United States Office of Air Quality EPA-450/4-84-008
Environmental Protection Planning and Standards August 1983
Agency Research Tnangle Park NC 27711
__ ,
Analysis Of Particulate
Matter Concentrations
And Visibility In The
Eastern United States
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EPA-450/4-84-008
Analysis Of Paniculate Matter
Concentrations And Visibility In The
Eastern United States
By
John Trijonis
Santa Fe Research Corp.
EPA Contract No. 68-02-3578
Thompson Pace, Project Officer
Office Of Air And Radiation
Office Of Air Quality Planning And Standards
U.S. Environmental Protection Agency
Research Triangle Park, North Carolina 27711
August 1983
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This report has been reviewed by the Office Of Air Quality Planning
And Standards, U. S. Environmental Protection Agency, and approved
for publication as received from Santa Fe Research Corporation.
Approval does not signify that the contents necessarily reflect
the views and policies of the Agency, neither does mention of
trade names or commercial products constitute endorsement or
recommendation for use.
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ABSTRACT
For the eastern half of the U.S., an analysis is conducted of EPA IP
Network data for participate concentrations combined with airport data for
visibility and relative humidity. Physically meaningful and statistically
well-fitting regression equations are used to determine light extinction
(visibility degradation) as a function of aerosol concentrations and
relative humidity. As expected from optical theory, the results indicate
that fine particles (FP) are much more closely tied to visibility than
inhalable particles (IP) or total suspended particles (TSP). It is found
that sulfate particles have a much greater extinction efficiency (per unit
mass) than nonsulfate TSP and nonsulfate IP; fine sulfate particles also
appear to have a somewhat greater extinction efficiency than fine non-
sulfate particles. The results suggest that sulfates and associated water
account for approximately 45% of total light extinction in the East. It
is found that dichotomous FP and IP concentrations can be predicted fairly
well from airport visibility data in conjunction with Hi-Vol TSP data,
with standard errors of prediction about 30-40% on a daily basis and
16-17% on an annual basis. Many of the above statistical studies would
benefit greatly by the inclusion of more data when available. It is shown
that airport visibility data are of good quality for analyzing the spatial/
temporal extent of fine particle episodes in the East. Visibility maps
provide important qualitative insights regarding the spatial extent,
transport patterns, and origins of fine particle episodes.
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'IV
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TABLE OF CONTENTS
ABSTRACT iii
EXECUTIVE SUMMARY 1
1. INTRODUCTION 9
1.1 Basic Concepts and Definitions 10
1.2 Organization of the Report 14
2. DESCRIPTION OF DATA BASES 15
2.1 Types of Data 15
2.2 Data Quality Assurance 17
2.3 Data Base for Development Phase 19
2.4 Data Base for Application Phase 23
3. RELATIONSHIPS BETWEEN PARTICULATE CONCENTRATIONS AND VISIBILITY . 27
3.1 Elimination of Variables and Functional Forms 27
3.1.1. Motor Vehicle Particles 27
3.1.2 Fine Versus Coarse Fractions from Dichotomous
Samplers 29
3.1.3 Relative Humidity and the Role of Aerosol Water ... 31
3.2 Light Extinction from Aerosol Concentrations 35
3.2.1 Variable Definition and Regression Methodology. ... 35
3.2.2 Overall Correlation Levels 37
3.2.3 Regression Results 41
3.2.4 Conclusions Regarding Issues of Concern 43
3.2.5 Geographical Variations 46
3.2.6 Limitations of the Analysis 49
3.3 Prediction of Dichotomous FP and IP from Airport Data
and Hi-Vol Data 50
3.3.T" Prediction of FP 52
3.3.2 Prediction of IP 56
4. CHARACTERIZATION OF PARTICULATE EPISODES USING AIRPORT DATA ... 59
4.1 Information Base 59
4.2 Episode Case Studies 61
4.2.1 30 June 1980 to 6 July 1980 61
4.2.2 11 July 1980 to 18 July 1980 72
4.2.3 24 July 1980 to 3 August 1980 *. . 82
4.2.4 20 January to 22 January 1981 96
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4.3 Potential Uses for the Methodology 98
5.0 REFERENCES 101
APPENDIX A A-l
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EXECUTIVE SUMMARY
In accordance with the provisions of the 1977 Clean Air Act Amendments,
EPA is presently reviewing the National Ambient Air Quality Standard for sus-
pended particulate matter. Based on this review, the present standards for
total suspended particles (TSP £ 50 ym) may be supplemented or replaced with
standards for inhalable particles (IP < 15 urn), 10 micron particles
(PM10 < 10 ym), and/or fine particles (FP<2.5 ym). In order to assist in
the standards review and to provide information on current particulate concen-
trations, EPA has established a major monitoring system, the EPA IP Network,
throughout the United States.
The EPA IP Network is providing Hi-Vol sampler data for TSP and dichot-
omous sampler data for IP and FP at nearly 200 locations on a six-day sampling
schedule. As part of a program to start making use of this large data set,
EPA contracted with Santa Fe Research Corporation to analyze the currently
available particulate data for the Eastern U. S. in conjunction with airport
data for visibility and relative humidity. The purpose of this study is to
improve the understanding of visibility/aerosol relationships and long range
particle transport in the East.
The man-made haze that covers the East may be an important welfare effect
to be considered in setting Secondary National Ambient Air Quality Standards for
particulate matter. The first major objective of this study is to improve our
technical knowledge of the Eastern haze, specifically with respect to the
relationships among visibility, particle size distribution, and particle
chemical composition. Major questions to be addressed are as follows: How
much better does FP relate to visibility than IP or TSP? How does the chemical
composition of fine particles (in particular the sulfate fraction) affect
visibility? Are the Eastern IP Network data consistent with other studies
showing that sulfate aerosols dominate visibility reduction in the East?
As an auxiliary to the first objective, we also address the problem
of predicting FP and IP concentrations from airport visibility data and
Hi-Vol TSP data. Such predictions would be very useful in expanding the his-
torical record for FP and IP to cover more regions and more years.
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The second major goal of this study is to show how airport visibility
data can be used to supplement the EPA IP Network data in short-term episode
studies. Because EPA IP Network data are rather sparse geographically,
and because the frequency of collection is only every 6th day, considerable
gaps exist in the particulate data. These data gaps hinder a full understanding
of fine particle transport and of the spatial/temporal extent of fine particle
concentrations. In this study, we show that routine airport data can be used
qualitatively to fill in the spatial/temporal gaps in the EPA IP Network data and
thereby provide a better understanding of fine particulate concentrations and
large scale transport.
Before we present our results, it is worthwhile to discuss some basic
definitions and concepts for those readers who are not well versed in visibility
studies. In this report, we define "visibility" as "visual range," the farthest
distance that an observer could distinguish a large dark object against the
horizon sky. We also use a more technically precise term, "extinction
coefficient," the fraction of light that is attenuated per unit distance as a
light beam traverses the atmosphere. In a uniform atmosphere, it can be shown that
visual range is inversely proportional to the extinction coefficient, with a
constant of proportionality depending on the contrast detection threshold of the
observer. Also, it is known from basic principles that the extinction coefficient
is a simple linear sum of four components: light scattering by air molecules
(blue-sky scatter), light absorption by gases (essentially all from NC^), light
scattering by particles (usually dominated by sulfates and other fine particles),
and light absorption by particles (basically all from elemental carbon). In the
Eastern U.S., the particulate contribution to the extinction coefficient, in
particular light scattering by particles, generally far exceeds the gaseous
contribution to the extinction coefficient.
