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

-------
            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

-------
     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

-------
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

-------

-------
     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

-------
     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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