Draft  Final  Report

    CHARACTERIZING VISIBILITY TRENDS:   A
    REVIEW OF HISTORICAL APPROACHES AND
    RECOMMENDATIONS FOR FUTURE ANALYSES

               SYSAPP-87/133

               September 1987

        EPA Contract No. 68-02-4373


                Prepared for

Office of Air Quality Planning and Standards
    U.S. Environmental  Protection Agency
   Research Triangle Park,  North Carolina
                 Prepared  by

                B. S. Austin
                L.  R.  Chinkin
                A.  K.  Pollack
                  M.  Moezzi
                C. S. Burton

         Systems Applications, Inc.
            101  Lucas Valley  Road
            San  Rafael,  CA 94903

                     And

                D. A. Latimer

               Gaia Associates
        Fort Cronkhite,  Building  1055
            Sausalito,  CA  94965

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                             ACKNOWLEDGEMENTS
The authors would like to express their appreciation to Neil Berg of the
U.S. Environmental Protection Agency for his guidance and helpful sugges-
tions during the project.  We also wish to thank several individuals who
provided their insight and knowledge about visibility.  We thank Marc
Pitchford of EPA Las Vegas, John Bachmann of OAQPS, William Wilson of ORD,
William Malm of the National Park Service, John Trijonis of Sante Fe
Research, Christine Sloane of General Motors, Scott Archer of the Colorado
Bureau.of Land Management, Dennis Haddow of the U.S. Forest Service, Haluk
Ozkaynak of Harvard University, Warren White'of Washington University,
John Molenar of Air Resource Specialists, and Chuck McDade of Combustion
Engineering.
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                            EXECUTIVE SUMMARY
The objective of this report is to review and recommend various approaches
for analyzing and presenting visibility trends to be included as part of
The U.S. Environmental Protection Agency (EPA) annual National Air Quality
and Emissions Trends Report.  Our review covers existing and potential
data sources for visibility trends analysis,  and parameters that have been
or could be used to describe such trends.  Our recommendations are based
on an extensive review of published visibility trend studies, a telephone
survey of individuals involved in developing  visiblity trends in the
United States, and a separate data analysis undertaken as part of this
study.  Specific areas addressed in the review and survey include alterna-
tive physical parameters for describing visibility; alternative sources of
data; statistical approaches for characterizing visibility frequency dis-
tributions; and methods of accounting for natural visibility impairment.
STUDY SUMMARY

Visibility Indicators are significantly different from the traditional air
quality indicators used in the Trends Report in that they report an air
quality effect rather than air quality itself.  This distinction is impor^
tant, both because air quality effects are not regulated under EPA stan-
dards and because they are more difficult to measure and interpret than is
air quality itself.  In choosing an indicator for visibility trends, it is
important to consider the relationship between the indicator and air
quality.

The two indicators that are used almost exclusively in visibility trends
studies are the extinction coefficient (bext) and visual range.  bext is a
measure of light-absorbing and scattering particles in the air and is
directly related to pollutant concentrations.  Its disadvantages as an
indicator are its inverse relationship with visibility and the lack of a
long-term data base of direct measurements.  It is also an unfamiliar
technical term to readers of the trends report.  Visual range is a measure
of both the air quality effect of a given pollutant concentration and of
unrelated factors such as the illumination of the atmosphere.  It is not a
direct measure of air quality.  The advantages of using visual range as an
indicator are that it is directly related to visibility, has a long-term

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data base, and is easily understood by the general public.  Using
assumptions about the just-discernable contrast, bext can be related
mathematically to visual range.   Other indicators such as contrast,
transmittance, or chromaticity may be appropriate for characterizing
vistas of special importance, such as those in national parks, but cannot
be used to characterize visibility in a concise and easy-to-understand
way.

There are only two potential data sources currently available for examin-
ing long-term trends in visibility.  The data base with the best spatial
and temporal coverage is the U.S. Weather Service airport network, which
has recorded hourly visual range observations at approximately 600 sites
throughout the United States since the early 1940s.  Use of this data for
visibility trends analysis requires careful consideration of factors such
as the availability of visibility markers past ten miles; inter-airport
variations in reporting practices and marker availability; and changes in
observers, locations, and marker types.  Almost all trends studies use
these data.  The next largest data base is the National Park Service (NPS)
monitoring network of 32 teleradiometer and camera sites in 28 western and
three eastern national parks.  Some of these sites have operated since
1978, but most have operated for shorter periods of time.  To date,
studies using these data have characterized current visibility conditions
rather than examining long-term trends.  Numerous other networks exist,
but they have fewer sites and/or have operated for much shorter periods of
time than has the NPS network.

Two new networks that may be important for future visibility trends
studies are the  IMPROVE network and the Eastern Fine Particle and Visi-
bility network (EFPVN).  The IMPROVE network will assess progress toward  -
the national visibility goal and establish background  levels of visibility
and will consist of 20 transmissometer/camera sites in Class I areas.  The
EFPVN will collect the information necessary to determine the correct
level of a secondary fine particle standard and will consist of 10 sites
equipped with fine particle samplers, nephelometers, and cameras.  The
suitability of either of these networks for future trends analysis is
questionable for several reasons:  (1) Neither has guaranteed funding after
an  initial 3-4 year period; (2) neither has a large number of sites to
adequately characterize visibility in the U.S.; and (3) with regard to the
IMPROVE network, there are general uncertainties  about transmissometer
data quality.

Visibility trends analysis has been performed using a  variety of summary
statistics such  as means, frequency of visibility in certain classes, per-
centiles, and ridits.  Our discussion of these  statistical methods centers
on  summaries of  airport data since they constitute the most extensive data
base available.  Choosing an appropriate summary  statistic for these data
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is problematic because many airport visibility observations are underesti-
mates of true visibility.   For example, an observation of 20 miles may
mean that the visibility marker at 20 miles can be seen and that the next
marker (for example, a radio tower 30 miles away) cannot.  The true visi-
bility may be 25 miles and not 20.  This factor makes it difficult to use
means for visibility trends since the presence of such underestimates will
lend a downward bias to the results.  Another statistical technique, the
flux method, reports the percentage of time that visibility falls within
distance categories such as > 20 and < 30 miles.  This method was used
extensively in early visibility trends studies.  Its advantage is that it
summarizes exactly what the data report.  However, it uses summary
statistics that are unitless; thus actual trends in visual range across
time are not reported as they would be by the means or median.  The
percentile approach reports trends in visibility equaled or exceeded some
percentage of the time.  (For example, the 90th percentile visibility
would be the distance exceeded in only 10 percent of the observations) and
is an extremely useful way of characterizing the entire visibility
frequency distribution.  Its principal disadvantage is that interpolation
is often required to derive the percentiles of interest.  Many current
visibility trends studies use percentiles.  Other approaches, such as
ridit analysis, have been used primarily to validate other methods.
SUMMARY OF RECOMMENDATIONS

For the purpose of the Trends Report, it is desirable to distinguish visi-
bility trends associated with anthropogenic factors from those associated
primarily with natural factors such as rain, snow, fog, or windblown
dust.  A number of screening techniques have been used to eliminate such
factors from the data.  The least controversial is to remove hours with
precipitation from the data base, since precipitation creates visibility
impairment that is not related to pollutant concentrations.  Removing fog
is more complicated because it is not always easy to distinguish natural
fog from anthropogenic haze.  Since natural fog is unlikely when humidity
is less than 80 to 90 percent, several techniques eliminate hours when
humidity exceeds 80 percent.  A problem associated with these techniques
is that high humidities can affect the visibility reduction caused by a
given concentration of particles; thus it may not be desirable to exclude
these hours from the data.  On the basis of our literature review and a
prototype analysis (Section 6), we recommend removing hours with both fog
and relative humidity greater than 85 percent from the data.

We performed a detailed prototype analyis of data from St. Louis to pro-
vide a further basis for our recommendations.  In this analysis, we
examined the selection of hours to be included in the data, the selection
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of visibility categories for use in flux analysis, and the effect of tech-
niques used for incorporating meteorological variability.  It may not be
desirable to use all daytime hours of data in the trends analysis because
certain hours of the day (such as early morning) often experience natural
visibility impairment not representative of visibility during the rest of
the day.  The choice of flux categories can also affect the estimated
visibility trend.  Our analysis indicates that the visibility trend was
not appreciably altered by using data for only one hour per day
(noontime), or by excluding observations for hours with precipitation or
fog when humidity was greater than 85 percent.  We also found that it is
necessary to choose different flux categories for each individual station
because there are large station-to-station differences both in reported
values and reporting practices.

Our recommendations for the current Trends Report are to use the NWS air-
port data since no other data base approaches its geographic extent and
long period of record.  Although there are advantages and disadvantages
associated with the use of any indicator, we recommend using visual range
as an indicator because this indicator will make the visibility portion of
the Trends Report useful to the largest number of people.  At this time,
we are recommending both flux and percentile statistical summaries, to
determine whether both methods show the same general trends.  If they do,
we will recommend box plots of percentiles that are consistent with data
displays currently presented in the Trends Report.  We recommend examining
trends in 10 cities chosen in conjunction with the EPA Project Officer for
a 15-year time period.  This time period is greater than that currently
used in the Trends Report, but many visibility researchers believe that 5
or 10 years is not long enough to distinguish visibility trends from
general meteorological variability.  We do not recommend a longer period
because of a major change in visibility reporting practices in 1970.  We
recommend using noontime observations and screening the data for
precipitation and fog when relative humidity is greater than 85 percent,
and when windblown dust is recorded.
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                                 CONTENTS

Acknowledgments	
Executive Summary	
1    INTRODUCTION	     1
          Study Background	     1
          Objectives of a Visibility Trend Analysis	     3
          Report Structure	  '   4
2    ALTERNATIVE PARAMETERS FOR EVALUATING VISIBILITY TRENDS	     5
          Visibility Parameters	     6
          Relationships Among Visibility Parameters.....	     7
          Parameters Used in Trends Studies	     9
3    SOURCES OF DATA FOR VISIBILITY TRENDS	    14
          United States Weather Service Airport Visibility
          Observations	    14
          Instrument-Based Visibility Data	    21
          The Improve Network	    23
          The Eastern Fine Particle Network	    28
4    ALTERNATIVE APPROACHES FOR CHARACTERIZING TRENDS
     IN VISIBILITY	    30
          Describing Visibility Trends with Means	    30
          The Flux Method	    31
          The Percentile Approach	    33
          Ridit Analysis	    38
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5    ALTERNATIVE METHODOLOGIES FOR INCORPORATING METEOROLOGICAL
     VARIABILITY IN VISIBILITY TREND ANALYSIS	    39
          Selection of Hours	    39
          Use of Running Averages to Smooth Data	    40
          Elimination of Hours Subject to Natural
          Visibility Impairment	    40
          Treatment of Meteorological Variability by
          Other Researchers	    41
6    PROTOTYPE ANALYSIS	    43
          Choice of Hours	    43
          Choice of Visibility Categories for Flux Analysis	    45
          Incorporation of Meteorological Variability	    49
7    CONCLUSIONS AND RECOMMENDATIONS	    53
          Recommended Data Base	    53
          Recommended Visibility Indicator	    53
          Calculation and Display of Trend Statistics	    54
          Geographical Areas Recommended for Study	    54
          Recommended Temporal Features of Trend Analysis	    55
          Recommendations for Meteorological Screening Procedures...    57
          Minimum Data Requirements	    57
References	    59
Appendix:  SELECTED INSTRUMENT-BASED VISIBILITY AND
           FINE PARTICLE MONITORING NETWORKS
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                             1   INTRODUCTION
The objective of this report 1s to review and recommend various approaches
for analyzing and presenting visibility trends to be Included as part of
the U.S. Environmental Protection Agency's annual National Air Quality and
Emissions Trends Report.  Our review covers existing and potential data
sources used for visibility trends analysis and parameters that have been
or could be used to describe trends.  Our recommendations are based on a
literature review, a telephone survey of individuals involved in develop-
ing visibility trends in the United States, and a separate data analysis
undertaken as part of this study.  Specific areas- addressed in the review
and survey include alternative physical parameters for describing visi-
bility; alternative sources of data; statistical approaches for charac-
terizing visibility frequency distributions; and methods of accounting for
natural visibility impairment.
STUDY BACKGROUND

The U.S. Environmental Protection Agency (EPA) 1s considering adding an
atmospheric visibility component to its annual National Air Quality and
Emissions Trends Report, which is directed toward policy makers and the
general public, and which contains summary statistics for atmospheric con-
centrations of total suspended particulate matter, sulfur dioxide, carbon
monoxide, nitrogen dioxide, ozone, and lead.  The key objective of the
trends report is to present statistics that describe current air quality,
particularly as it relates to National Ambient Air Quality Standards
(NAAQS) and trends during 5- or 10-year periods.  The report compiles
information from hundreds of monitoring sites into a highly condensed for-
mat.  Specifically, the annual trends report is currently designed to pro-
vide information concerning

     (1)  Trends in air pollutant concentrations (i.e., whether air pollu-
          tion 1s deteriorating, improving, or staying the same);

     (2)  Whether NAAQS are being met or exceeded, and if exceeded, the
          average number of exceedances per year;
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     (3)   Geographical  variability in air quality;

     (4)   Long-term average air quality as well  as  episodic, short-term
          averages for  periods of high (peak)  pollutant concentrations;

     (5)   Disaggregated data for key urban areas and

     (6)   Emission trends summaries used for comparison with air quality
          trends.

The EPA's legislative responsibility for visibility is outlined in a num-
ber of sections of the  1977 Clean Air Act Amendments, particularly sec-
tions 109, 169A, and 165.  Section 109 discusses air quality standards
applicable to the entire United States; the latter two sections discuss
issues applicable to pristine areas such as national parks.  The inclusion
of visibility in the Trends Report could function as an indicator of pro-
gress toward achieving  goals set forth in these sections.  Section 109
requires the EPA to set primary (health-based) and secondary (welfare-
based) standards for air pollutants.  Visibility is considered to be an
important welfare criteria.  Since visibility impairment is primarily
influenced by concentrations of fine particles (less than 2.5 ym) in the
air, the EPA has recently initiated rulemaking for a secondary fine par-
ticle standard based largely on fine particle visibility effects  (Federal
Register July 1, 1987).