Description of Data Bases
Three types of data are used in this report: visibility observations from
airports, relative humidity readings from airports, and particulate measurements
from the EPA IP Network. The latter include all Hi-Vol and dichotomous sampler
measurements of particulate mass, sulfate, and lead that were recorded by the
EPA IP Network through the middle of 1981. The quality of the airport data for
visibility and relative humidity is assured in four ways: (1) restricting the
visibility data to daylight readings each day, (2) limiting the airports to
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Class-1 weather stations, (3) conducting a telephone survey to make sure that
all airports used have adequate visibility marker systems, and (4) identifying
possibly erroneous data using histograms of single variables and scatterplots
of correlated variables. We also formulate and apply data quality screens
that eliminate highly dubious values from the EPA IP Network data. The parti-
culate data quality screens are based on both physical and statistical considera-
tions.
The data base for the first part of this study — development of relationships
between particulate concentrations and visibility — consists of daily particulate
measurements from 14 EPA IP Network sites combined with corresponding visibility
and relative humidity observations from nearby airports. The 14 EPA IP
Network sites are located in 6 metropolitan areas: Birmingham (5 sites),
Washington, D.C. (1 site), Boston (2 sites), Minneapolis (2 sites), Buffalo
(2 sites), and Philadelphia (2 sites). Using data from 8 of these sites, we assembl
a "development" data set to analyze the relationship between particulate concen-
trations and visibility. This development data set contains 254 daily values,
approximately one-third of which include chemical composition measurements for
the particulate samples (SOT and Pb). Considering that the data cover nearly
one and one-half years (beginning of 1980 to the middle of 1981), the sample
size is not extremely large; there are four reasons for this: (1) some sites
were not operational for the full one and one-half years, (2) the particulate
data were taken only every sixth day (every 12th or 24th day for chemical
constituents), (3) there were missing values within the routine particulate
sampling schedule, and (4) certain days were eliminated based on weather con-
siderations. Using data for the other six sites, we create an "independent test"
data base of 197 daily values to be used for checking the conclusions based on
the development data set. Furthermore, with data from all 14 locations,
an "annual mean" data base is formulated to test predictive formulas for
FP and IP on a yearly average basis.
In the second part of this study -- characterization of particulate episodes --
the particulate data and airport data are used separately. First, maps are
prepared listing particulate concentrations at EPA IP Network sites in the
Eastern U.S. during the period April 1980 to March 1981. These maps allow us
to identify particulate episodes. Then, visibility data from 70 carefully
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selected airports in the East are used to investigate the participate episodes.
The airport data provide greater resolution than the EPA IP Network data both
spatially and temporally.
Relationships Between Particulate Concentrations and Visibility
We find that it is possible to formulate physically meaningful and statis-
tically well-fitting regression equations that predict light extinction levels as
a function of aerosol concentrations and relative humidity. In these equations,
it is important to include relative humidity as a multiplicative factor of the
form (1-RH)" on the aerosol concentrations. Such an approach correctly includes
water as an integral part of the particulate concentrations and correctly
approximates the hyperbolic dependence of aerosol water on relative humidity.
As expected from theoretical principles, we find that fine particles cor-
relate much better with extinction than inhalable particles, which in turn
correlate much better with extinction than total suspended particles. The best
statistical fits are achieved with regression models that include nonlinear
relative humidity effects and that distinguish sulfate particles from nonsulfate
particles. Our results imply that, if welfare standards are to be established
to protect visibility, such standards should be set for FP and/or sulfates,
not TSP, IP, or PM10.
The extinction efficiency (per unit mass) for sulfate aerosols is much
greater than the extinction efficiency for the nonsulfate portion of TSP and IP.
The extinction efficiency for fine sulfate particles also appears to be somewhat
greater than the extinction efficiency for fine nonsulfate particles (an obvious
exception, on theoretical grounds, being fine elemental carbon). Our.results
suggest that, on the average, sulfates and associated water account for about
45% of total light extinction in the East. This estimate is subject to uncertain-
ties and is somewhat lower than other estimates of 50 to 75% published in the
literature.
The small contribution to extinction from coarse particles cannot be
precisely quantified with the limited data currently available from the EPA IP
Network. The same conclusion holds for the small contribution from fine primary
vehicular particles (estimated using Pb as a tracer element). The extinction
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properties of primary vehicular particles should be given emphasis in future
studies because elemental carbon particles from increased diesel use may cause
a national deterioration in visibility over the next twenty years.
The inverse process of predicting fine particle concentrations as a
function of visibility and relative humidity is less physically straightforward
and less statistically well-fitting than the direct process discussed above.
Although visibility is a good qualitative indicator of fine particle and sulfate
concentrations, we find that airport data alone cannot be used as a precise,
quantitative substitute for fine particle monitors, especially on a daily
basis. Fine particle concentrations can be predicted from airport visibility
data with a standard error of 45-60% on a daily basis and 25% on an annual
mean basis. Although TSP alone also is a poor predictor of FP, TSP in
conjunction with airport visibility yields more reasonable estimates of FP,
with errors potentially as low as 40% on a daily basis and 16% on an annual
basis. Adding relative humidity data does not significantly improve pre-
dictive accuracy. We further find that airport data is a poor predictor of
IP,.but that TSP alone is a fairly good predictor of IP (30-35% daily errors
and about 17% annual errors).
Many of our conclusions can be made more certain and more precise when more
data become available from the EPA IP Network. Specific issues that can be
quantified more accurately are as follows: (I) the extinction efficiency of coarse
particles, (2) the extinction efficiency of primary vehicular particles, (3) the
relative extinction efficiencies of fine sulfates and fine nonsulfates,
(4) geographical variations in aerosol extinction efficiencies, and (5) the
coefficients and errors for equations predicting FP and IP from airport
data and TSP data.
Characterization of Particulate Episodes Using Airport Data
Airport visibility data are of fairly good quality and utility for study-
ing the spatial/temporal extent of fine particle episodes in the East. For
several case studies in the summer of 1980, we find that the geographical
distribution of haziness (based on airport data) generally agrees with the
geographical distribution of fine particle concentrations. For example.
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Figure 1 shows a visibility map on a day when extremely high fine particle
concentrations were recorded in the Ohio Valley. Furthermore, the day-to-
day movement of the haziness generally appears to agree with large scale
wind patterns.
Studies with large scale visibility maps can be useful, in an overall sense,
by establishing the general need for interstate considerations in control policies
for fine particles and sulfates. Also, whenever a specific fine particle episode
is analyzed with respect to control requirements for short-term standards,
visibility maps can provide important qualitative insights regarding the spatial
extent, transport patterns, and origins of the episode. Because visibility data
are routinely available, such data can be incorporated in episode studies at very
1ittle cost.
