewskyj, "An Update on the Use of
Particulate Ratios To Assess Likely PM^g Attainment Status," technical
memorandum, Systems Applications, Incorporated, January 13, 1984.
13. John G. Watson, Judith Chow and Jitindra Shah, Analysis of Inhalable
and Fine Particulate Matter Measurements. Final Report to U.S.
Environmental Protection Agency, Research Triangle Park, North Carolina,
by ER&T under Contract No. 68-02-2542, Task Order 6, pages 8-18,
December 1982.
14. Neil H. Frank and Thomas C. Curran, "Statistical Aspects of a 24-hour
National Ambient Air Qulaity Standard for Particulate Matter,"
Paper 82-23.8, 75th Annual APCA Conference, New Orleans, Louisiana,
June 1982.
15. W. Feller, An Introduction to Probability Theory and Its Application,
Volume I, 3rd Edition, John Wiley and Sons, 1968, page 282.
16. Memorandum from W. P. Smith to N. P. Ross, subject: "Recursive
Algorithms for Computing Compliance Probabilities," November 1, 1982.
41
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APPENDIX A
Monitoring Sites in the National
IP Monitoring Network Used in the Analysis
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APPENDIX B
Alternative Curves for Use With the Upper Range
Values Proposed by the Administrator.
(Annual arithmetic mean = 65 pg/m3, 24-hour NAAQS of 250 ug/m3
not expected to be exceeded more than once per year).
-------
For the upper range of proposed PMiQ NAAQS (250/55)
Figure A1 should replace Figure A in the text
Figure B1 should replace Figure B in the text
Figure 3' should replace Figure 3 in the text
Figure 4' should replace Figure 4 in the text
Numbers used in examples on pages 8-9, in example 3 on page 17, and in
example 4 on page 18 should be changed accordingly.
B-l
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