Section 169A establishes as a national goal "the prevention of any future
and remedying of any existing, impairment of visibility in mandatory Class
I federal areas where such impairment results from man-made air pollu-
tion."  Section 165 requires the states to include measures in their
implementation plans (to achieve federal air quality requirements) to
"prevent the significant deterioration" of air quality in areas with air
quality better than the applicable national standard."  These measures
have historically taken the form of emission controls on new point sources
that could degrade air quality in these areas over some agreed-upon
increment.

Most anthropogenic visibility impairment is a result of particles smaller
than 2.5 micrometers in diameter (fine particles).  Because of the rela-
tionship between visibility and fine particle concentrations, visibility
can serve as a surrogate for fine particles as well as an indicator of the
visual effect of certain kinds of air pollution per se.  In fact, visibil-
ity is the public's primary basis for assessing pollutant levels.  How-
ever, natural factors such as rain, snow, sleet, fog, windblown dust,  and
forest fires also impair visibility.  Furthermore, a given concentration
of fine particles can cause different visibility effects, depending on the
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relative humidity.   Thus,  care  must be exercised in interpreting visi-
bility trends to distinguish between trends in meteorological  or climatic
factors and trends  in anthropogenic visibility impairment associated with
fine particles.
OBJECTIVES OF A VISIBILITY TREND ANALYSIS

The selection of the methods to be used in EPA visibility trend analysis
hinges on the specific applications served by the Trends Report.  Visi-
bility trends may be designed for the following specific objectives:

     PSD Class I visibility.  The visibility trends report could be
     designed to relate specifically to the national goal expressed in
     Sections 165 and 169A of the Clean Air Act of preventing future and
     remedying existing visibility impairment in PSD Class I areas.  Such
     trends reporting would be specific to those national parks and wil-
     derness areas (i.e., Class I areas) that are afforded visibility
     protection by the Act.

     Urban visibility.  Since most Americans reside in urban areas, the
     visibility conditions found in these areas best characterize the
     typical, daily experience of the average American.  Thus, urban area
     visibility could form the basis for exploring visibility trends.

     Rural (nonurban) visibility.  If the objective is to characterize the
     visibility patterns of the land mass composing the United States,
     rural visibility would be most representative because urban areas and
     affected areas downwind of urban centers make up a relatively small  ..
     fraction of the U.S. land area.

     A combination of these objectives.  It may be appropriate to focus on
     a combination of these objectives.  If so, it might be necessary to
     include a separate section in the Trends Report for each objective
     since there are fundamental differences in visibility, and in the
     importance of visibility, in each of these regions.  For example, for
     reporting trends in Class I areas, the most interesting variable
     might be the number of days with exceptionally clear visibility,
     whereas in urban areas it might be the number of days with exception-
     ally bad visibility.  Thus, the selection of specific data, indica-
     tors, and statistical parameters for visibility trends analysis and
     reporting should be based on a careful consideration of the specific
     uses of the visibility trends data.
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REPORT STRUCTURE

Our review of the various alternatives and components of visibility trends
analysis is structured in the following manner:  Section 2 discusses
alternative parameters for evaluating visibility trends.  Section 3 dis-
cusses available and potential sources of data for visibility trends anal-
ysis.  Section 4 discusses alternative statistical methods for charac-
terizing visibility trends.  Section 5 reviews the various methodologies
that could be used to treat meteorological variability in such analyses.
A prototype analysis is presented in Section 6; Section 7 contains con-
clusions and recommendations.
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           ALTERNATIVE PARAMETERS FOR EVALUATING VISIBILITY TRENDS
EPA trends reports  have traditionally presented air quality trends that
are defined in terms of the composition of the atmosphere (e.g., Pb con-
centration levels).   In no case of which we are aware have trends in air
quality effects (e.g.,  Pb levels in blood) been presented in these
reports.  This distinction is important for three reasons: (1)  Tradi-
tionally, it is air quality that is regulated through ambient standards
that specify (among other things)  permissable levels of atmospheric con-
stituents after comprehensive consideration of their effects; (2).air
quality can be measured to a desired degree of accuracy and precision but,
because of numerous complicating factors, air quality effects cannot (for
a cost comparable to that for measuring air quality); and (3) it is con-
siderably less difficult to interpret air quality measurements than to
interpret measurements  of air quality effects.

Visibility is an air quality effect.  Its accurate measurement depends on
numerous complicating factors involving how the atmosphere and, in turn,
an object or scene, is  illuminated and how the composition of the atmo-
sphere varies with distance between the viewer and objects of interest.
Interpretation of visibility measurements is made difficult by these fac-
tors and also by gaps in our understanding of the factors influencing
human visual perception.  Visibility can be characterized by a number of
physical parameters such as fine particle concentration, extinction,
visual range, contrast  transmittance, delta E, modulation transfer func-
tion, and blue-red ratio.  The fine particle concentration is an atmo-
spheric constituent that is closely related to visibility whereas extinc-
tion (or atmospheric transmittance for a sight path) is an air quality
effect that is closely  related to air quality; however, these relation-
ships, which are well understood in principle, are difficult to treat in
practice (Sloane and White, 1986; White, 1986).

Parameters such as visual range, contrast, contrast transmittance, delta
E, color contrast, modulation transfer function, or blue-red ratio depend
on the illumination of  the atmosphere and are poorly related to air
quality; thus it is unlikely that they would form the basis of regulation,
especially if traditional precepts in air quality management are fol-
lowed.  In this section, we discuss alternative physical parameters that
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could be used as Indicators  of  visibility,  their mathematical  relation-
ships, and the assumptions governing those  relationships.  A summary of
parameters used in past trends  studies is also included.
VISIBILITY PARAMETERS

The parameter most people are familiar with is visual range, i.e., how far
we can see.  Visual range has traditionally been defined in terms of the
maximum distance from which a dark object can be seen against the horizon
sky.  However, when this definition is examined more closely, the actual
visual experience is not so simple.  In a valley just before sunset, for
example, the visual effect of haze is quite different in different direc-
tions.  When looking toward the sun, the hills appear to be silhouettes
with no detail; when looking away from the sun, all the rocks, trees, gul-
lies, and other details on the hills are clearly discernible though the
visual range is the same in both directions.  This example suggests the
importance of sun angle to visibility.  The amount of detail that can be
discerned on the hills corresponds to the contrast parameter.  Another
parameter, the extinction coefficient, can be derived from visual range
and the "just-noticeable" contrast.  This derivation requires a number of
assumptions regarding the background sky radiance, and the uniformity of
particulate concentrations in the atmosphere between the observer and the
object.  These three parameters—visual range, extinction, and contrast—
are most often considered to be good visibility indicators.
Visual Range (rv)

The visual range, or "visibility," is the observation made at U.S. Weather
Service monitoring sites in units of miles or kilometers.  This parameter
is one the public readily understands because of everyday experiences and
its use in weather forecasts.  Visual range is highest in conjunction with
the lowest levels of air pollution, so it is inversely related to the
atmospheric concentrations used in traditional trends reporting.
 Light Extinction Coefficient (bext)

 This parameter is a measure of the concentration of  light-scattering and
 absorbing species in the atmosphere.  Its units are  inverse distance
 (e.g., m  , km" , Mm  ).  The extinction coefficient  is the equivalent
 light-scattering cross section (area) per unit volume of atmosphere, hence
 units of square meter per cubic meter or m^/m^ = m"*.  Since  it  is  similar
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to a concentration,  with increasing  values  corresponding to increasing
pollution, its values can be directly  compared with the composition of the
atmosphere (e.g., fine particle  concentrations).   Light extinction is
inversely related to visual  range.
Contrast (C)

Contrast can also be used as a parameter.  ' The contrast of a marker with
the horizon sky is what the U.S.  Weather Service observer responds to when
making visual range observations.  Contrast is also the parameter measured
by a teleradiometer, from which other parameters can be calculated.  How-
ever, its value depends on the intrinsic contrast of the marker or target
and the distance to it; thus, it is not a direct indicator of atmospheric
conditions.  It is specific to the particular line of sight used, unless a
standard target and distance is used.  Increasing contrast implies
increasing visual range and decreasing extinction, and therefore decreas-
ing atmospheric particulate concentration.
Other Parameters

Other much more sophisticated parameters have been used to characterize
color and psychophysical conditions closely related to visual percep-
tion.  These parameters include transmittance, chromaticity, luminance,
AE, modulation transfer function, and jnd.  Their calculation requires
very sophisticated and extensive measurements of the spectral properties
of several lines of sight.  Such parameters may be appropriate for charac-
terizing vistas of special importance such as those in national parks, but
they cannot be used to characterize visibility in a concise and easy-to-
understand way.
RELATIONSHIPS AMONG VISIBILITY PARAMETERS

Visibility impairment can be defined as the loss of the contrast of
objects viewed through the atmosphere.  The contrast, C, of a landscape
feature is the difference in light intensity of that feature when compared
to its viewing background:
where IQ and Ib are the spectral radiance (light intensities) of the  land-
scape object and the background, respectively.
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The contrast of a landscape feature decreases as a function of the dis-
tance between the feature and the observer in an exponential manner that
can be described by the Lambert-Beer law:

                        C(r)  = C(0) exp(-bext r)  .                   (2)

where C(0) and C(r) are the contrasts of the landscape feature at distan-
ces 0 and r (also called the intrinsic or inherent contrast and the
apparent contrast, respectively), bgxt is the average light extinction
coefficient of the atmosphere between the observer and the landscape fea-
ture, and r is the distance between the observer and the landscape fea-
ture.

For black objects, C(0) = -1 and we can simplify Equation 2 as follows:

                          C(r) = - exp(-bext r)  .                     (3)

The visual range distance (ry) is defined as the distance at which C(r) is
the just-noticeable contrast or at the perceptibility threshold.  If we
define this contrast as Cmi-n, inserting these values into Equation 3 and
solving for rv, we have the following equation (known as the Koschmieder
equation):
Traditionally, Cmin has been taken to be 0.02 (2 percent contrast) and has
been used as the basis for the most widely quoted version of the Kosch-
mieder equation:

                    rv = -ln(0.02)/bext = 3.912/bext  .                (5)

This commonly used formula for visual range (called the meteorological
range) is based on the following assumptions (Middleton, 1952):

     (1)  The background sky radiance at the observer is similar to  that
          at the observed object location;

     (2)  The distribution of the light-absorbing and -scattering  con-
          stituents of the atmosphere is uniform over the line of  sight
          between the object and the observer; and

     (3)  The observed object is black  (thus having a C(0)  = -1);  and

     (4)  The threshold, or just-noticeable contrast, is 0.02.
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The last two assumptions are rarely valid, whereas the first two may or
may not be valid, depending on specific circumstances.  Most viewed land-
scape features are not black, and intrinsic contrasts for typical dark
viewed objects range from -0.7 to -0.9 (Malm et a!., 1982).

Field studies have suggested that a threshold contrast of 0.05 may be more
appropriate for normal viewing situations.  Inserting a value of 0.05 for
Cm^n into equation (4) yields

                     ry = -ln(0.05)/bext = 3.0/bext  .                 (6)

This formulation may be the most appropriate for relating U.S. Weather
Service observations of visual range (rv) to light extinction coefficients
(bext).  However, other studies (Ozkaynak, 1985; Dzubay et. al, 1982) have
suggested that Koschmieder constants in the range of 1.63 - 1.80 (instead
of 3.9) might be most appropriate, at least for urban areas.  This range
would correspond to a contrast threshold of approximately 0.17.
PARAMETERS USED IN TRENDS STUDIES

Table 2-1 summarizes 30 visibility trend studies reviewed as part of this
study.  It is not a comprehensive list of all trends studies.  The trends
studies we reviewed all used either visual range or extinction coef-
ficients to describe visibility.  Different advantages and disadvantages
are associated with the use of either parameter.  Most people readily
understand the meaning of visual range, but it is inversely related to
atmospheric concentrations used elsewhere in the annual trends report.
The terms extinction coefficient is less familiar, but can be thought of
as a concentration of light-scattering and -absorbing species; it is
directly related to fine particle concentrations.

The reasons why trends studies do not use indicators other than visual
range and/or extinction coefficients are straightforward.  First, as will
be discussed in Section 3, there is no long-term data.  Collecting data
for specialized parameters such as chromaticity or transmittance can be
extremely expensive.  Also, these parameters are not easily summarized.