The main drawback of the visibility mapping technique is that it is not
quantitative. Visibility maps do not serve as an accurate substitute for fine
particle monitors, nor do they provide a quantitative estimate of ambient con-
tributions from local sources versus ambient contributions from large scale
transport.
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V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded
light and variable winds
moderate flow
and persistent flow
xr--^
Figure 1 Visibility map for 26 July 1980.
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1. INTRODUCTION
In accordance with the provisions of the 1977 Clean Air Act Amendments,
EPA is presently reviewing the National Ambient Air Quality Standards for
suspended particulate matter. It is anticipated that this review may result
in new standards for specific size fractions of particulate matter. That is,
the present standards for total suspended particles (TSP < 50ym) may be
supplemented or replaced with standards for inhalable particles (IP< 15 um),
ten-micron particles (PM10 < 10 urn), and/or fine particles (FP < 2.5 ym). In
order to assist in the standards review, to provide information on current
particulate concentrations, and to facilitate future implementation planning,
EPA has established a major monitoring system, the EPA IP Network, throughout
the United States.
The EPA IP Network is providing Hi-Vol sampler data for TSP and dichotomous
sampler data for IP and FP at nearly two hundred locations. The particulate
samples are being collected every sixth day, with chemical analyses performed
every twenty-fourth day. As part of a program to start making use of this
large data set, EPA has contracted with Santa Fe Research Corporation to study
the currently available particulate data for the Eastern U.S. in conjunction
with airport data for visibility and relative humidity. The purpose of the
study, for which this document is the final report, is to improve the understand-
ing of visibility/aerosol relationships and long-range particle transport in
the East.
Visibility degradation is an important welfare effect that must be
considered in setting Secondary National Ambient Air Quality Standards for
particulate matter (EPA 1982). The issue of particula-te standards and
visibility has special importance in the East, where a rather dense man-made
haze occurs (Trijonis 1982a). The first major goal of this study is to
improve our knowledge of the relationships among visibility, particle size
distribution, and particle chemical composition in the East. Specific
questions to be addressed are as follows: How much better does FP relate
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to visibility than IP or TSP? How does the chemical composition of fine
particles (in particular the sulfate fraction) affect visibility? Are the
Eastern IP Network data consistent with other studies showing that sulfate
aerosols dominate visibility reduction in the East?
There is another statistical problem that is closely related to the
first objective. This problem concerns quantitative predictions of FP and
IP from airport visibility data and Hi-Vol TSP data. Such predictions
would be useful in expanding the historical record for FP and IP to include
many more regions and years. This problem is addressed as an auxiliary
to the first objective.
The second major goal of this study is to show how airport visibility
data can be used as a qualitative supplement to the EPA IP Network data in
short-term studies (e.g. episode analyses). Because EPA IP Network data
are rather sparse geographically, and because the frequency of collection
is only every sixth day, considerable gaps exist in the particulate data.
These data gaps hinder a full understanding of fine particle transport and
of the spatial/temporal extent of fine particle concentrations. It is
important to try to improve this understanding because attainment of
particulate standards could be significantly affected by the long-range
transport of fine particles, especially in the Eastern United States. In
this study, we show that routine airport data can be used qualitatively
to fill in the spatial/temporal gaps in the EPA IP Network data and thereby
provide a better understanding of fine particle patterns and large-scale
transport.
1.1 BASIC CONCEPTS AND DEFINITIONS
The definitions for the particulate (aerosol) parameters used in this
study are fairly straightforward. The term total suspended particles (TSP)
refers to the mass of particles measured by a Hi-Vol sampler (those particles
less than approximately 50 pm in aerodynamic diameter). Inhalable particles
(IP) and fine particles (FP) refer to dichotomous sampler measurements with
size ranges <15 urn and <2.5 ym, respectively. Coarse particles (CP) denotes
IP minus FP, particle mass in the range 2.5 to 15 ym. In this study, we also
use chemical composition data for particulate sulfate ton (SOT) and particulate
lead (Pb). • .
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The basic concepts of visibility deserve a more extended introductory
discussion because they are not as straightforward and because they may not
be familiar to the reader. Generally, visibility refers to the clarity of the
atmosphere. Visibility can be defined quantitatively in terms of discoloration
(wavelength shifts produced by the atmosphere), contrast (the relative bright-
ness of visible objects), and/or visual range (the farthest distance that one
would be able to distinguish a large black object against the horizon sky).
Because this study is based on weather-station measurements of visual range,
we will define visibility as visual range and will use the two terms inter-
changeably. It should be noted that the concept of visual range makes most
sense in situations of large-scale homogeneous haz-e, which is the type of
visibility phenomenon addressed in this report.
Visibility through the atmosphere is restricted by the absorption and
scattering of light by both gases and particles. The sum of absorption and
scattering is called total extinction which is measured by the extinction
coefficient "B". The extinction coefficient represents the fraction of light
that is attenuated per unit distance as a light beam traverses the atmosphere.
In a homogeneous atmosphere, visibility is inversely proportional to extinction;
the Koschmeider formula expressing this relationship is:
B=-f" (1-1)
The constant in Equation (1-1) is usually chosen to be k = 3.9 or k = 3.0,
depending on whether one assumes a 2% or 5% contrast detection threshold for
the observer. As explained later in Section 3.2.1, a Koschmeider constant
of 3.0 is most appropriatt when using airport visibility data.
Figure 1.1 presents a map of annual median visual range for nonurban areas
of the United States. It is obvious that the eastern one-half of the U.S.,
the study area for this project, has much lower visibility than most of the
West. Although some parts of the study area (e.g., northern New England or
northern Minnesota) experience moderate visibility levels (^25 miles), median
visual range is generally 10-15 miles in areas east of the Mississippi and south
of the Great Lakes. For a Koschmeider constant of 3.0, this range corresponds to
a median extinction coefficient of 1.2 - 1.9 10" m (the conventional units for
extinction).
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It is often preferable to discuss visibility in terms of extinction
coefficient rather than visual range because the extinction coefficient can
be linearly subdivided into contributions from various atmospheric components.
In general , total extinction is a linear sum of four terms:
B = BRayleigh + BAb-Gas + BScat-Part + BAb-Part * (1"2)
An explanation of each of these terms is as follows:
BR , . , = light scattering by air molecules (Rayleigh or blue-
sky scatter). This term is on the order of .10 to .12
(10 m)~ depending on altitude (i.e. depending on the
density of air); it would restrict visibility to approx-
imately 155-190 miles (for a 5% contrast) if all particles
and pollutant gases were absent.