Which of these parameters should be used as a visibility indicator in a
trends report?  Tradition suggests that fine particle or extinction mea-
surements would be preferable; fine particle concentrations are a measure
of air quality, and extinction is a measure of an air quality effect that
is closely related to air quality.  A more intuitive measure such as
visual range might be less preferable.  However, as discussed in Section
3, there are no fine particle concentration or extinction records of suf-
ficient scope (duration or spatial coverage) to use either of these
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TABLE 2-1.  Visibility trend studies.
Reference
Trljonls and
Shapland (1979)
Husar and
Patterson (1984)
Husar and
Patterson (1986)
Husar et al.
(1979)
Husar et al .
(1981)
(synopsis paper)
Trljonls and
Yuan (1978)
Sloane (1980)
Vlnzanl and
Lamb (1986)
Holzworth and
Maya (I960)
Miller (1972)
Data Base
94 airports
137 airports
137 airports
70 airports
70 airports,
~ 30 turbidity
monitors;
1 pyrhello-
meter
100 airports;
6 sites from
NASN parti cu-
late network
15 airports
8 airports
2 airports
3 airports
Time Period
Analyzed
1974-1976
1948-1983
1948-1983
1948-1978
1960-1974
1916-1980
1974-1976
1948-1978
1949-1980
193S-1957
1962-1969
Area
Entire Onited
States
Entire United
States
Entire United
States
Eastern United
States
Eastern United
States
Northeastern,
eastern United
States
Mideastern
United States
Upper mid-
western United
States (Illinois
region)
Central and
southern
California
i
Ohio, Kentucky,
and Tennessee
Temporal Features
1n Analysis
Annual and 3rd
quarter analysis,
noontime observations
Seasonal analysis
Noontime observa-
tions, calendar
quarterly analysis
Seasonal analysis,
noontime observations
Dally, weekly,
seasonal . 10 year
analysis
Annual , seasonal
analysis; dally aver-
age of four daylight
observations
Daylight observations
at 10 a.m., 1 p.m.,
and 4 p.m. Seasonal
analysis
Seasonal and annual
analysis
Daytime observations
in May, July, Septem-
ber, and November
Summer daylight
observations
Parameters
Median midday visual
range
50th percentlle bext
25th, 50th, 75th, 90th
percentlle bext
Median midday bext
Mean bext, average
turbidity coefficient
10th, 50th, 90th percen-
tlle visual range
60th, 90th cumulative
percent lies visibility
In miles
Visual range 60th,
90th percent lies, mean
Mdits
Percent frequencies of
visibility between 0-10,
11-19," 20-30, > 30 miles
Frequency of visual range
< 6 miles and > 6 miles
Metorologlcal
Adjustments and Other
No data eliminated
RH, rain, snow, and fog
adjustments
Performed analysis with (1) no
data el mi na ted (2) precipitation
and fog eliminated (3) precipita-
tion and fog eliminated, rh adjusted
l%-2% of data discarded due to
heavy rain, snow, or fog
No data eliminated
No data eliminated
Rain and RH > 90% eliminated
Performed analysis with (1) no
data eliminated (2) precipitation
and RH > 90% eliminated
Data eliminated for hours
with precipitation and/or
RH > 90%
Performed analysis with (1) no
data eliminated (2) precipitation
and RH > 90% eliminated
                                                                                                                                                      continued

-------
TABLE 2-1.  Continued
Reference
Halm and
Molenar (1984)
Tombach et al .
(1987)
John Mulr
Institute (1983)
Malm et al . (1981)
4
4
Latlmer et al.
(1978)
Trljonls and
Yuan (1979)
Craig and
Faulkenberry (1979)
C-b 011 Shale
Venture (1977)
Roberts et al .
(1975)
Ursenbach
et al. (1978)
Time Period
Data Base Analyzed
27 NPS sites 1978-1982
In western
parks; tele-
radiometers
11 sites; 1981-1982
teleradlometers,
nephelometers
25 NPS Sites; 1978-1981
teleradlometers
13 NPS sites; 1978-1979
teleradlo-
meters
16 airports 1948-1970
17 airports 1948-1976
3 airports 1950-1974
Photometers NA
Photometers NA
Photometers NA
Area
Western United
States
Western United
States
Western United
States
Southwestern
United
States
Western United
States
Southwestern
United States
Oregon
Northwestern
Colorado
Arizona
Utah
i
Temporal Features
1n Analysis
Dally and seasonal
analysis
Annual and seasonal
analysis
Monthly analysis
Observations at
9 a.m., noon, 3 p.m.;
seasonal averages
Seasonal , annual
analysis;
daylight observations
3-year moving aver-
ages; observations
at 8 a.m., 11 a.m.,
2 p.m., and 5 p.m.
Daytime observations;
seasonal analysis
NA
NA
NA
Parameters
Median SVR calculated
from radiance
10th, median, 90th percentlle,
geometric mean, visual range
Geometric mean, standard visual
range
Cumulative frequencies
of standard visual
range
Percent of time visi-
bility exceeds various
values
10th, 50th, and 90th per-
centlle visual range
Mean rldlts
10th percentlle, median
visual range
10th percentlle,
median visual range
Median visual range
Meteorological
Adjustments and Other
Data validation procedures to
determine accuracy of measure-
ments, based on meteorological
conditions and consistency checks


Radiance measured at 405,
450, 550. and 630 ran for
5-6 vistas 1n each park
Data for hours with fog and
precipitation eliminated
No data elimination mentioned
Data for hours with precipita-
tion or RH > 90% eliminated



                                                                                                                                                     continued
         87iiO  2

-------
TABLE 2-1.  Concluded
Reference
Tombach and
Chan (1977)
Pueschel et al .
(1978)
Trljonls (1982)
Duckworth and
Klnney (1978,
1983)
Nelburyer (1955)
Espey, Huston, and
Associates, Inc.
(1984)
Zlmmerroann and
Wrlyht (1985)
Kelso, Weiss, and
Waggoner (1984)
Roberts et al .
(1974)
Green and Battan
(1967)
Kelso (1981)
Data Base
Nephelometers
Nephelometers,
photometers
67 airports
1n' California;
19 studied
A1 rport
sites In
California
NA
16 airports
2 airports
Nephelometers
Photometers:
black and
white photos
(densltometry)
2 airports
Nephelometers
Time Period
Analyzed
NA
NA
1974-1976
1949-1976
1958-1977
NA
1971-1972
1976-1977
1981-1982
1960-1985
1979-1982
1973-1974
1949-1965
1975-1980
Area
Utah
Utah
California
California
Los Angeles
Texas
Dallas and
El Paso, Texas
China Lake,
California
Petrified
Forest,
Arizona
Arizona
China Lake, i
California
Temporal Features
In Analysis
NA
NA
Observations at
1 p.m.; seasonal
and annual analysis;
3-year moving averages
Noontime observa-
tions; summertime
and annual analysis

Annual and seasonal
analysis; noontime
observations
Annual analysis;
noontime
observations
Monthly average of
1-hour averages
Annual and seasonal
analysis
Observations at
2 daytime hours
Annual and monthly
analysis
Meteorological
Parameters Adjustments and Other
10th percentlle, median
visual range
Median visual range
10th, median, 90th No data eliminated
percentlle visual range
Mean percent occurrence Data with RH > 70% eliminated
of adverse noontime
visibility over a 3-
year period

Median, 80th percentlle, mode. No data eliminated
from noontime visual range
80th percentlle, days with Data for hours with fog and
visual range > 15 and > 50 precipitation eliminated
miles
Mean bscat No data eliminated
Mean, median, 95th percentlle; No data eliminated
visual range
Frequency of times per year No data eliminated
visibility Is < 15 miles and
< 30 miles
Mean, median, 10th, 90th No data eliminated
percentiles, bscat
NA = not available.

-------
parameters for trends analysis,  whereas there are sufficient records of
visual range.   Visual range is also most closely related to people's
everyday experience.   Thus, for  the objectives of the EPA trends report,
visual range may be the best indicator of visibility trends.

For these reasons, trends studies use visual  range and/or extinction as
indicators, often presenting graphical results with visual range on one
side of the y-axis, and extinction on the other (for example, see Husar,
1979 and 1981).
87110 9                           13

-------
                    SOURCES OF DATA FOR VISIBILITY TRENDS
In this section,  we discuss the data sources available for analysis of
visibility trends.   The two primary sources are U.S. Weather Service visi-
bility observations made at hundreds of airports since the 1940s and the
National Park Service (NPS) visibility monitoring network of approximately
31 stations operating since 1978.   We describe these two networks in
detail, focusing  on their past and potential use in visibility trends
analysis.  Two other networks that may also be of importance in the future
are the IMPROVE network and Eastern Fine Particle Visibility Network
(EFPVN).  We describe these networks at the end of this section.  Repre-
sentative long-term (over one year) visibility and fine particle monitor-
ing networks in the United States  are presented in Table 3-1.  A brief
discussion of selected monitoring  networks that are limited in geographi-
cal and/or temporal scope, but that have collected important data is
presented in the Appendix.

As Table 3-1 illustrates, the sources of visibility data available.for
analyzing and reporting trends for the entire country are limited.  The
visibility data base with the best spatial and temporal coverage is the
network of meteorological measurement sites operated by the U.S. Weather
Service, primarily at airports.  There are approximately 600 airport vis--
ibility measurement sites throughout the United States.  Visual range and
meteorological observations at these weather stations are made hourly.
The National Park Service and other instrumented networks measuring visi-
bility-related parameters provide  much more limited spatial and temporal
coverage; none of them covers the  entire country and none has operated as
long the ten years generally considered to be a rough minimum time period
for a visibility trend analysis (Malm 1987a; Pitchford, 1987; Molenar,
1987; Middleton, 1987; Latimer, 1987).
UNITED STATES WEATHER SERVICE AIRPORT VISIBILITY OBSERVATIONS

Airport visual range estimates are made primarily for aircraft-related
purposes and are therefore much more detailed and accurate in the 0-7 mile
range than above 7 miles.  For lower visibility readings, visibility
obstructions such as precipitation, fog, or windblown dust must be
reported.  This focus on the lower range visibility results in several
 87110  10                          14

-------
TABLE 3-1.   Visibility  and fine particle monitoring in the United States.
Study Name
NWS Airport Data
EPA-SAROAD
System
EPA IPMN
NASN
'EPA-WPCS
EPA-ORV
'EPA-RAPS
IMPROVE
Eastern Fine
Particle Network
NPS Regional
Network
NPS PMN
SCENES
EPRI-SURE
Data Record
1948-present
1972-present
1978-1984
1956-1975
7/79-9/81
1980-1981
1975-1977
Mostly 1987
To begin
fall, 1987
1978-present
1982-p resent
11/83-12/88
8/77-6/79
No. of Sites Measurements
Approx. 600 Prevailing
visibility
TSP = ~ 2500 S02, N02, 03,
S02 • ~ 530 TSP, CO, Pb
163 Fine, coarse,
inhalable
particles;
sulfate
66 TSP, SOX
40 Fine, coarse
particles;
chem-analysis
3 Fine particles
20 Fine, coarse
particles
20 Fine, coarse
particles;
radiance;
visual range
10 bscat» visual
range, fine
particle mass
31 Radiance,
visual range,
fine particles
Fine particles
6 Total , coarse,
fine particles;
aerosol chemical
composition
56 S02, NO/NOX, 03;
total, fine,
Inhalable
particles
Instruments
Human observation
Various
analyzers and
samplers
Filter media
Particle samplers
Particle samplers
Filter media
Particle samplers,
nephelometers
Cameras, teleradio-
meters, fine
particle samplers,
transmissometers
Fine particle
samplers, nephelo-
meters, and cameras
Teleradiometers,
cameras, particle
samplers
Particle samplers
Teleradiometers,
parti cul ate
monitors, cameras
Various
analyzers and
samplers
Frequency
of Measurements
Hourly
Varied
24-hr average,
every 6 days
Every 6 days
72 hr
24 hr
every 6 days
Every 3 days,
continuously
Hourly and 3
times per day
10 a.m. and 2 p.m.
Camera--5-6
times daily;
nephelometers
continuously
Every 3 days,
continuously

24 hr
Continuously and
4 times per day
Area
Urban and
suburban, entin
United States'
Urban
Many SAROAD
sites
Urban
Urban
Ohio River
Valley Study
St. Louis area
Class I areas
Eastern urban
and suburban
areas
28 western
and 3 eastern
Class I areas
Eastern Class
I areas
Western
United States
47 urban and
9 Class I sites
                                                                                                                  continued
        87ilO 2
                                                           15

-------
TABLE 3-1.   Concluded
Study Name
EPRI-ERAQS




EPR1-URAQS





TVA-USMNP


PANORAMAS



RESOLVE


Blue Hill
Observatory
Los Angeles
Observations
Data Record No. of Sites Measurements
6/79-12/79 9 S02, N0/N02. 03;
total, fine.
Inhalable
particles

1/81-10/82 11 Fine, coarse
particles;
prevailing and
viewing path
visibility;
radiance
1980-1981 1 Fine particles


7/84-10/84 26 bscat» f1ne«
coarse
particles

8/83-8/85 7 bext; radiance;
fine, coarse
particles
1889-1958 1 Visual range.
"ext
1933-present 1 Visual range

Instruments
Telephotometers,
nephelometers,
cameras, particle
samplers, human
observation
Teleradlometers,
nephelometers.
fine particle
samplers, Human
observation

Particle samplers


Nephelometers,
particle samplers,
cameras, tele-
photometers
Particle samplers,
teleradiometers

Human observation

Human observation

Frequency
of Measurements Area
Varied Nine
"Class I site-



Varied Nine western
states




Great Smoky
Mountains
National Par
Daily Northwestern
United State


Varied California


8 a.m. and 2 p.m. Massachusett
-
Noontime Downtown Los
Angeles
                                                     16

-------
limitations that must be considered when using  these data for visibility
trend analysis,  and suggests that certain indicators and statistical
descriptors of visibility would be more suitable than others.  Trends
studies based on airport data are used to illustrate our discussion of
these limitations and their implications.

The terms "visibility" and "visual range" are commonly used interchange-
ably.  Visual range is defined as the farthest  distance at which a black
object is discernible against the horizon background sky.  Since the capa-
bility of different observers to discern objects having small contrasts is
likely to differ, visual range observations are subject to human per-
ceptual and judgment differences and variations over time and are there-
fore likely to be somewhat uncertain.  By making assumptions regarding the
"just-discernable" contrast and the nature of the specific line of sight,
one can derive relationships between visual range and quantitative
parameters that describe the atmosphere's ability to scatter and absorb
light, as discussed Section 2.
How Airport Visibility Measurements are Made

Since black objects are rarely available at airports, visual range obser-
vations are only approximations based on the "perceptibility" of avail-
able, distant objects and are based on whether or not "markers" (e.g.,
buildings, towers, mountains) at different distances and in different
directions are visible.

There are standardized methods for observing and recording visual range at
civilian and military airports in the United States.  The U.S. Department
of Commerce "Federal Meteorological Handbook No. 1, Surface Observations"
documents the method by which visual range observations are made.  When
visual range is reported, standard operating procedures state that the
criterion of "prevailing visibility" should be used.  "Prevailing visibil-
ity" is the "greatest visibility equaled or exceeded throughout at least
half the horizon circle, which need not necessarily be continous."

In determining prevailing visibility, the observer divides the horizon
into sectors and assigns a visibility value to each.  The number of  sec-
tors selected depends on the amount of variation in visibility between
sectors.  The observer uses well-known and documented (known distance or
range) markers to determine visibility.  However, over the course of sev-
eral years, the location of visibility markers and even the airport  itself
may change, possibly introducing discontinuities into visibility trend
data.
 8711010                          17

-------
If a visual  range of a certain distance is recorded, that means the marker
at that distance was visible,  and that the marker at the next greatest
distance was not visible.   Thus,  the reported visual range is probably
understated, assuming a black  target.  After about 10 miles, the number of
markers decreases rapidly  and  there are much greater distances between
them.  Before the 1970s, little effort was made to estimate visibility
beyond 15 miles.  If the 15-mile marker was very clear but a 30-mile mar-
ker was not visible, a visibility of only 15 miles was reported.  Visi-
bility between.markers is  currently estimated based on the clarity of the
farthest marker that can be seen.  Visibility past 15 miles is reported in
five-mile increments.
Data Availability

Airport visual range observations are made every hour; however, in recent
years, only the readings from every third hour are entered into the
National Climatic Center's data base.  Data are available in three differ-
ent forms from the National Climatic Data Center in Asheville, North Caro-
lina:  (1) computerized records (TDF-14 format), (2) Local Climatological
Data (LCD) summaries in hard copy, and (3) observer reports in hard copy
or microfiche.  While most of the data are available in both computerized
and hard-copy format, data for some sites are available only in hard
copy.
Data Limitations

There are a variety of limitations associated with airport visibility
data.  One is the general scarcity of markers beyond 10 miles, particu-
larly for data obtained before 1970; another is interairport variation in
reporting practices and marker availability.  There can also be changes in
observers, locations, and "type" of markers.  The implications of these
limitations are discussed in some detail below.
Marker Availability At Individual Airports

The reported visual range depends on the range within which the observa-
tion occurs: within a 3-mile range, visual range is reported to fractions
of a mile; between 3 and 15 miles, it is reported to the nearest mile;
beyond 15 miles, the resolution is very coarse and somewhat arbitrary.
Even in areas of the United States that have typically low visibilities,
such as the East Coast, visibility is often greater than 15 miles.  The
availability of markers past 15 miles is crucial in determining trends in
 8711010
                                  lo

-------
median and good visibility.   However, most airports in the East and many
airports in the West have few, if any, markers beyond 15 miles.

Since 1970, airport observers have routinely estimated visibility between
markers and beyond the farthest marker based on the clarity of the tar-
get.  This practice has resulted in a marked improvement in the availabil-
ity of data for visual ranges greater than 15 miles.  Many airports in the
East have median visibilities around 15 miles, causing some researchers to
report 60th percentile visibility to get around the pre-1970 truncation of
data at 15 miles (Sloane, 1982a, 1982b).  Many airports in the West have
visibilities greater than 15 miles significantly more than 50 percent of
the time.  The frequency of visibility greater than 15 miles makes it
difficult to analyze visibility trends using airport data from before
1970.
Interairport Variations

Variations in marker availability and reporting practices among airports
have important implications both for choosing which airports to use in an
analysis and for deciding how to describe typical visibility in a
region.  As another example, assume two airports have median visibilities
of 75 miles.  If at one of the airports the most distant marker is less
than 75 miles the median would have to be extrapolated from the data and
would probably be calculated as less than 75 miles.  The airport with
markers more distant than 75 miles will provide more data for calculating
the median correctly.

There are also important observer variations. - Some observers may care-
fully estimate visibility between markers and beyond the farthest marker
whereas others may not.  In a survey of ten major airports conducted as
part of this project, we found that the chief observer in Seattle almost
never reported anything beyond the farthest marker observed.  He inter-
preted NWS observer regulations as meaning that if he could see the 50-
mile marker but not the 100-mile marker, he should report 50.  Other
observers had a systematic method for reporting between markers.*  Fin-
ally, the "type" of marker can change.  In one year many markers may be
 *  For example, in New York there are no markers between 8 and 20 miles.
   If the 8-mile marker  (the World Trade Center) is  'pretty clear',  obser-
   vers will report 12 miles.   If the observers can make out  some of the
   colors in the banners on the center they report 15 miles (Thompson,
   1987).


 8711010                          19

-------
tree-covered hills or mountains while in another many markers may instead
be radio towers or buildings.   The type of marker itself may have an
effect on reported visibility  since some markers are easier to see than
others.

If a single airport is used to describe visiblity for a given region, the
presence of such reporting factors should be investigated and docu-
mented.  If more than one airport is used, attention must also be given to
the fact that variations in visibility trends between airports may be
obscured or exaggerated by differing reporting practices and marker
availability.  Problems inherent in using just one site can be more easily
overcome than problems related to pooling sites.  If data from different
airports are pooled and averaged in some way, these considerations are
even more important.

Because of-the factors just discussed, regional-scale trends studies have
generally carried out a telephone survey of potential airports before
choosing which ones to use (Trijonis and Shapland, 1979; Trijonis and
Yuan, 1978; Sloane, 1980; and  Vinzani and Lamb, 1985).  The survey makes
it possible to find which airports have the best set of markers for visi-
bility trends.  Questions can  focus on the number and types of markers at
various distances, approaches  for reporting visibility between markers and
beyond the farthest marker; changes in personnel, and the effect of local
pollutant sources on visibility.
Some Implications Regarding the Use of Airport Data*
In Visibility Trends Studies

The nature of airport visibility data also has implications for the selec-
tion of statistical descriptors of visibility parameters.  For example,
* NOTE:  The U.S. Weather Service is in the process of evaluating alterna-
  tive instrumentation for measuring visual range  (M. Pitchford, personal
  communication, 1987).  If such a change in the manner in which the U.S.
  Weather Service measures visibility occurs, visibility trends analysis
  may be limited to two distinctly different time  periods and measurement
  approaches:   (1) The first time period, from 1948 to the transition time
  in the late 1980's or early 1990's, would be based on human observa-
  tions; (2) the second period, after the transition, would be based on
  the new instrumentation.  It might not be possible to compare data
  before and after the transition because of differences in measurement
  technique.


87110  10                          20

-------
since reported visibility is often understated, a statistic such as the
mean can be a misleading indicator of true visibility conditions.  Anal-
ysis techniques need to be selected to minimize sources of uncertainty.
For instance, analytic methods based on the cumulative frequency of visi-
bility are considered to be best for analyzing airport data because they
are well suited to the analysis of observations that are inherently under-
estimates.  Cumulative frequency and other methods are discussed more
fully in Section 4.

In spite of their limitations, the U.S. Weather Service visual range
observations at airports appear to be the only currently available source
of visibility data to support trend analysis and reporting for the entire
country.  Almost all trend analyses performed to date have been based on
these data (Duckworth and Kinney, 1978,1980,1981; Husar et al., 1979,
1981, 1984, 1986; Latimer et al., 1978; Sloane, 1981, 1982a,b, 1983, 1984;
Trijonis and Yuan, 1978, 1979; Trijonis, 1980, 1981, 1982, Trijonis and
Shapland, 1979).  Concerns have been raised about the accuracy and relia-
bility of data based on human observations (Middleton, 1984); however,
with careful screening of sites and observation periods, these data have
been demonstrated to be useful for visibility trend analysis.
INSTRUMENT-BASED VISIBILITY DATA

Data obtained from instrument-based measurements of visibility or fine
particles may be more precise than those based on human observations of
visual range, and thus more reliable.  However, each visibility measure-
ment technique used currently is known to be subject to uncertainty.  This
uncertainty is related to a number of conditions that violate the assump--
tions of the methods used to estimate visual range from the parameter mea-
sured (e.g., change in contrast, transmittance, and scattering coef-
ficient).  These conditions include bright and dark clouds behind the
viewing target, bright haze, shadowed targets, and snow-covered targets
(Malm, 1979; Allard and Tombach, 1981; Malm and Walther,  1980; Malm et
al., 1982).  The most extensive instrument-based visibility data base is
the National Park Service (NFS) monitoring network begun  in 1978 (Malm and
Molenar, 1984).  We describe this network and its use in  trend and visi-
bility characterization studies next.  Other networks, including some
particle monitoring networks, are described in Appendix A.  All of these
monitoring networks are summarized in Table 3-1.
 The  National Park Service Visibility and Fine Particle Network

 The  NPS  has a responsibility to "conserve the scenery" (NPS Organic  Act  of
 1916)  and  (along with other Federal Land Managers) to protect the  air-
 quality-related values  (Clean Air Act Amendments,  1977) of Class  I areas
 8711010

-------
under their jurisdiction.   Visibility is  considered to be an air quality-
related value;  thus,  it is  a critical aspect of the overall  management of
our national  parks.   Regulations implementing the Clean Air Act require
visibility monitoring in Class I areas to determine existing visibility
conditions, establish trends in visibility as emissions change, assess
cause-effect relationships, review progress toward visibility goals, and
provide input for modeling  new sources.  The NFS monitoring network is a
result of this monitoring responsibility.

The NFS visibility monitoring network was initiated in March of 1978 as a
joint effort by the EPA Environmental Monitoring Systems Laboratory (EMSL)
in Las Vegas and the NPS. In its early stages, it was called VIEW (Visi-
bility Investigative Experiment in the West).  Initially, 13 sites in west
Texas, New Mexico, northern Arizona,  western Colorado, and Utah were
equipped with multi-wavelength teleradiometers and cameras.  The teleradi-
ometers were used to measure sky and.target radiance at different wave-
lengths for approximately 5 targets in each of the 13 parks at 9 a.m.,
noon, and 3 p.m. daily.  Contrast, standard visual range, and extinction
coefficient were calculated from these measurements.  Site operators also
recorded meteorological conditions and target snow conditions.  Associated
with VIEW was a Western Fine Particle (WFP) program operated by the Uni-
versity of California at Davis from late 1979 to late 1981.  By 1982,
there were 31 sites in the  NPS network containing teleradiometers, cam-
eras, and fine particulate  samplers,  three of which were in eastern Class
I areas.  Twenty of these contained both teleradiometers and particulate
monitors.

For a variety of reasons, only a limited number of trend assessments have
attempted to make use of this data set.  These reasons include  (1) maximum
amount of data available at any one site is 8 years,  (2) this data is
available from a small number of sites, and  (3) technical problems with
the teleradiometers make many of the measurements subject to uncer-
tainty.  In fact, data from this network has only been used to  charac-
terize current visibilty conditions and source effects.  Malm et al.
(1981) used the network to characterize visibility in the southwestern
United States from the summer of 1978 to the spring of 1979, calculating
standard visual range from the teleradiometer data.  Malm and Molenar
(1984) used this network to characterize western visibility from 1978 to
1982.  Typical geometric mean visual  ranges found in the summer of 1982
ranged from 40 to 144 km in California, Oregon, and Washington, and from
100 to 187 km in other parts of the West.   These figures compare well
with reported visual ranges obtained from airport observations  by Trijonis
(1979).

Other studies have also used data from this  network.  Examples  include
Tombach et al.  (1987), which examined annual and seasonal visibility from
 87iio  10                          22

-------
1981-1982 at 11 sites;  a study by the John Muir Institute, which operated
the network for the first few years (John Muir Institute, 1982), that
analyzed data for 25 sites for a three-year period covering 1978 through
1981, and a study that  evaluated the impact of sources on visibility
(Pitchford et al., 1981).
IMPROVE NETWORK

Description of the IMPROVE Network

In 1984, the EPA reached a settlement agreement with the Environmental
Defense Fund and the National  Parks and Conservation Association over a
suit filed because of the slow progress of states in responding to EPA
visibility requirements (Stonefield and Metsa, 1987).  The first part of
the agreement required the EPA to propose and promulgate federal implemen-
tation plans covering monitoring and new source review.  The committee
formed to oversee the monitoring efforts is called IMPROVE (Interagency
Monitoring and Protected Visual Environments).  IMPROVE is made up of a
number of federal agencies, including the NPS, the EPA, the Forest Ser-
vice, the Fish and Wildlife Service, and the Bureau of Land Management.

IMPROVE's responsibility is to establish the background levels of visibil-
ity necessary for assessing the impacts of potential new sources; to
assess progress toward the national visibility goal; and to determine
sources and levels of reasonably attributable impairment.  The network set
up to achieve these goals will consist of about 20 sites.  All of the 156
United States visibility-protected areas were classified into four cate-
gories on the basis of their potential for anticipated visibility change,'
scenic sensitivity and value, existing visibility problems, and the number
of other visibility-protected areas that could be represented.  Sites in
the first category had the highest monitoring priority.  The locations of
the monitoring sites are presented in Figure 3-1.

Each site will use camera/transmissometer combinations and particulate
samplers instead of teleradiometers.  Some of the current NPS sites are
included in the  IMPROVE network.
Current Status

The  IMPROVE network will not officially begin operation until fall of
1987.  At present, less than five transmissometers are operating  and bids
are  out to develop sites for the remainder of the network.  The NPS  is
operating the network through a private contractor in Fort Collins,
Colorado.
 87110  10                          23

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NPJS SCENEd f.ito..   — Auxiliary Sites
— IMPROVE Network     — New Auto Camera Sites
                Figure 3—la. Visibility monitoring sites.
                               (See  next page for  key)

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FIGURE 3-1 b.  VISIBILITY MONITORING SITES
           AUXILIARY SITES
            1.  BUFFALO NR
            2.  CAPITAL REEF NP
            3.  CAPULIN MOUNTAIN NM
            4.  CHACO CULTURE NHP
            5.  COLORADO NM
            6.  CRATERS OF THE MOON NM
            7.  DEATH VALLEY NM
            8.  DINOSAUR NM
            9.  EVERGLADES NP
            10.  GRAND TETON NP
            11.  GUADALUPE MOUNTAINS NP
            12.  JOSHUA TREE NM
            13.  LAVA BEDS NM
            14.  LEHMAN CAVES  NM
            15.  NORTH CASCADES NP
            16.  OLYMPIC NP
            17.  THEODORE ROOSEVELT NP
            18.  WIND CAVE NP
            19.  ZION NP
            20.  GLEN CANYON NRA
            21.  LAKE MEAD  NRA
            NEW AUTOMATIC CAMERAS SITES

            1.  BADLANDS NATIONAL PARK
            2.  BANDELIER NATIONAL MONUMENT
            3.  GREAT SAND DUNES NATIONAL MONUMENT
            4.  HALEAKALA NATIONAL MONUMENT (NOT SHOWN)
            5.  LASSEN VOLCANIC NATIONAL PARK
            6.  POINT REYES NATIONAL SEASHORE
            7.  REDWOOD NATIONAL PARK
            8.  VIRGIN ISLANDS NATIONAL PARK

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FIGURE 3-1 c. VISIBILITY MONITORING  SITES
           IMPROVE NETWORK

            1. ACADIA NP
            2. BIG BEND NP
            3. BRIDGER NF
            4. BRYCE CANYON NP
            5. CANYONLANDS NP
            6. CHIRICAHUA NP
            7. CRATER LAKE NP
            8. DENALI NP (NOT SHOWN)
            9. GLACIER NP
           10. GRAND CANYON
           11. GREAT SMOKY  MOUNTAINS
           12. JARBRIDGE NF
           13. MESA VERDE NP
           14. MOUNT RAINIER NP
           15. ROCKY MOUNTAIN  NP
           16. SAN  GORGONIO NF
           17. SHENANDOAH NP
           18. SUPERSTITION  NF
           19. WEMINUCHE NF
           20. YOSEMITE NP
           21. VOYAGEURS NP
           22. ISLE ROYALE NP
           23. HAWAII VOLCANOES NP  (NOT SHOWN)
           24. YELLOWSTONE  NP
           25. PETRIFIED FOREST NP
           26. ARCHES NP
           27. CARLSBAD  CAVERNS NP
           28. PINNACLES NP
            NhS  SCENES SITES

             1.  -RYCE  CANYON
             2.  GLEM CANYON
             5.  GRAND  CANYON
             4-.  LAKE

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Suitability for Future Trends Analysis

The three key factors in identifying the suitability of the IMPROVE net-
work for future trends studies are future funding, the number of sites,
and the reliability of transmissometers.  Funding for the network is cur-
rently authorized by the EPA for the next three years.  The federal land
managers involved in the project are currently contributing 60 percent of
the funding and may be willing, if budgets permit, to increase their
involvement (Pitchford, 1987).  In any case, future funding is uncer-
tain.