BAb-Gas = ^'9nt absorption by gases. Nitrogen dioxide (NOp) is the
only prevalent gaseous pollutant that is a significant
light absorber. Although concentrations of NO^ are usually
not large enough to produce significant reductions in overall
visual range, N0~ can produce significant brownish discolora-
tion because it preferentially absorbs blue light.
o
Scat-Part = light scattering by particles (aerosols). In most cases,
this is the dominant part of total extinction. As shown
later in Figure 3.1, light scattering per unit mass of
particle depends strongly on particle size; particles in the
0.1 to 1.0 micron size range are much more efficient light
scatterers than particles outside that size range. Signifi-
cant contributions to light scattering can be made by many
types of particle: particles emitted from fuel combustion,
particles emitted from vegetative burning, fine dust parti-
cles, and secondary aerosols (aerosols formed from gas-to
particle conversion in the atmosphere). Secondary aerosols,
especially sul fates, are often major contributors to light
scattering because they tend to form in the O.i to 1 micron
13
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size range. Hygroscopic particles — again sulfates are
the obvious example — often attract a substantial volume
of water into the particle phase, thereby further in-
creasing aerosol light-scattering.
B.. p . = light absorption by particles. The most important light
absorping aerosol is usually graphitic carbon. Light
absorption by aerosols can be substantial in those urban
areas where soot contributes a significant fraction of
the aerosol.
1.2 ORGANIZATION OF THE REPORT
This report is organized into four chapters. This chapter has
provided a statement of objectives and an introduction to the basic
technical concepts of visibility. Chapter 2 describes the data bases used
in the project. Chapter 3 deals with the first major objective of the
study — exploring the statistical relationships between particulate
concentrations and visibility. The second major objective — using airport
visibility data to investigate the spatial extent and transport patterns
of fine particle episodes -- is addressed in Chapter 4.
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2. DESCRIPTION OF DATA BASES
This chapter discusses the data sets that serve as the basis for this
report. Section 2.1 reviews the various types of measurements included in the
data sets. Section 2.2 discusses data quality considerations. The specific
data bases for the development and application phases of this project are
summarized in Sections 2.3 and 2.4, respectively.
2.1 TYPES OF DATA
Three types of data are used in this report -- visibility observations
from airports, relative humidity readings from airports, and particulate
measurements from the EPA IP Network. Table 2.1 lists the specific parameters
included, as well as their averaging times and units.
The visibility data consist of "prevailing visibility" readings made by
human observers at weather stations (airports). According to National Weather
Service procedures, prevailing visibility is defined as the greatest visual
range that is attained or surpassed around at least half of the horizon circle,
but not necessarily in continuous sectors (Williamson 1973). Daytime visibility
is measured by observing markers (e.g. buildings, mountains, towers, etc.)
against the horizon sky; nighttime visibility measurements are based on un-
focused, moderately intense light sources. Because our experience indicates
that daytime and nighttime observations are often incompatible, and that day-
time data are usually of higher quality (Trijoin's and Yuan 1978; Trijonis 1979),
only daytime observations are employed in this study.
Weather observers usually perform visibility measurements each hour, but
only the readings from every third hour are contained in the formal data reports
published by the National Climatic Center. The visibility readings that we
used were the observations for 7:00 AM, 10:00 AM, 1:00 PM, and 4:00 PM Standard
Time in the Eastern Time Zone, and 6:00 AM, 9:00 AM, noon, and 3:00 PM Standard
Time in the Central Time Zone.
The relative humidity readings were obtained for the same airports and for
the same four daytime hours as the visibility observations. All the airport
data were acquired from the National Climatic Center in hard-copy form, monthly
summaries of Local Climatological Data (monthly LCD's). The daily visibility
and relative humidity recordings were averaged manually, with the 4-hour means
then transcribed into computerized form.
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TABLE 2.1 PARAMETERS INCLUDED IN THE DATA BASE
PARAMETER (SYMBOL)
UNITS
AVERAGING TIME
Airport Weather Data
Visual Range (V)
Relative Humidity (RH)
EPA IP Network Particulate Data
Hi-Vol mass (TSP)
Hi-Vol sulfate (TSPSO^)
Hi-Vol lead (TSPPb)
Dichotomous inhalable mass (IP)**
Dichotomous inhalable sulfate (IPS07)
Dichotomous inhalable lead (IPPb)
Oichotomous fine mass (FP)***
Dichotomous fine sulfate (FPSOT)
Dichotomous fine lead (FPPb)
miles 4 daylight hours
none(fraction) " " '
ug/nT
24 hours
7:00 AM/10:00 AM/1:00 PM/4:00 PM EST and 6:00 AM/9:00 AM/noon/3:00 PM CS"
Inhalable measured as ±15 urn.
Fine measured as —2.5 urn.
^ ,-
ib
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The particulate data were acquired from EPA in computerized form
(magnetic tape). These data consisted of all Hi-Vol and dichotomous sampler
measurements of particulate mass, sulfate, and lead that had been processed
for the EPA IP Network as of February 1982. The available particulate data
covered the approximate time period of the middle of 1979 to the middle of
1981, although the specific start and end dates of the data varied greatly
(by many months) from site to site.
It should be noted that the EPA IP Network basically involves sampling
every sixth day, simultaneouly over the entire network. Chemical analyses
for sulfate, lead, and other species are conducted on every fourth sample
(e.g. every 24th day), although chemical analyses have been performed on
every other sample (every 12th day) at most sites during the first six months
of station operation.
Because the Hi-Vol data prior to 1980 were taken using quartz fiber
filters, and because such data are not necessarily equivalent to the subse-
quent Hi-Vol measurements on glass fiber filters, we restricted our analyses
to the years 1980 and 1981. To insure consistent periods for our analyses with
and without chemical composition data, we decided to limit the data at each
site to the period for which chemical composition measurements had been processed
(often deleting a slightly extended period containing data on particulate
mass but not on particulate chemical composition). Furthermore, because our
data quality screens for the particulate data are based on comparisons of
the Hi-Vol and dichotomous sampler measurements, we restricted our data sets
at each site only to days reporting Hi-Vol TSP as well as dichotomous IP and FP.
2.2 DATA QUALITY ASSURANCE
Several previous studies (Trijonis and Yuan 1978; Trijonis 1979, 1980,
1982c; Trijonis et al. 1982; Cass 1979; Leaderer and Stolwijk 1979) have shown
that airport data are of good quality for use in characterizing visibility/
aerosol relationships. The quality of the data are indicated by the high
correlations (typically 0.7 to 0.9) obtained in relating airport visibility
data to independent particulate and/or meteorological measurements. Some of
the limitations of airport visibility data and the resulting implications
for our statistical analyses are discussed later in Section 3.2.6.
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To help assure the quality of the airport visibility and relative humidity
data used in this study, three steps were taken. First, the airports considered
were restricted to Class-1 weather stations operated by National Weather
Service personnel; because of the functional priorities, such stations likely
have higher quality weather data than lesser class stations or stations
operated by FAA and military personnel,. Second, a telephone survey was con-
ducted to insure that all airports used had adequate visibility marker systems.
Third, outlier or unusual values for visibility and relative humidity were
identified using histograms of single variables and scatter plots of correlated
variables. All unusual values and many normal values were checked against
the original hard-copy NCC forms to eliminate manual transcription errors.