The small number of sites also poses some limitations for future trends
studies in Class I areas.  There are 156 Class I areas listed by the
Department of Interior as having visibility important values.  Although an
effort was made to select 20 IMPROVE sites that were (among other things)
representative of other sites, further assumptions of representativeness
would need to be made if the 20 sites were used to estimate visibility
trends in all 156 Class I areas.  If we imagine the difficulty of charac-
terizing visibility in a single large park such as Yellowstone with one
monitoring site, we can begin to understand the difficulty of making such
assumptions Malm (1987).  It has been suggested that 30 to 40 sites in the
West and 30 to 50 in the East might be enough to adequately characterize
Class I area visibility.

The transmissometer is still a relatively unproved instrument, which was
only introduced into the field in 1986.  This constitutes a limitation to
the IMPROVE network because the quality of the data collected by the
transmissometer is relatively unknown.  Some testing and calibration will
have to be performed to evaluate the performance of transmissometers in
the IMPROVE network.  The conversion to transmissometry as the principal
method for long-path extinction measurements will require a number of
tests to ensure the accuracy of the measurements:

     (1)  Assessments of the comparability of transmissometer measurements
          with those collected by teleradiometers and other methods such
          as slide densitometers or nephelometers;

     (2)  Comparison of measurements from two collocated transmissometers;

     (3)  Introduction of filters into the line of sight to measure their
          effect on average  line-of-sight extinction; and

     (4)  Comparison of extreme values with theoretical maxima/minima.
 87110  10
                                  27

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EASTERN FINE PARTICLE VISIBILITY NETWORK (EFPVN)

Description of the Eastern Fine Particle Visibility Network (EFPVN)

The IMPROVE network will  fulfill part of the ongoing effort to meet the
national visibility goal.  The Eastern Fine Particle and Visibility Moni-
toring Network (EFPVN) is the result of recommendations made by the Inter-
agency Visibility Task Force in 1985.  The task force was charged with
developing a long-term strategy for addressing visibility impairment from
regional haze and integrating visibility issues with related regional
issues such as acid deposition and fine particles (Bachmann, 1985).  The
overall purpose of the network is to facilitate the review of particulate
matter air quality standards with respect to fine particles and visibility
(Evans et al., 1987).  The EPA is considering a fine particle standard
that establishes specific visibility goals for regions in the East that
are affected by regional  haze, and has solicited public comment on the
appropriateness of this focus (Federal Register, July 1, 1987).

The EFPVN will collaborate with two other long-term networks in collecting
data.  The EVFPN will monitor scene-specific visibility, the optical pro-
perties of the atmosphere, and aerosol characteristics, and will collect
some meteorological data.  There will be two types of sites: Tier 1 sites,
which are primarily non-urban and will constitute the core research sites,
and Tier 2 sites, which will gather more limited information.  The 10
Tier-1 sites will use an automated fine particle sampler, a nephelometer,
and an automated camera.   Meteorological data collected will include wind
speed, wind direction, temperature, change in temperature, relative hum-
idity, and solar radiation.  The Tier-1 sites will be located in each of
ten areas identified by Husar (1984, 1986) as having similar visibility
trends based on airport data (Evans, 1987).  The 20 Tier-2 sites will be
camera-only sites and will likely be located along with with existing
SLAMS PM-10 monitors (Evans, et al, 1987).
Current Status

A test site at Whiteface mountain in New York is operating now with a
nephelometer, quartz and teflon filter fine particle samplers, and two
cameras (one half-way between the target and the first camera).  The net-
work should begin operation in October or November of 1987.  As currently
planned, it appears that only the Tier-1 sites will be included in the
network because of funding limitations.
                                  28
 87110  10

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Suitability For Future Trends Analysis

As with the IMPROVE network, a primary problem with the EFPVN is fund-
ing.  This network has been established largely to aid in determining the
correct level of a possible fine particle standard (Pitchford, 1987).  It
has been funded for only four years, which is not considered to be an
adequate time period for a trends study.
                                   29
 87110  10

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                  ALTERNATIVE APPROACHES FOR CHARACTERIZING
                           TRENDS IN VISIBILITY
Visibility trends analysis has been performed using a variety of different
summary statistics,  including means, medians, percentiles, frequencies of
visibility greater or less than a reference distance, and ridits (proba-
bility of a given visibility frequency distribution being better than a
reference distribution).   However, visibility trends analysis can be com-
plicated by diurnal  and seasonal variation.   Diurnal variation has been
addressed through examination of summary statistics for certain times of
day only (most commonly midday) or several times of day excluding morn-
ings.  It is common to exclude nighttime visibility because it is not
relevant to human visual  perception.  Seasonal variation has been addres-
sed through the separate calculation of trends for each season.  In this
section, we discuss several approaches for summarizing and comparing visi-
bility frequency distributions in an abbreviated, easy-to-understand
fashion, and the implications and limitations associated with each
approach.  These approaches are reviewed here in the context of summari-
zing airport visibility data.  Our recommended approach is discussed in
Section 7.
DESCRIBING VISIBILITY TRENDS WITH MEANS

It is difficult to summarize visibility data as reported by airport
weather stations using means because of the nature of these data.  Calcu-
lating a mean value from airport data has been compared to "having one
foot in a pail of boiling water and another in a pail of freezing water
and saying that, on average, the temperature is luke-warm" (Sloane,
1987).  As discussed in Section 2, each reported value is actually a lower
bound to the actual visibility, particularly at longer distances.  For
example, if there are markers at 7 and 15 miles and the 7-mile marker is
visible but the 15 mile marker is not, 7 miles might be the reported visi-
bility, though visibility is actually between 7 and 15 miles.  Thus, not
only is calculation of the mean value an inappropriate method for sum-
marizing data reported in such a manner, but it will also systematically
underestimate true average visibility.

None of the studies reviewed calculated simple arithmetic means for air-
port data, though a few (Kelso, 1981; Kelso et al., 1984; Roberts, 1974)
 87110

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have used this approach to summarize trends in visibility data collected
with instrument networks.   Other studies (Kelso et al.,  1981; Husar, 1981;
John Muir Institute,  1983; Roberts,  1974;  Tombach et al., 1987) have used
geometric means (means calculated from logarithms of the data), which are
better measures of central tendency  for the skewed distributions that
might be observed in  air quality or  visibility data.  For most of these
studies, the means are calculated and compared with other measures of
central tendency, such as the median, but  are not normally used as the
sole measure of "average" visibility.
THE FLUX METHOD

One way of presenting visibility trends is known as the flux method, which
graphically portrays exactly the reported data.  The trend parameters are
the percentage of time that visibility is less than or greater than the
discrete reported values.  This method is well-suited to airport visi-
bility data, in which visual range is reported for only a limited set of
markers.  With airport data, we can summarize the percentage of time that
visual range is less than or greater than each marker distance, but we
would have to interpolate to estimate the percentage of time that the
visual range is less than or greater than a distance for which there is no
marker.  Early papers on visibility have all used this form of analysis
(for example, Holzworth and Maga, 1960; Green and Battan, 1967; Duckworth
and Kinney, 1978 and 1983; Latimer et al., 1978; and Miller, 1972).

An example of a visibility trend analysis that uses the flux method is
shown in Figure 4-1.  We see that there are markers at 4, 7, 10, 12, 20,
and 25 miles.  The left vertical axis shows the percentage of time that
visual range is less than the marker distances, and the right vertical
axis Indicates the percentage of time that visual range is greater than
the marker distances.  Thus we see that in the summer quarter of 1973,
visibility was less than 7 miles about 33 percent of the time, while in
1975 it was less than 7 miles about 25 percent of the time.  The plot
shows the main advantage of the flux method:  With limited visibility
data, such as that reported at airports, the plot shows exactly how the
distribution of values changes across time.

Since the flux method uses summary statistics that are unitless, actual
trends in visual range across time are not reported, as would be the case
with trends calculated using means or medians.  Thus the flux plots are
slightly more difficult to interpret.  Another difficulty in inter-
pretation can arise if there are different trends at different markers.
This might erroneously appear to be the case if reporting practices
change.  For example, in Figure 4-1, we see that visibility in the winter
quarter is not reported as greater than 25 miles until 1975.  This may
 87110  >»

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                   Noontime  Visibility  at Baltimore, 1972-1986


               FIGURE 4-1.   Example  of  visibility  trends characterized using
               flux method.
87110
                                           32

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indicate a real  change in the percentage of days with "good" visibility or
it may indicate  a change in the reporting practice.

The major drawback of the flux method is that site-to-site comparisons are
very difficult,  if not impossible.   If the objective is to compare the
percentage of time visibility is above or below a reference value at each
site, then plotting the frequency of good or bad visibility conditions for
all sites would  require interpolation and extrapolation of percentiles at
some sites.  Thus, to achieve spatial comparability one would be forced to
interpolate and  extrapolate from the cumulative frequency distribution,
the avoidance of which is one of the principal advantages of the flux
method.  However, many researchers believe that it would be better to pre-
sent visibility  trends in a site-specific manner in any case, because of
other problems associated with pooling data across stations.
THE PERCENTILE APPROACH

The most commonly used statistical indicator of visibility frequency dis-
tributions is a percentile of the visibility frequency distribution.  The
50th percentile, or median visibility, is an indicator of typical visibil-
ity conditions:  Half of the time visibility is better than the median
value and half of the time it is worse.  The 10th percentile is that vis-
ibility which is exceeded 90 percent of the time.  Similarly the 90th vis-
ibility percentile is visibility that is exceeded 10 percent of the time
during the period of record.  Percentiles are currently used by most visi-
bility trend analysts (e.g., Malm and Molenar, 1984; Sloane, 1981,
1982a,b, 1983, 1984; Trijonis and Yuan, 1978, 1979; Trijonis and Shapland,
1979; Trijonis, 1980).

Percentiles are straightforward to understand and interpret since the
units of the percentile are the same as the quantitative parameter used to
describe visibility (i.e., the visual range or extinction coefficient);
trend lines can therefore be drawn with y-axes in the units of interest.
The extremes of the visibility frequency distribution (both good and bad
visibility) can be characterized by different percentiles.  10th and 90th
percentiles are most commonly used to characterize the tails of the dis-
tribution; however, 25th and 75th percentiles could be used in their
place, or in addition.  Several percentiles can be effectively plotted on
the same graph using the boxplot technique currently used in the EPA
trends reports for summarizing national distributions of annual air qual-
ity summary statistics.  An example of visibility percentiles displayed in
boxplots is provided in Figure 4-2.  The median is indicated by the line
in the center of each box; the top and bottom of each box indicates the
75th and 25th percentiles, and the black bars outside the boxes mark the
10th and 90th percentiles.
 87110
                                  33

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                 1972    1974    1976    1978    1980    1982     1984     1986
                                              Year
              Boxplot Comparisons  of Visibility for Baltimore,   1S72 —1936
               FIGURE 4-2.   Example of visibility trends characterized using
               percentiles  displayed with box plots.   The percent!les displayed
               are the 10th, 25th,  50th (dashed line  connects median across
               time), 75th, and 90th.
87110
                               34

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The main disadvantage of the percent!le approach when applied to U.S. Wea-
ther Service airport visibility observations is that the calculation of
specific percentiles, as typically carried out, requires interpolation of
the empirical frequency distribution.   This in turn requires assumptions
about the form of the underlying frequency distribution.  For example,
assume that the markers used to report visual range at a given airport are
at 5, 10, 30, 40, and 50 miles, and that the frequency of visual range at
these markers is as follows:
                                                 Cumulative
            Marker          Frequency of         Frequency of
            (miles)        Exceedance (%)       Exceedance (%)

              50                  5                   5
              40                 15  .                20
              30                 10                  30
              10                 30                  60
               5                 40                 100

What would the 50th percentile be?  Figure 4-3 illustrates two possible
answers.  The actual cumulative percentages from the tabulation just given
are shown as points on the plot.  The 'step1 function shown by the dashed
lines is statistically correct in the sense that it admits there is no
data betwen points and assumes no more than the data record.  The median
calculated with this representation of the distribution is 30 miles. The
common approach is not this step function, but rather an interpolation
from the solid line connecting the reported values; as shown in the fig-
ure, the 50th percentile or median calculated in this way is about 17
miles.  Such an interpolation assumes a very simple model for the reported
data—that frequencies of occurrences are uniform between markers.  For
example, 30 percent of the values are reported as greater than or equal to
10 miles but less than 30 miles.  The interpolation scheme assumes that if
we had markers at every mile, we would have exactly as many occurrences of
10 miles as we would of 11 miles or 12 miles or 29 miles.  Clearly this is
a naive assumption; several researchers have shown that visibility distri-
butions, like most air quality distributions, are skewed (e.g., Husar et
al., 1979).

A second problem with the percentile method involves the estimation of
extreme percentiles of the visibility frequency distribution.  Suppose
that there were no marker at 50 miles in our example tabulation, and sup-
pose we were interested in the 90th percentile, as an indicator of good
 87110
                                   35

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                            2O         3O


                       Marker Distance (miles)
4O
50
 FIGURE 4-3.  Example of calculation of percentiles in a visibility

 frequency distribution—interpolation (solid line) versus empirical

 step function (dashed line).
                      36

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visibility.  We know only that visibility is better than 40 miles 20 per-
cent of the time.  To estimate the 90th percentile, we have to assume some
mileage for the best possible visibility to be able to interpolate, or in
this case really extrapolate, what the 90th percentile would be.  Obvi-
ously, the choice of "best" determines what the percentile will be.  If
the best visibility is assumed to be 60 miles, then the 90th percentile
will be calculated as 50 miles; if the best visibility is assumed to be 70
miles, then the 90th percentile is 55 miles.  At sites where visibility
markers are few and far between, such interpolation and extrapolation can
introduce significant uncertainty.  A discussion of sources of uncertainty
in these circumstances can be found in Sloane (1984, Appendix B).

Some researchers have concluded that lognormal distributions fit visi-
bility data in many cases.  Husar et al. (1979) maintain there is con-
siderable evidence that visibility and light extinction, like atmospheric
concentrations of fine particles, are lognormally distributed.  They
inspected several locations and seasons for which entire visibility cumu-
lative frequency distributions could be determined and found no evidence
of gross inconsistency between the actual distributions and the lognormal
distribution in the 10th to 90th percentile range.  They developed a semi-
automated extrapolation procedure whereby the cumulative frequency distri-
bution function is fitted with a straight line (on lognormal probability
paper), using a semicircular weighting function centered at 50 percent,
with cutoffs at 10 and 90 percent.  The fitted distributions were used to
estimate the mean and percentiles.

Other common statistical distributions, such as the Weibull, gamma, or
inverted gamma, could also be fit to visibility data.  The small number of
routinely reported visual ranges at airports would not likely allow one to
conclude which of these is a more accurate representation of the actual
visibility distribution.  More than likely, the data could appear to fit
any number of distributions.

Fitting a distribution to the data can cause potential problems in evalua-
ting variations in visibility.   In our example, different distributions
cause the resulting median to vary by 12 miles (from 17 miles for the
interpolated percentile to 30 miles using the step function).   In effect,
13 miles of  'noise' are introduced into the analysis.  This "noise" makes
it difficult to determine whether a given annual or seasonal change  (or
lack of change) was a true change or just an artifact of the method of
determining percentiles.
 87110  U                           37

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RIDIT ANALYSIS

Another approach to obtaining a concise representation of the entire visi-
bility frequency distribution for the purpose of trend analysis is through
ridits.  A ridit is the probability that a visibility observation in a
given period was better or less than a visibility observation from a ref-
erence distribution.  The given period could be any year, while the refer-
ence could be all years included in the trend analysis.  Ridit analysis
has been used by Craig and Faulkenberry (1979), Sloane (1982a), and
Vinzani and Lamb (1985) for visibility trend analysis.  The technique has
the advantage of being able to use the specific visibility target dis-
tances directly.  It also uses the entire frequency distribution.  A sin-
gle number, the mean ridit, is obtained for each year.  However, the
approach has several disadvantages.  The concept is more difficult to
understand; a unitless probability that a given frequency distribution is
better than a reference is less instructive than a percentile or mean
visibility or extinction.  Also, though the entire frequency distribution
is used, ridit analysis is less sensitive to changes at the tails of the
distribution; therefore the trends in 90th percentile (poor) visibility
conditions may be underrepresented.  Sloane (1982a) found that ridit anal-
ysis is consistent with trend analysis based on percentiles (carefully
applied) and therefore ridit analysis may offer no clear advantage over
the percentile approach.  Sloane1s more recent (1984) visibility trends
work has used percentile visibility rather than ridit analysis.
 87110 >»                           38

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          ALTERNATIVE METHODOLOGIES FOR INCORPORATING METEOROLOGICAL
                 VARIABILITY IN VISIBILITY TREND ANALYSIS
For the purposes of the national air quality trends report, it is desir-
able to distinguish visibility trends associated with anthropogenic
(human-caused) factors from those associated primarily with natural
events.  For example, low visibility may be due to natural events such as
rain, snow, fog, windblown dust, or even volcanic eruptions.  Other nat-
ural factors, such as humidity, act in conjunction with existing pollutant
concentrations to improve or degrade visibility.  This section reviews
methodologies used for incorporating meteorological variability in past
visibility trend studies.
SELECTION OF HOURS

Airport visibility data for every third hour are archived in the National
Climatic Data Center's data base.  Thus, all daytime values (e.g., LST
values for 0700, 1000, 1300, and 1600) can be used to compile visibility
frequency distributions.  Given 4 observations per day for 365 days, a
total of 1460 data points per year would be available.

Sloane (1982) has documented that certain hours of the day have generally
bad visibility for reasons unrelated to pollutant levels.  For example,
many areas experience early morning fog.  Furthermore, early in the morn-
ing, high local particulate concentrations caused by trapping within the
ground-level nocturnal inversion may cause visibility impairment.  Sloane
(1982a) noted that early morning (0700) observations did not follow the
same pattern as did other daylight observations made at other times of
day.  Other times of day are subject to such influences as sun angle,
which can result in visibility degradation because of increased backward
or forward light scattering.  Latimer et al. (1978), Trijonis and Yuan
(1978, 1979), Sloane (1982a), Holzworth and Maga (1960), and Miller (1972)
have all used all daytime visibilities in their trend analyses.   Such an
approach need not make assumptions regarding the contribution to visibil-
ity improvement or impairment made by particular hours.

Other researchers have used a single hourly visibility value, representing
either midday or afternoon, rather than all daytime, values (Husar et al.,
 87110  5                           3g

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1979, 1981,  1984;  Trijonis  and Shapland,  1979;  Trijonis, 1980; Vinzani and
Lamb, 1985).   Because daily variations in visibility are most pronounced
in the early morning or late afternoon,  the use of midday or early after-
noon observations  may provide a data base that  is less influenced by nat-
ural meteorological  phenomena.  Vinzani  and Lamb (1985) used afternoon
(1500) data  because  temperatures are at  their highest at that time and
thus relative humidities are at their lowest and atmospheric mixing is at
its highest.   Some studies  have used only those daylight hours during
which natural visibility impairment would be unlikely to occur (Sloane,
1980, 1982;  Malm,  1981).
USE OF RUNNING AVERAGES TO SMOOTH DATA

Because of year-to-year variation in meteorological conditions, many
researchers have averaged visibility statistics from several years (most
typically three) to obtain a running average (overlapping mean, of several
years' statistics) in an attempt to smooth out interannual variations
(Trijonis and Yuan, 1978, 1979; Husar et al., 1979; Sloane, 1982a, 1984;
Vinzani and Lamb, 1985).  One disadvantage of this approach is that it
introduces another layer of averaging that moves farther from the actual
data and the very real interannual variation in visibility conditions
caused by meteorological differences.  Also, running averages of several
years' data are not currently computed for other kinds of air quality data
reported in the EPA annual trends report (although some short-term stan-
dards are based on expected exceedances, normally calculated as running
averages of three years).
ELIMINATION OF HOURS SUBJECT TO NATURAL VISIBILITY IMPAIRMENT

An analysis of visibility trends related to anthropogenic factors may need
to exclude from the data hours subject to natural visibility impairment.
For example, precipitation is a natural phenomenon that limits visibility
and is not representative of the pollutant concentration in an air mass.
A more complex example is fog, which may form from natural or anthropo-
genic aerosols and is sometimes difficult to distinguish from haze.
Another meteorological factor, relative humidity, can influence the amount
of impairment caused by a given particulate concentration.  Other natural
phenomena such as windblown dust can also result in visibility impairment
unrelated to anthropogenic emissions.  Almost all meteorological adjust-
ment procedures entail removing hours subject to certain meteorological
conditions.
 87110 S
                                  40

-------
Precipitation

All trends studies we are aware of that have used meteorological adjust-
ments have eliminated hours with precipitation from their analysis.
Humidity. Fog, and Visibility

In general, higher humidities result in greater visibility impairment.  At
higher relative humidities, more liquid water is associated with such
hygroscopic and/or deliquescent aerosols as sulfates and nitrates and some
organics, thereby increasing the light scattering mass.  Sloane (1983,
1984) has shown that relative humidity within a region can vary signifi-
cantly from one year to another, and that part of the observed downward
trend in visibility in the 1950s can be attributed to climatic changes in
humidity.  Because humidity-related visibility impairment may be associa-
ted with climatic changes rather than with increased pollution, some
studies have deleted humidities greater than certain values such as 90
percent (Sloane, 1980, 1982a,b; Vinzani and Lamb, 1985; Holzworth and
Maga, 1960; Miller, 1972; Craig and Faulkenberry, 1979) or 70 percent .
(Duckworth and Kinney, 1978, 1983).  A key factor in using a 90 percent
cutoff point for humidity observations is that natural fog is unlikely to
occur at relative humidities lower than 90 percent.  Thus the elimination
of relative humidities greater than 90 percent will eliminate most natural
fog and preserve observations of haze that may have been mistakenly iden-
tified as fog (Holzworth and Maga, 1960; Sloane 1982a).

Reiss and Eversole (1978) presented a technique whereby visibility statis-
tics can be adjusted to remove relative humidity effects on the basis of a
semi-empirical relationship between the scattering coefficient and humid-
ity.  Another approach to removing relative humidity effects is to strat-
ify the visibility data base by relative humidity and to compare only
interannual visibilities within a given relative humidity class.
TREATMENT OF METEOROLOGICAL VARIABILITY BY OTHER RESEARCHERS

As Table 3-1 illustrates, existing studies have used many different
approaches in analyzing and presenting visibility trends.  The majority
eliminate no data, or eliminate data for days with precipitation, fog,
and/or humidity over 90 percent.  There are some indications that trends
are the same whether or not meteorological screening is performed.   For
example, Sloane (1980, 1982a) performed a trend analysis for Charlotte,
North Carolina under seven different data-screening scenarios and found no
statistically significant differences once early morning hours were  elim-
inated from the data.  Vinzani and Lamb (1985) found that the inclusion of
 87110  5                           .,

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observations of precipitation and high humidities in Evansville, Indiana
did not affect visibility trends.

A more complex adjustment for year-to-year meteorological  variability used
by Holzworth and Maga (1960)  and others (Zeldin and Meisel, 1978; Chock
et. alt 1982; Sloane, 1984)  involves placing visual range values in dif-
ferent meteorological categories based on standard wind speed, direction,
and stability class.  Pollutant levels can then be adjusted to reflect
what they would be if, in each year, the frequency of occurrence of each
meteorological class were typical.  Alternatively, meteorological condi-
tions can be classified by their impact on visibility and trends in visual
air quality can be analyzed for each meteorological class separately or by
normalizing the frequency of occurrence for each class.
 87110  5                           42

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                              PROTOTYPE ANALYSIS
To provide a further basis for the conclusions and recommendations pre-
sented in Section 7, we performed a detailed analysis of seven years of
visibility observations from the Lambert International Airport in the St.
Louis metropolitan area.  This prototype analysis examined the

     (1)  Choice of hours to be included in the trend data set,

     (2)  Choice of visibility categories for use in flux analysis, and

     (3)  Effect of techniques for incorporating meteorological variabil-
          ity in trend analysis.


CHOICE OF HOURS

The NWS reports visibility and meteorological data at least once every
three hours.  Nighttime visibility is defined and measured differently
from daytime visibility and has little relationship to the public's per-
ception of prevailing visual air quality.  Therefore, we considered only
daytime observations as candidates for analysis.  Daytime observations are
recorded at local times corresponding to 9:00 a.m., 12:00 noon, and 3:00
p.m.

To reduce processing costs, to simplify the display of visibility pat-
terns, and to avoid the inclusion of daytime hours that may have charac-
teristically good or poor visibility for reasons unrelated to pollutant
concentrations, trend analyses can be conducted for just one of the three
available hours rather than for each of the hours or for their combined
data.

Figure 6-1 shows a plot of 10th percentile (poor) visibilities against
time for each of the three daytime hours for which data are available.
The patterns for each of the hours track each other well.  Similar plots
were examined for median and 90th percentile (good) visibility; again the
trend lines were very similar.  These analyses indicate that for this
particular station, little information is lost by using data for only one
hour per day.  Analyses reported in the literature indicate similar
87110 6
                                  43

-------
                                                                  HK 9A.M.
                                                                  -M- 12 Noon
                                                                  -»- 3P.M.
               1976     1977    1978     1979     1980
                                              Year
1981
1982
1983
                FIGURE  6-1.   Trends  in  10th percent!le (poor) visibility by
                recording hour at the St.  Louis airport.
                                        44
87110

-------
results (Sloane, 1982; Vinzani  and Lamb,  1985).   However, a more complex
technique employed by (Sloane,  1984)  suggested that meteorologically
adjusted trends were more optimistic  than analyses which did not consider
meteorological factors and an error analysis.
CHOICE OF VISIBILITY CATEGORIES FOR FLUX ANALYSIS

Flux analysis examines trends in the frequency of occurrence for various
categories of visibility.  Correct definition of these categories requires
an analysis of the annual frequencies of reported visibility at each sta-
tion for reasons explained in the following paragraphs.

As discussed in Section 3, visual range observations at a given station
may change over time due to personnel or procedural changes, or because of
changes in marker locations or reporting practices.  These changes can
result in inconsistencies in the measurements recorded, even if the true
visibility remains constant.  Such factors may lead to distortions in the
visibility distribution for some time periods relative to others.  If
these distortions are serious, recorded measurements may obscure real
trends in visibility or may give evidence of trends in visibility that are
artifacts of reporting changes.  Thus visibility categories used in repre-
sentations of visibility data must be selected carefully to reduce the
influence of procedural changes on the measurement of trends.