A statistical screening test of the particulate data has already been
conducted by EPA. This test flags a significant number of data points
(Rodes 1981). However, EPA only eliminates a data point when there is an
obvious cause for a measurement error; as a result, most of the questionable
points are left in the data base. For example, recordings remained for certain
sites and days with IP values two to five times greater than TSP values.
Because TSP essentially represents the mass of particles less than 50 urn in
diameter while IP only represents the mass of particles less than 15 urn in
diameter, and because TSP also tends to be inflated over IP due to "artifact"
collection of gases on Hi-Vol filters, such recordings are obviously unreasonable.
We decided to eliminate these and other highly unreasonable data points by
formulating our own data quality screening procedure.
Our data quality screens (see Table 2.2) are based on ratios of simul-
taneous Hi-Vol and dichotcmous sampler data. We selected cut-offs for these
ratios using both physical and statistical considerations. The physical con-
siderations involved what would be reasonable given the particle size ranges
measured by the samplers and the possibility of artifacts with the Hi-Vol samplers.
The statistical considerations were based on identification of outliers in
simple histogram plots of the ratios. We chose the cut-offs so that only the
most egregious outliers., at most a few percent of the data, would be eliminated.
Table 2.2 indicates the percent of data deleted by the data quality screens.
About 2?i of the data were eliminated entirely; of the chemical data in the
remainder, about 4* of the sulfate data and about &% of the Pb data w,ere
eliminated by screens on the chemical constituents.
18
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TABLE 2.2 DATA QUALITY SCREENS APPLIED TO
THE EPA PARTICULATE DATA BASE
DATA QUALITY SCREEN PERCENT DATA ELIMINATED
Screens on particulate mass measurements.
1. IP/TSP > 1.5 1.4%
2. TSP/IP > 4 and FP/IP > 0.6 .3%
Percent total data points eliminated: 1.7%
Screens on chemical constituent measurements
3. IPSOj/TSPSO^ < .25 or > 1.5 4.4%
4. IPPb/TSPPb < .5 or > 2.5 when 3 8.1%
either IPPb or TSPPb > 0.2 ug/m
2.3 DATA BASE FOR DEVELOPMENT PHASE
The development phase of this project (Chapter 3) will involve regression
studies interrelating atmospheric extinction coefficient (determined from
visibility data), relative humidity, and particulate concentrations. The re-
lationship will be developed using simultaneous daily measurements of particulate
parameters at EPA IP Network sites and weather parameters at nearby airports.
Locations were selected for this analysis based on the following criteria:
• The locations are in the eastern U.S., up to one tier of states west of
the Mississippi River.
• The EPA IP Network sites have a relatively large number of daily parti-
culate measurements, including an adequate amount of chemical composition
data.
• The period of the particulate data is not extremely biased seasonally,
e.g. the available data are not for only one season.
« The EPA IP Network sites are located near an airport weather station,
not more than 15 km away, preferably less than 10 km away.
• The airport weather data include relative humidity.
• The airoorts have adequate sets of visibility markers and are preferably
Class-i weather stations.
19
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Based on the above criteria, we selected 14 locations in 6 air basins
(see Table 2.3). The selection of these locations was a rather obvious choice.
All of the sites in Table 2.3 measure up very well against the criteria, while
all other potential sites seriously fail the criteria.
A decision was required concerning how to handle the situation of more
than one site in a given air basin. The data bases for the sites in a given
air basin are partly redundant in the sense that some of the sampling dates
are duplicated and in the sense that the same airport weather data are used
(except in Philadelphia, where two different airports are considered). The data
bases are not fa] ly redundant, however, because the particulate data are unique
to each site and because some of the sampling days are independent (because one
particulate site was operating while another was not). We decided to handle
this situation by creating two data bases — a data base to develop the relation-
ships and a data base to test the relationships. In each air basin, we chose the
site with the greater amount of data for development and the site with the lesser
amount of data for testing. The Birmingham area created a special problem
because of five particulate sites. We chose three of these sites (010380003,
010570001, and 012540001) for development data and two (010380026 and 013200001)
for testing data. To eliminate redundancies within these two data sets, we
averaged the particulate measurements whenever more than one of the sites
was operating on a given date. Thus, the daily particulate data for Birmingham
represent averages over one, two, or three sites in the development set and
averages over one or two sites in the testing set.
Table 2.4 lists the number of daily samples in the entire development and
testing data bases. Considering that the data cover nearly one and a half
years (beginning of 1980 to nearly the middle of 1981), the sample size is
not extremely large. There are four reasons for this: (1) some sites were
not operational for the full one and one-half years, (2) the particulate data
were taken only every sixth 'day (every 12th or 24th day for chemical consti-
tuents), (3) within the routine particulate sampling schedule there were
missing values, and (4) certain days were eliminated based on weather consider-
ations. The fourth reason needs further clarification. Because we are
interested in the relationship between visibility and particulate concentrations,
we decided to eliminate days when visibility reduction was likely dcm.in.ated
20
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TABLE 2.3 STUDY LOCATIONS FOR THE DEVELOPMENT PHASE OF THE PROJECT
AIRPORT (NCC code)
PARTICULATE SITE (EPA Code)
DISTANCE FROM AIRPORT
TO PARTICULATE SITE(km;
Birmingham Mun. (13876)
Birmingham, AL (010380003)
Center Point, AL (010570001)
Mountain Brook, AL (012540001)
Birmingham, AL (010380026)
Tarrant City, AL (013200001)
8
6
10
3
6
National Airport (13743) Washington, D.C. (090020017)
Logan International (14739) Boston, MA (220240013)
Boston, MA (220240012)
2
6
Minneapolis/St. Paul Int'l Minneapolis, MN (242260049)
Minneapolis, MN (242260051)
7
12
Buffalo International(14733) Buffalo, NY (330660003)
Buffalo, NY (330660010)
11
11
Northeast Philadelphia Apt. Philadelphia, PA (397140024)
Philadelphia International Philadelphia, PA (397140036)
1
6
21
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TABLE 2.4 SUMMARY OF DATA BASES FOR THE DEVELOPMENT
PHASE OF THE PROJECT
Table 2.4a Development Data
LOCATION (EPA CODE) SAMPLE SIZE CHEMICAL COMPOSITION SAMPLE SIZE
Dichotomous SO^ Complete Sets for Botf
Hi-Vol and Dichotomous
SOJ and Pb.
Birmingham, AL (010380003,
010570001, and 012540001)
Washington, O.C. (090020017)
Boston, MA (220240013)
Minneapolis, MN (242260049)
Buffalo, NY (3306600Q3)
Philadelphia, PA (397140024)
61
32
40
38
29
54
28
11
14
15
9
16
26
11
11
13
5
3
Total 254 93 74
Table 2.4b Testing Data
LOCATION (EPA CODE) SAMPLE SIZE CHEMICAL COMPOSITION SAMPLE SIZE
Dichotomous SOT Complete Sets for Both
Hi-Vol and Dichotomous
SOJ and Pb.