For the station used in the exploratory analysis, the only official mar-
kers used were at 3 and 4 miles, though over the 7-year period of record
examined, observations were recorded at 18 different distances.  Table 6-1
shows the frequency of observations by year at each of these 18 distan-
ces.  Distances are listed horizontally; years are listed vertically.  The
first number in a given box is the number of observations for that
distance in that year.  The next number is the percent of all observations
reported by the first number.  The third number is the percentage of times
that distance was recorded in that year, relative to the other years.  The
fourth number is the percentage of a years observations at the distance.
Prior to 1979, there were only a few isolated reports of visibilities
greater than 10 miles, but after 1979 more than 10 percent of the
observations in any year were recorded at distances of 11 miles or
greater.  Also noteworthy is the dramatic decrease in 1979 in the fre-
quency of observations recorded at 7 miles and an increase in the number
of observations reported at 8 miles.  These changes imply a change in
reporting practices rather than an actual change in visibility.

Visibility categories should be chosen to reduce the sensitivity of the
analysis to such procedural changes.  In the example just given, the
observations recorded at 8 miles could be combined with those recorded at
 87110  6
                                  45

-------
TABLE 6-1. Frequency of visibility by year at 18 distances for the St. Louis airport.
Vis Year
FREQUENCY
PERCENT
ROW PCT
COL PCT
0.312
1.375
1.5
2
2.5
3
j
76
h 	 -.--__H
1
0.01
100.00
0.10
0
0.00
-0.00
0.00
0
0.00
0.00
0.00
1
0.01
10.00
0.10
5
0.07
18.52
0.52
4
0.06
5.13
0.42
77
0
0.00
0.00
0.00
0
0.00
0.00
0.00
2
0.03
50.00
0.23
3
0.04
30.00
0.34
6
0.08
22.22
0.68
17
0.24
21.79
1.94
78
0
0.00
0.00
0.00
0
0.00
0.00
0.00
1
0.01
25.00
0.12
1
0.01
10.00
0.12
6
0.08
22.22
0.69
23
0.32
29.49
2.65
79
0
0.00
0.00
0.00
0
0.00
0.00
0.00
0
0.00
0.00
0.00
2
0.03
20.00
0.23
6
0.08
22.22
0.70
12
0.17
15.38
1.39
80
0
0.00
0.00
•o.oo
0
0.00
0.00
0.00
0
0.00
0.00
0.00
0
0.00
0.00
0.00
1
0.01
3.70
0.11
2
0.03
2.56
0.22
81| 82
0
0.00
0.00
0.00
0
0.00
0.00
0.00
0
0.00
0.00
0.00
0
0.00
0.00
0.00
0
0.00
0.00
0.00
5
0.07
6.41
0.54
0
0.00
0.00
0.00
1
0.01
100.00
0.11
1
0.01
25.00
0.11
1
0.01
10.00
0.11
1
0.01
3.70
0.11
13
0.18
16.67
1.48
83
0
0.00
0.00
0.00
0
0.00
0.00
0.00
0
0.00
0.00
0.00
2
0.03
20.00
0.23
2
0.03
7.41
0.23
2
0.03
2.56
0.23
TOTAL 953 877 868 862 904 924 879 861
13.37 12.30 12.18 12.09 12.68 12.96 12.33 12.08
TOTAL
•
1
0.01
•
1
0.01
•
4
0.06
•
10
0.14
•
27
0.38
•
78
1.09
7128
100.00
                                                                                  Continued
87110
                                          46

-------
      TABLE 6-1.  Continued.
Vis
FREQUENCY
PERCENT
ROW PCT
COL PCT
4
5
	 	 - - -H
6
7
8
9
TOTAL

76
15
0.21
11.11
1.57
>.__ 	 _.__H
29
0.41
8.71
3.04
58
9.57
6.09
87
. 1.22
5.92
9.13
120
1.68
21.16
12.59
1
0.01
4.17
0.10
953
13.37

77
22
0.31
16.30
2.51
52
0.73
15.62
5.93
86
14.19
9.81
122
1.71
8.30
13.91
119
1.67
20.99
13.57
0
0.00
0.00
0.00
877
12.30

78
23
0.32
17.04
2.65
66
0.93
19.82
7.60
68
11.22
7.83
104
1.46
7.07
11.98
154
2.16
27.16
17.74
0
0.00
0.00
0.00
868
12.18

79
26
0.36
19.26
3.02
53
0.74
• 15.92
6.15
123
20.30
14.27
325
4.56
22.11
37.70
53
0.74
9.35
6.15
5
0.07
20.83
0.58
862
12.09
Year
80
14
0.20
10.37
1.55
39
0.55
11.71
4.31
62
10.23
6.86
154
2.16
10.48
17.04
57
0.80
10.05
6.31
11
0.15
45.83
1.22
904
12.68

81
8
0.11
5.93
0.87
20
0.28
6.01
2.16
64
10.56
6.93
199
2.79
13.54
21.54
36
0.51
6.35
3.90
7
0.10
29.17
0.76
924
12.96

82
16
0.22
11.85
1.82
40
0.56
12.01
4.55
56
9.24
6.37
204
2.86
13.88
23.21
15
0.21
2.65
1.71
0
0.00
0.00
0.00
879
12.33

83
11
0.15
8.15
1.28
34
0.48
10.21
3.95
89
14.69
10.34
275
3.86
18.71
31.94
13
0.18
2.29
1.51
0
0.00
0.00
0.00
661
12.08

TOTAL
135
1.89
333
4.67
606
1470
20.62
567
7.95
24
0.34
7128
100.00
                                                                                   Continued
                                           47
87110

-------
       TABLE 6-1.  Concluded.
Vis
HREQUENCY
PERCENT
ROW PCT
COL PCT
10
12
13
15
20
- - .._„-.- H
25
TOTAL

76
333
4.67
14.61
34.94
299
4.19
42.78
31.37
0
0.00
0.00
0.00
0
0.00
0.00
0.00
0
0.00
0.00
0.00
0
0.00
0.00
0.00
953
13.37

77
270
3.79
11.84
30.79-
172
2.41
24.61
19.61
0
0.00
. 0.00
0.00
6
0.08
0.79
0.68
0
0.00
0.00
0.00
- - - - - - H
0
0.00
0.00
0.00
877
12.30

78
283
3.97
12.41
32.60
139
1.95
19.89
16.01
0
0.00
0.00
0.00
0
0.00
0.00
0.00
0
0.00
0.00
0.00
h - - _._-H
0
0.00
0.00
0.00
868
12.18
Y
79
211
2.96
9.25
24.48
18
0.25
2.58
2.09
4
0.06
7.41
0.46
23
0.32
3.04
2.67
1
0.01
1.25
0.12
K - - . ..._H
0
0.00
0.00
0.00
862
12.09
ear
80
266
3.73
11.67
29.42
32
0.45
4.58
3.54
30
0.42
55.56
3.32
211
2.96
27.87
23.34
23
0.32
28.75
2.54
h---- _.-.H
2
0.03
100.00
0.22
904
12.68

81
294
4.12
12.89
31.82
13
0.18
1.86
1.41
19
0.27
35.19
2.06
209
2.93
27.61
22.62
50
0.70
62.50
5.41
0
0.00
0.00
0.00
924
12.96

82
287
4.03
12.59
32.65
25
0.35
3.58
2.84
0
0.00
0.00
0.00
213
2.99
28.14
24.23
6
0.08
7.50
0.68
0
0.00
0.00
0.00
879
12.33

83
336
4.71
14.74
39.02
1
0.01
0.14
0.12
1
0.01
1.85
0.12
95
1.33
12.55
11.03
0
0.00
0.00
0.00
0
0.00
0.00
0.00
861
12.08

TOTAL
2280
31.99
699
9.81
54
0.76
757
10.62
80
1.12
2
0.03
t-
7128
100.00
87110
                                           48

-------
7 miles since a visibility recorded  at 7  miles means that visibility was
7 miles or greater.

For the test site examined, 10 miles is apparently the lowest upper limit
used in reporting visibilities over  the period, so "10 miles or greater"
would be the category used for any observation recorded at any distance
greater than 10 miles.  On the basis of the frequency table and informa-
tion provided by the observation station, visibility cutoffs of 3, 4, 7,
and 10 miles (that is, less than 3 miles; at least 3 miles but less than
4 miles; at least 4 miles but less than 7 miles at least 7 miles but less
than 10 miles; and 10 miles or further) were selected for the sample anal-
yses for this station.
INCORPORATION OF METEOROLOGICAL VARIABILITY

As discussed in Section 5, many visibility trend studies utilize some form
of screening on the basis of meteorological measurements to present trends
that reflect the impact of human activities on visibility.  To gain more
insight regarding the best way to accomplish this screening, our prototype
analysis examined

     (1)  The distribution of visibilities under various screening
          scenarios,

     (2)  The proportion of data that would be omitted under various
          screening scenarios,

     (3)  The distribution of visibility in different humidity categories,
          and

     (4)  10th, 50th and 90th percentile visibilities for five different
          humidity categories.

We analyzed the distribution of visibility under five different screening
scenarios:  one for all daytime observations; one for daytime hours with
no precipitation; one for only those daytime hours with precipitation; one
for daytime hours with no fog or other nonanthropogenic aerosols; and one
for only those hours with nonanthropogenic aerosols.  A dramatic decrease
in the frequency of poor visibilities was noted upon exclusion of hours
with precipitation, which indicates a strong association between precipi-
tation and poor visibility.  There is a greater frequency of poorer visi-
bilities when hours with natural aerosols are included than when they are
excluded.  Table 6-2 shows the proportion of data by season and year that
would be omitted under the screening procedures recommended in Section
7.  The total percentage of data thus excluded ranges from 10.9 percent
 87110  6                           4g

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            TABLE 6-2.  Percentage of noontime observations
            excluded from analyses for prototype station
            due to adverse meteorological conditions.
Year
1976
1977
1978
1979
1980
1981
1982
1983

Winter
12.2
24.4
33.3
41.1
33.3
10.0
31.1
40.0

Spring
12.0
12.0
25.2
25.0
14.1
15.2
8.7
25.0
Season
Summer
5.4
8.7
6.5
4.3
7.6
8.7
8.7
3.2

Fall
14.3
23.0
16.5
12.0
9.9
14.2
19.8
3.3

Annual
10.9
17.0
20.3
20.5
16.1
12.1
17.0
20.8
871102                          50

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for 1976 to 20.8 percent for 1983.   The percentage omitted by season is
more dramatic.   In summer and fall  the percentage of omitted data does not
differ greatly  from the annual amount.  In winter and spring, more data
are omitted, almost twice as much in 1979 as the highest amount in 1983.

As discussed earlier,  poor visibility is often associated with high humid-
ity.  In California, the visibility standard incorporates humidity
effects.  A violation of the California standard for visibility occurs
when the concentration of visibility-reducing particles is sufficient to
reduce visibility to less than 10 miles when humidity is less than 70 per-
cent.  At the St. Louis station, however, eliminating any observations for
which humidity  was greater than 70 percent would remove over 30 percent of
the data from the record.  This suggests that the California approach is
not appropriate for all parts of the country.  The elimination of hours
with higher humidities may also eliminate hours with higher particulate
concentrations; high humidities coupled with high temperatures may result
in increased emissions of visibility-reducing particles due to increased
demand for electric power for air conditioners.  Our analysis showed a
strong association between adverse visibilities and high humidities.
Visibility distributions do not appear to be associated with humidity at
humidities lower than 60 percent, but are highly correlated with humidi-
ties greater than 60 percent.

A key issue in eliminating hours with high relative humidities is the
question of whether such screening will change the trend in visibili-
ties.  Figure 6-2 shows 10th percentile visibilities as a function of time
for each of five levels of humidity screening:  no screening, removal of
hours with humidities greater than 95 percent, 90 percent, and 85 per-
cent.  Although there are some differences in the trend lines between the
different levels of screening, they track each other remarkably well.
Similar graphs were examined for the median and 90th percentile visibili-
ties; the effects of humidity screening on the middle and high ranges of
the visibility distribution are negligible.  It would be incorrect to
generalize these results to other stations or for longer periods of time,
but the results for the prototype station do not provide evidence that
humidity screening would have a dramatic effect on the visibility patterns
detected.  As described earlier, however, an analysis by Sloane (1983,
1984) has shown that changes  in humidity may have contributed signifi-
cantly to the observed downward trend in visibility in the late 1950s.
 871106                            51

-------
                                                     •+• No Humidity Scr
                                                     •*- RH<95%
                                                     -M- RH<90%
                                                     -•- RH<85%
          1976     1977    1978     1979    1980
                                        Year
1981
1982
1983
            FIGURE 6-2.   Trends in 10th percentile (poor) visibility using
            different meteorological  screening procedures.
87110
                                        52

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                       CONCLUSIONS AND  RECOMMENDATIONS
This section summarizes our conclusions and recommendations for developing
visibility trends data to be included in the National Emissions and Air
Quality Trends Report.  Recommendations cover the visibility data base,
visibility indicator, geographical areas for study, time period, and
method of accounting for natural visibility impairment.  Recommendations
are based on the literature review and telephone survey of visibility
experts conducted as part of this study, and on our own analysis of data
from St. Louis, Missouri.  Table 7-1 summarizes the advantages and dis-
advantages of each recommendation.
RECOMMENDED DATA BASE

Our research indicates that the only data base currently appropriate for
use in visibility trends analyses is a compilation of NWS airport
visibility observations.  Since no other data base approaches its
geographical extent and long period of record, we recommend that these
data be used in characterizing visibility trends for the National Trends
Report.  A careful selection of stations, visibility indicators, and
presentation format can reduce the limitations caused by variations in
marker availability and observer practices at different airports.
RECOMMENDED VISIBILITY INDICATOR