Birmingham, AL (010380026
and 013200001)
Boston, MA (220240012)
Minneapolis, MN (242260051)
Buffalo, NY (330660010)
Philadelphia, PA (397140036)
54
31
34
30
48
14
9
10
6
10
11
7
6
5
6
Total 197 49 35
22
-------
by weather events rather than by ambient aerosol concentrations. Based on
our previous experience (Trijonis and Yuan 1978; Trijonis 1979, 1980, 1982c;
Trijonis et al. 1982), the specific elimination criteria we chose were as
follows: (1) eliminate the day if there was any measurable precipitation
during the 24-hour period, and (2) eliminate the day if fog was reported during
the daylight hours of useful visibility data (with the sole exception that a
:--rc"e *oc oc$ev"vation at tne 5:00 AM or ^OG AM ocur
T
he development phase of this project examines not only how visibility
depends on' relative humidity and particulate concentrations, but also how
dichotomous FP and IP can be predicted from TSP, visibility, and relative
humidity. In the latter analysis, we want to evaluate FP and IP predictive
formulae on an annual basis as well as a daily basis. For this purpose, we
created an "annual" test data set consisting of yearly mean values at the
14 study locations. The yearly means were taken over the period January 1980
to March 1981 (data were sparse in the first quarters of both 1980 and 1981,
so two first quarters were used to compensate for this scarcity). The yearly
means were typically composed of about 40 daily values and thus corresponded to
an "every ninth day" particulate sampling schedule.
2.J. DATA BASE FOR APPLICATION PHASE
Unlike the development phase of this project, where particulate data and
airport weather data are melded together to analyze the interrelationships, the
application phase treats the particulate measurements and airport recordings
separately. Data from the EPA IP network are first used to identify episodes of
high particulate concentrations in the East. Then, the airport data are used
to provide greater temporal and spatial resolution during those episodes.
In organizing the EPA IP network data to search for particulate episodes,
we limited ourselves to one complete year of data. This year was chosen as the
second calendar quarter of 1980 to the first calendar quarter of 1981 (April 1980
to March 1981), because, at the time of our study, that period provided the great-
est Quantity of data from the EPA IP Network. During the year of interest, 51 EPA
I? Network sites in the eastern U.S. reported at least some Hi-Vol and dichot-
omous data. However, some of the sites reported very little data, and several
cities nad more than one monitoring site. In preparing maos of the particulate
23
-------
data, we used only those sites with at least 18 data points during the year,
*
and we selected only one site per city. This restricted the analysis to the 25
sites illustrated in Figure 2.1.
Using data for these 25 sites, we constructed maps which listed daily TSP,
IP, FP, sulfate, and lead concentrations (see Chapter 4). Because such maps are
not very useful on days with few sites reporting data, the maps were prepared
only for days having data from at least 15 locations (although we also allowed
days with as few as 13 reporting sites, if at least seven of these sites had
chemical composition data for that day).
In the application phase, particulate patterns during various episodes
are studied using visibility and relative humidity data at 70 airports. The
airport data provide greater resolution than the EPA IP Network data both
temporally and spatially — temporally because the airport data are available
daily (rather than every sixth day), and spatially because the airport data
are from 70 locations (rather than 15 to 25 locations).
Figure 2.2 illustrates the 70 airports included in the application phase/
The criteria for selecting these airports are (1) that they are geographically
scattered over the East, (2) that they report data for relative humidity, and
(3) that they have an adequate set of visibility markers and are preferably
Class-1 weather stations.
~The criteria for selecting one site in each city were (1) quantity of data during
the year and (2) (for comparison purposes) proximity to the nearby airport being
used.
TT*
We used a slightly more refined site-type classification in Figure 2.2
(metropolitan versus urban/suburban versus nonurban) than in Figure 2.1
(metropolitan/urban versus suburban/nonurban) for two reasons. First, air-
ports are generally sited somewhat differently than EPA IPA Network sites,
on the fringes of cities/towns rather than in the center of cities/towns.
Second, Figure 2.2 contains many more sites than Figure 2.1, making a finer
resolution more meaningful.
24
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Minneapolis
(049) *
Marshall town(00;
O
Buffalo
(003)
Will Cntyy
(007)
•• iiiex unity.i
. _ _ •|Youngstown(002) (001) V
Medina O T, ' - '
[002J Akron
i nMlddletown (006) Res/tor
1 u ' pool)
Washington D.C.
^(017)
Birmingham
(026)
\City Name if
/Site Code i rp C i ty
York City
(Oil)
idelphia
(024)
jover (001)
Metropolitan/urban sites
O Suburban/nonurban sites
Figure 2.1 EPA IP network sites included in the application phase,
-------
,o
3url iington
Erie .,. O
tit 1 1 lamsport
Philadelphia
tndianapol is'.
^Cincinnatv
€
Springfield
0 Fort Smith
C
Little Rock
O
3 i rmi ngnam
saton Rouge\
Lake'Charles C
€
'-letropo 1 i tan 3 i tes
C Urban/suourftan sites
O Monuraan sites
Figure 2.2 Airport weather stations included in the application phase.
-------
3. RELATIONSHIPS BETWEEN PARTICULATE CONCENTRATIONS AND VISIBILITY
This chapter develops statistical relationships between the EPA IP
Network data for participate concentrations and the airport data for visi-
bility and relative humidity. As discussed in the previous chapter
(Section 2.3), the analysis is based on data from 14 sites in 6 air basins
of the Eastern U.S. In the investigation, some data sets are used to develop
the relationships, while other data sets are reserved to test the relationships.
In order to simplify later discussions, this chapter starts (Section 3.1)
by eliminating those variables and formulations that were found to be insigni-
ficant in our analysis. Section 3.2 then presents regression studies relating
atmospheric light extinction (determined from visibility data) to particulate
concentrations and relative humidity. The regression models are formulated
in a physically meaningful way, and they yield quantitative insights regarding
the visibility reduction efficiencies of particles according to their size
and chemical composition. Section 3.3 investigates the inverse type of
problem -- statistical formulae for predicting dichotomous particulate
concentrations (FP and IP) from airport data for visibility and relative
humidity, as well as from Hi-Vol data for TSP.
3.1 ELIMINATION OF VARIABLES AND FUNCTIONAL FORMS
it is useful to begin describing our findings by addressing variables
and formulations that were found to be of little significance or utility. By
eliminating these variables and formulations, subsequent discussions of
methodology and results can be simplified considerably.
3.1.1 Motor Vehicle Particles
Particulate matter from motor vehicles is one of the major aerosol
fractions distinguished in our analysis. Total ambient particulate concentra-
tions from auto exhaust, diesel exhaust, tire wear, and brake wear are
estimated using lead (Pb) as a tracer for motor vehicle emissions. Specifically,
the concentrations of suspendible, inhalable, or fine motor vehicle particles
*
are determined by the equation:
[Vehicular Particles] =k . fpbl . (3-1)
L • J x x L Jx
27
-------
where [ J denotes concentration and x signifies either TSP (suspendible),
IP (inhalable), or FP (fine). The constant k is the ratio of (suspendible,
/\
inhalable, or fine) total vehicular participate emissions to (suspendible,
inhaTable, or fine) noncatalyst exhaust Pb emissions. For the time period
of our study, 1980 to mid 1981, the values of the constants are k-,sp = 15,
kyp = 15, and k-p = 14 (Trijonis and Davis 1981).