We recommend that visual range be used for trend analysis and reporting.
Although there are advantages and disadvantages associated with the use of
any indicator, visual range is most closely related to people's everyday
experience and would make the trends report useful to the largest number
of people (Bachmann, 1987).  Many researchers prefer bext because it is
directly related to aerosol concentrations and can easily be converted to
visual range through simple relationships such as the Koschmeider equa-
tion.  However, the assumptions implied in the Koschmeider equation are
often not valid and, as discussed earlier, some research has suggested
that different relationships would apply to different situations.  If we
find that the percentile analysis is the most useful approach for describ-
ing visibility, we will present graphs showing visual range on the left
y-axis and bext on the right.
 87110 7
                                    53

-------
         TABLE 7-1.   Advantages  and  disadvantages  of  components  recommended  for  visibility trends analysis.
         Trends  Analysis
           Components
      Recommendation
               Advantages
                          Disadvantages
         Data base
         Visibility
         Indicator
         Statistical
         parameter
NWS airport data
Visual range
10th. 25th, 50th. 75th.
90th percent lie visibility
                         Percent  frequency  less than
                         various  distances
Long period of record.  Large spatial  No data for Class I areas.  Visual range 1s
coverage.  Use for trends Is well      the only visibility Indicator reported; some
established                            differences among airports
Closest to what people see.
to what airport data report
Closest
Consistent with current Trends
Report.  Makes Interslte compari-
sons easier

Reports only what the data show; does
not require Interpolation of data
Not a good Indicator for visual features
such as contrast and texture.  Requires
assumptions to relate it to atmospheric
pollutant concentrations

Hust Interpolate most percentlles from
reported data
                                                                     Visibility categories may change over
                                                                     time due to changes In reporting practices
                                                                     or marker availability, making 1t diffi-
                                                                     cult to compare data between years.
                                                                     Inter-site comparisons are difficult
en
         Temporal
         features
Noontime observations
                         Annual, winter, summer
                          15-year trend time period
Uses the hour least likely to have
natural impairment

Analyzes seasons with most striking
trends while providing annual sum-
maries

Longer than the 10-year suggested
minimum; uses data after 1970, when
reporting practices changed
          Does not use all available daylight
          observations

          Other seasons may also have Important trends
                                                                     Not consistent with trend time period
                                                                     currently used 1n Trends Report.  Does not
                                                                     use all available data
        Meteorological
        screening
        Geograhlcal
        Area
Delete data for hours with
fog when relative humidity
> 85%. hours with wind-
blown dust, and hours with
precipitation
10 urban sites
Screens out data that may be more
representative of natural Impair-
ment than anthropogenic Impairment.
Leaves in data for high humidities
that also may have anthropogenic
impairment

Includes 10 of the 14 metropolitan
areas covered In Trends Report.
Sites in all but one EPA region
          Does not use all  available data
          May not be adequate to characterize entire
          United States; 1n particular, not adequate
          to characterize rural visibility
            87110 2

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CALCULATION AND DISPLAY OF TREND STATISTICS

In Section 4, we discussed several  methods of displaying trends in visi-
bility data.  Rather than recommend one specific approach at this time we
recommend applying two of the approaches discussed.  We will use the flux
method to show trends in reported visibility data, and we will also calcu-
late percentiles of the visibility distribution at each site and portray
these in box plots.  The percentiles of interest (10, 25, 50, 75, and 90)
will be calculated by linear interpolation.  For those cases in which
linear interpolation is not possible at the extremes of the distribution,
we will show a truncated box in the box plots rather than extrapolate a
percentile.

We will recommend which plots should be used in the national trends report
after examination of both sets of plots.  If both sets show the same gen-
eral trends, then we will recommend box plots that are consistent with the
data displays presented in the air quality sections of the Trends Report.
GEOGRAPHICAL AREAS RECOMMENDED FOR STUDY

In Section 1, four potential goals of visibility trends analysis were
identified, each suggesting a specific geographical focus for analysis.  A
visibility trends study might examine trends in urban, rural, or Class I
areas, or in some combination of these.  The level of visibility in each
of these three areas is important for a different reason.  Most trends
studies in the past have examined only rural and/or urban trends, largely
because of the lack of a long-term data base for Class I areas.

We recommend that the 1986 trends report contain visibility trends in
urban areas and that future trends reports summarize trends for urban,
Class I, and selected rural and other areas that may be of particular
interest.   Since large changes in emissions can quickly alter visibility
in a region, future trends reports should examine the effects of such
changes on rural or other areas.  For example,

     S02 emissions have decreased by 40 percent in parts of California
     since 1979;

     Smelter shutdowns and controls in Arizona, Seattle, and Utah since
     1970 have dramatically reduced S0£ emissions in these states;

     S02 emissions in the southeastern United States have increased sig-
     nificantly in the past 10 to 20 years.
 871107

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Specifically,  we recommend  that visibility trends be presented for each of
10 metropolitan statistical  areas chosen in conjunction with the EPA
project officer.  These areas are among the 14 currently used in the
Trends Report.  This will provide consistency with pollutant summaries and
allow comparisons of visibility trends and trends in pollutant concentra-
tions for these 10 areas.

Until a longer period of record for Class I areas is available, it will be
difficult to study visibility trends in these areas.  When such a record
is obtained, it will be necessary to formally resolve uncertainties aris-
ing from the assumptions used to convert instrument measurements to visual
range estimates.  It should also be recognized that trends can only be
developed for those Class I areas equipped with visibility monitors.  The
necessary level of detail  for Class I areas in the Trends Report may be
greater than is appropriate for urban or rural areas because of the spe-
cial value of visibility in these areas.  For example, visual range alone
may be a convenient, usable measure of visibility trends in urban areas,
but not in parks, where color and contrast may play more important
roles.  Thus it may be necessary to examine trends in Class I areas using
more than one visibility indicator.
RECOMMENDED TEMPORAL FEATURES OF TREND ANALYSIS

The temporal components of a trend analysis include the overall trend time
period (5, 10, 15, 50, years), the time period over which trends are sum-
marized (monthly, seasonally, annually, etc.), and the times of day used
in the analysis (daytime observations, noontime, etc).  Meteorological
variability is the fundamental consideration for choosing each of these
components.  Consideration of these components is necessary whether or not
trends are to represent only anthropogenic factors because annual, sea-
sonal, and diurnal meteorological variability can either imply anthropo-
genic trends that do not exist or obscure trends that do.
Overall Trend Time Period

It is possible that during a period of 2 to 3 years unusually favorable or
adverse meteorological conditions will occur that are not representative
of typical conditions.  As a result, it becomes necessary to choose  an
verall trend time period that brackets several such periods.  For example,
some visibility trends research based on airport data has analyzed trends
over relatively short periods such as 7 years (Miller, 1972), but most
studies have used at least 14 years (see Table 3-1 for examples).  The
results of our telephone survey indicate that 5 years is considered  to be
 87110  7

-------
present annual  trends in addition to seasonal  trends in order to examine
trends over all data and to provide a condensed summary consistent with
annual values reported in the Trends Report.

We recommend presenting trends for summer and  winter, along with annual
summaries.  We propose defining summer as July, August, and September, and
winter as December, January, and February.  Although the seasons thus
defined do not correspond to the calendar quarters often used in the
trends report, we believe that they best encompass those meteorological
conditions most representative of each season  (i.e., stability and wind
speed).
RECOMMENDATIONS FOR METEOROLOGICAL SCREENING PROCEDURES

Techniques for eliminating data for hours subject to natural visibility
impairment such as rain have been almost universally employed in studies
examining anthropogenic visibility trends.  Some of these techniques, such
as elimination of hours with precipitation from the data, are not contro-
versial; others, such as elimination of certain levels of humidity, are
more debatable.  As explained in Section 5, it is common to use a relative
humidity level of about 90 percent as a cutoff point for the elimination
of data due to humidity because of the way in which aerosols are formed
and the difference between natural fog and anthropogenic haze.  However,
in California, the visibility standard is based on a 70 percent relative
humidity level.  We recommend deleting data for hours with fog when rela-
tive humidity is greater than 85 percent.  We recommend using 85 percent
as a cutoff point on the basis of our preliminary investigation of the 10
urban sites selected for study.  Our screening analyses showed a high
degree of correlation between the reporting of fog as an impairment to
visibility and an RH of 85 percent or greater.  Data should also be
deleted for hours during which there is precipitation or when windblown
dust is recorded.  This approach would remove from the analysis, to the
extent possible, those hours characterized by visibility impairment due to
natural phenomena.
MINIMUM DATA REQUIREMENTS

The EPA establishes minimum data requirements for sites to be  included  in
an air quality trends analysis.  These data requirements  specify  a minimum
number of years of continuous data as well as the minimum amount  of data
in each year to be obtained.  These requirements are essential  because  of
the frequency of missing air quality data and the differing  lengths of  the
monitoring records.  For airport visibility data, however, we  need not  be
as concerned about the effects of missing data.  All airport data sets
 87110  7                             57

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under consideration have a data record of decades and data are rarely
missing.  However, we will omit observations when meteorological factors
(fog, precipitation, or windblown dust) result in low visibility.  There-
fore, we need to establish minimum data requirements in case many observa-
tions are excluded for meteorological reasons.  We recommend that a month
be considered representative if at least half of the days in the month are
included in the analysis, and that a season be considered valid if all
three months are representative.
 87110  7
                                     58

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Keith, R. W.   1970.   "Downtown Los Angeles Noon Visibility Trends 1933-
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Latimer, D. A., G. E. Anderson, H. Hogo, R. G. Ireson, R. E. Morris, and
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Malm, W. C.  1987a.  National Park Service, personal communication.

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Malm, W. C., and E. G. Walther.   1980.  "A Review of  Instruments Measuring
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Malm, W. C., and J. V. Molenar.   1984.  Visibility measurements  in
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     Detroit, Michigan,  June 1985.
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                                Appendix

                  SELECTED  INSTRUMENT-BASED VISIBILITY
                  AND FINE PARTICLE MONITORING NETWORKS
87110 11
                                    66

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                                 Appendix

                   SELECTED INSTRUMENT-BASED VISIBILITY
                  AND FINE PARTICLE MONITORING  NETWORKS
INSTRUMENT-BASED VISIBILITY MONITORING NETWORK

Several other instrument-based visibility monitoring efforts have been
carried out in the recent past; however,  each of these studies are limited
in their spatial and temporal  coverage.  Some  examples include the EPRI
Eastern Regional Air Quality Study (ERAQS) and Western Regional Air
Quality Study (WRAQS),  the EPA Regional Air Pollutants Study (RAPS); the
Subregional Cooperative Environmental  Protection Agency, National Park
Service, and Electric Utilities Study  (SCENES); Research on Operations
Limiting Visual Extinction (RESOLVE);  the Pacific Northwest Regional Aero-
sol Measurement Apportionment Study (PANORAMAS); and Visibility Impairment
due to Sulfur Transport and Transformation in the Atmosphere (VISTTA).
EPRI Eastern Regional Air Quality Study (ERAQS)

For nine months in 1979 and 1980, visibility data were collected at two
sites in Pennsylvania and Ohio using telephotometry, nephelometry,
photography, and human observations (Tombach and Allard, 1983).
EPRI Western Regional Air Quality Study (WRAQS)

For 22 months in 1981 and 1982, visibility data were collected at 11 sites
in the western United States using telephotometry, nephelometry, and human
observations (Tombach et al., 1987).  Aerosol sampling and analysis and
meteorological measurements were also performed (EPRI, 1987).
EPA RAPS

The EPA operated a 20-site visibility and fine particulate network using a
ground and aircraft-based measuring system in the St. Louis area for
approximately three years from 1975 through 1977.  Nephelometers were used
to estimate the scattering coefficient.

87110  11
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SCENES

Visibility and particulate measurements  have been continuing since 1983 at
6 sites in the Southwest using teleradiometry,  photography, nephelometry,
and particulate samplers.  This network  has  been operated jointly by the
NPS, EPA, EPRI, Salt River Project,  Southern California Edison, and
Department of Defense.
RESOLVE;

Visibility measurements have been made since August 1983 at 7 sites in the
California desert in an attempt to characterize contributing sources to
regional haze at Department of Defense sites.  Three sites were in source
areas of Southern California; and four in receptor areas in the Mohave
desert.  The study used particulate samplers, nephelometers, and tele-
radiometers.
PANORAMAS

A joint study by EPA Region X, and the states of Oregon, Washington, and
Idaho consisting of 26 sites located in these states.  Data was collected
from July to the beginning of October in 1984 with nephelometers, fine
particulate monitors, cameras, and a four telephotometers (Berg, 1986).
Short-term intensives

Visibility measurements have been made at a variety of locations for
limited periods (much less than a year) as parts of various studies
designed to evaluate visibility and/or aerosol conditions and relation-
ships.  Such short-term studies were carried out in Denver, Detroit, Hous-
ton, Los Angeles, Shenandoah Valley, Research Triangle Park, Louisiana
Gulf Coast, Atlantic Coast, and Great Smoky Mountains.  Details are pro-
vided in the review by Mathai and Tombach (1987).
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FINE PARTICLE AND SULFATE  MONITORING  DATA

National  Air Surveillance  Network  (NASN)

This network measured TSP  and sulfur  from 1956 to 1975 at 46 urban and 20
rural sites throughout the United  States. Most of these data have been
entered into the SAROAD system.


EPA Inhalable Particulate  Network  (IP)

This network measured particulate  concentrations in three size ranges from
1978-1984:  0-2.5, 0-15, and 15-30 micrometers.  Samples were collected at
163 sites in the United States.  These  data have known biases due to the
use of glass fiber filters.


EPRI SURE (Sulfate Regional Network)

This network was operated  by the EPRI at 54 stations throughout the north-
eastern United States during 1977  and 1978.  Only nine stations operated
continuously; the remainder operated  for only one month per season. Total
suspended particulate concentrations  and sulfate and nitrate fractions
were measured.


EPA Ohio River Valley (ORV) Aerosol Study:

Three monitoring stations were operated from May 1980 to August 1982 in
the Ohio River Valley.  Both fine and coarse aerosols were collected.


EPA Western Particle Characterization Study (WPCS);

The EPA also collected fine and coarse particulates and performed chemical
analyses on samples from 40 sites in urban areas of the United States from
July 1979 to September 1981.
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