In all of the various regression equations applied to the development
data set, the vehicular particle term was found to be statistically insignifi-
cant. The regression coefficient (extinction efficiency per unit mass concen-
tration) for vehicle particles was usually very small (e.g. compared to the
sulfate coefficient) and sometimes even negative. This result is identical
to our findings in a recent California study (Trijonis et al. 1982). It
should be stressed, however, that the lack of statistically significant
relationships does not imply that vehicular particles really have zero or
negligible effect on visibility. Rather, our results indicate that the effect
is not pronounced enough so that one can quantify it with the (admittedly
limited) airport data sets. Actually, from the conclusions of other studies
performed with more detailed aerosol and visibility data (Hidy et al. 1974;
Groblicki et al. 1980; Wolff et al. 1980; Cass et al. 1981; Conk!in et al.
1981), we know that vehicular particles (elemental carbon, organics, lead,
etc,) do contribute significantly to visibility reduction. Unfortunately, we
are unable to isolate and quantify this contribution with our current data
set. The problem of isolating the vehicular contribution may be especially
difficult in the East because of predominance by the sulfate/visibility relation-
ship (see later discussions).
It is interesting to note that, in separate regressions run with the
"test" data set, the vehicular particle term usually was statistically signi-
ficant (at 95 to 99% confidence levels). Combining the development and test
data sets together, we found the vehicular term to be sometimes significant,
sometimes not, with vehicular particle extinction efficiencies ranging from
10 to 90% of sulfate extinction efficiencies. These results are encouraging
because they suggest the possibility of quantifying the vehicular term better
when more months or years of data become available from the EPA IP Network.
It will be very important to analyze the vehicular term further in- any subse-
quent studies because elemental carbon from diesels, the most critical visibility-
28
-------
related subcomponent of vehicular particles, is expected to cause a major
deterioration in visibility over the next ten to twenty years (NRC 1981;
Trijonis 1982b, Trijonis 1983a).
Having discussed our findings with respect to vehicular particles in
this section, we will no longer distinguish vehicular particles as such.
Rather, vehicular particles will be aggregated in the "nonsulfate particle"
category for the purpose of subsequent discussions.
3.1.2 Fine Versus Coarse Fractions from Dichotomous Samplers
Several regression models were formulated with inhalable particles
(IP< 15 urn) separated into fine particles (FP < 2.5 urn) and coarse particles
(2.5 < CP < 15 urn). In all such regressions conducted with the development
data set, we found FP highly significant and CP statistically insignificant,
suggesting that coarse particle contributions to visibility reduction in the
East are minor if not negligible. This result is similar to the findings of
Groblicki et al. (1980) in Denver and Fernan et al. (1981) in rural Virginia.
This conclusion is also very consistent with the Mie theory of aerosol light
scattering which indicates that coarse particles have very small scattering
efficiencies per unit mass compared to particles in the 0.1 to 1.0 urn size
range (see Figure 3.1).
It should be noted that a controversy may be brewing with respect to
coarse particle contributions to visibility reduction. -Recent work in the
arid Southwest by Pitchford (1982) suggests that coarse particles account,
on the average, for as much as one-third of aerosol light extinction.
However, we would expect coarse particles to be relatively more important
in the arid Southwest (where fugitive dust is relatively more significant
and where fine particle extinction efficiencies are reduced due to lower
relative humidity) than in the East. Although our current findings do not
prove that coarse particle visibility effects are entirely negligible in
the East, the results do indicate that coarse particle effects are minor.
It ~s "riteres11^c to note that, D, comc'T^g the ceve" :ornent 5rc test
ciata sets, we ootainec a marginal statistical significance for CP in one case--
regression of extinction versus FP/(1-RH'' and CP/(1-RH). The extinction
efficiencv for CP was small, about one-s'xtn that of F". Because of the
-------
o
d
8
Q
m
Q
ro
s
to
o'
CD
O
CD
O
CJ
O
Q
o
ro
o
o '
u
^
fO
0
CJ
O
,
O
f>^
o
OJ i-
>i"a OJ
4J O
w C
••- 3 T3
1- C
O) i. «3
4-> O
-(-> M— O
0) ^
•M 3
•M 3 —
J= C.
CT = Lf)
30
-------
marginal statistical significance (just over 95%) and because of statistical
colinearity problems in this regression form, the results can be viewed with
very little confidence. However, these results suggest that it may be possible
to quantify the (small) contribution from CP with larger amounts of data from
the EPA IP Network.
3.1.3 Relative Humidity and the Role of Aerosol Water
The role of aerosol water is so important to visibility that it deserves
an extensive preliminary discussion. Thermodynamic calculations (Tang 1981) as
well as measurements made with microwave waterometers, nephelometers, and
multi-stage cascade impactors (Covert et al. 1972; Hidy et al. 1974; Ho et al.
1974; Stelson and Seinfeld 1981; Ferman et al. 1981; Countess et al. 1981)
suggest that, at relative humidities of 65-75%, the mass of water associated
*
with fine ambient aerosols is approximately equal to or slightly greater than
the mass of aerosol electrolytes (e.g. sulfates and nitrates). As relative
humidity increases toward 100%, the mass of aerosol water rises hyperbollically.
Figure 3.2 demonstrates that the dependence of aerosol water content on
relative humidity varies significantly with the specific chemical form of the
aerosol electrolyte (in this case, sulfates). For example, pure sulfuric acid
aerosol retains water even at very low relative humidities. Pure ammonium
sulfate aerosol, on the other hand, does not hydrate until the deliquescent
point at 80% relative humidity. Under real atmospheric conditions, several
chemical forms of sulfate are probably present, so that the aerosol water/
relative humidity dependence would be intermediate to the various curves shown in
Figure 3.2. Also, under real atmospheric conditions, the deliquescent points
might be smoothed out by hysteresis effects.
The role of water becomes all the more critical to visibility when one
realizes that, because of density differences, water should scatter more light
per unit mass than the aerosol electrolytes. For example, White (1981) has
suggested that, because water is 1.7 times less dense than sulfate, it should
In discussing aerosol water, it is imperative to distinguish between ambient
particles and measured (filter-collected) particles. Filter-collected particles
are typically equilibrated at about 40-50% relative humidity, and much, if not
most, of the ambient water is lost in the measurement.
31
-------
3.0-
2.5-
o
•^
•o
*
OJ
CO
o
"3
1.5-
1.0
Note: Volume of added
water is propor-
tional to:
(d3/d3) - 1
(NH4)3H(S04)
i i
30 40 50 60 70 80
Percent Relative Humidity
i
90
100
Figure 3.2 Growth curves for sulfate aerosols as a function
of relative humidity (Tang 1981).
scatter approximately 1.7 times as much light per unit mass than does sulfate.
Thus, if there is typically slightly more water mass than electrolyte mass in
the aerosol, the total amount of light scattering from the water should be
about twice that from the electrolyte. This hypothesis is supported by recent
field studies comparing scattering levels from "wet" versus "dry" aerosols
(Groblicki et al. 1980; Ferman et al. 1981).
Figure 3.3 illustrates the geographical pattern of average relative
humidity in the Eastern U.S. Over most of the region, relative humidity
averages about 70 to 75% annually (about 60 to 70" during daylight"hours).
On a day-to-day basis, relative humidity can reach very high levels at all
32
-------
Figure 3.3 Geographical distribution of annual average relative
humidity (percent) in the Eastern U.S. (NOAA 1977).
locations in the region. Because of the fairly high humidity levels,
water should be a very critical component of the ambient aerosol through-
out the East.
Water should be regarded as an integral part of the aerosol to which it is
attached. In other words, if the sulfate aerosol were eliminated, the water
associated with the sulfate would also be eliminated from the aerosol phase.
Transferring water from the aerosol to the gas phase produces essentially no
change in relative humidity, because the total water in the gas phase is
typically orders of magnitude greater than the total water in the particulate
33
-------
phase. Thus, there would be no reason for the water that had been associated
with the sulfate to tend to become attached to the remainder of the aerosol.
The above observations help to explain why several statistical studies
have found that sulfate aerosol is the dominant contributor to visibility re-
duction in the East (Trijonis and Yuan 1978; Leaderer and Stolwijk 1979;
Pierson et al. 1980; Ferman et al. 1981). Not only do sulfates constitute a
sizeable fraction of the fine (optically active) aerosol, but also they tend
to carry with them a substantial volume of water.
In our statistical studies, we tested regression formulae that incorporated
relative humidity in two ways -- first according to a strictly linear model,
Light Extinction = a + b,RH + Zb (aerosol term). (3-2)
i i 1
and second according to the model,
Light Extinction = a + Z b (aerosol term)i _ (3_3)
i i (1-RH)
The first approach represents a straightforward multiple linear regression with
no physical insight used to define the role of relative humidity. The second
approach makes physical sense because Cass (1979) and Latimer et al. (1978) have
concluded that the mass of water associated with hygroscopic aerosols should vary
approximately as (1-RH) .
As one might expect a priori, arid as has been suggested in other studies
(Trijonis and Yuan 1978; Trijonis 1979, 1980; Trijonis et al. 1982), our results
show regression form (3-3) to be much superior to regression form (3-2).
Equation (3-3) always produces higher correlation coefficients than Equation
(3-2), despite the fact that Equation (3-3) has one less free parameter.
Furthermore, we found that Equation (3-2) yields extinction efficiencies for
sulfates that were nearly twice what would be expected on theoretical grounds.
The most apparent reason for the badly inflated coefficients with Equation
(3-2) is that the sulfate variable in Equation (3-2) represents not only
sulfate compounds but, also some of the water attached to those compounds
[i.e. aerosol water is ambiguously presented in both the sulfate and linear
RH terms in Equation (3-2) . Thus, Equation (3-3) is obviously preferable
-------
empirically in addition to being best theoretically. Equation (3-3) correctly
includes water within the aerosol terms and correctly approximates the
hyperbolic dependence on relative humidity . For the above reasons, we will
eliminate the strictly linear regression model, Equation (3-2), from all
subsequent discussions.
3.2 LIGHT EXTINCTION FROM AEROSOL CONCENTRATIONS
Our analyses relating visibility to aerosol concentrations follows the
statistical procedures established by Cass (1979), White and Roberts (1977),
Trijonis and Yuan (1978), Trijonis (1979, 1981), and Trijonis et al. (1982).
Regression equations are developed which relate daytime average extinction
to daytime average relative humidity and 24-hour average particulate concen-
trations. The coefficients in the regression equations can be interpreted as
estimates of "extinction coefficients per unit mass" or relative "extinction
efficiencies" for each aerosol species. These extinction efficiencies can be
used to estimate the fraction of visibility degradation attributable to
individual aerosol components (e.g. sulfates).
3.2.1 Variable Definition and Regression Methodology
The parameters for the regression studies consist of visual range [miles],
f i p 3
relative humidity (fraction, no units , and particulate concentrations jug/m "].
Before conducting the regressions, however, we must perform some simple trans-
formations in the forms of the variables. For example, instead of using visual
range (V) as the dependent variable, it is much more appropriate to use the
extinction coefficient (B), which is inversely proportional to visual range.
As explained in Section 1.1, the extinction coefficient is a linear sum of
four components: light scattering by gases, light scattering by aerosols, light
absorption by gases, and light absorption by aerosols. Extinction coefficient
is most appropriate for use in linear regression models because each of the
components of extinction should be directly proportional to aerosol or gas
concentrations (assuming other factors, such as light wavelength, aerosol size
distribution, particle shape, and refractive index remain constant).
-------
We compute extinction coefficient from visual range data using a modified
Koschmeider formula:
Equation (3-4) differs from the usual Koschmeider formula, B = 3.9/V, in the
sense that Equation (3-4) assumes a 5% contrast detection threshold for the
visibility observer rather than a 2% detection threshold. We have chosen the
modified Koschmeider formula because recent studies (Allard and Tombach 1980;
Malm et al. 1979; Trijonis 1979) suggest that airport visibility observations
underestimate true instrumental visual range (defined -as the distance at which
the contrast for a perfectly black target is reduced to 2%). Several investi-
gators specifically recommend using a contrast threshold of 5% (Koschmeider
constant of 3.0) in order to obtain an unbiased estimate of extinction coef-
ficient from human observations of visual range (Allard and Tombach 1980;
Malm 1979; Middleton 1952; Douglas and Young 1945).
The choice of a Koschmeider constant has a proportional effect on the
aerosol extinction efficiencies that we calculate from the regression analysis.
The choice has no effect, however, on allocating total extinction among aerosol
species, because total extinction and the extinction contributions from indivi-
dual aerosol species are both changed in proportion.
Because one major goal of our analysis is to quantify the relative
importance of TSP, IP, and FP as determinants of visibility, we have conducted
entirely parallel regression studies for each of those three particulate
parameters. In each case, we define the total mass concentration as X (where
X denotes TSP, IP, or FP). Furthermore, in order to compare the effects of
sulfate and nonsulfate particles, we subdivide total aerosol mass according to
X = XSULFATE + XNONSULFATE. (3-5)
*
For consistency with established convention, we change the units of extinction
to 10~4m~l after applying Equation (3-4).
3(5
-------
The variable, XSULFATE, is defined as 1.38 times the measured sulfate concen-
tration in order to account for the mass of the cation, presumably ammonium
as (NH4)2S04. The variable, XNONSULFATE, is computed simply by subtracting
XSULFATE from the total mass.
Figure 3.4 illustrates the design of our regression studies. For each
of TSP, IP, and FP, we start by regressing extinction (B) against total mass
concentration. We then examine how the results change and improve as we add
relative humidity, chemical composition (sulfate versus nonsulfate), or both
to the model. All the bivariate regressions are run stepwise, retaining only
those terms that are statistically significant at a 95% confidence level.
It should be noted that, in the final step with both relative humidity
and chemical composition, we allow the regression model to include or exclude
hygroscopicity (relative humidity in the aerosol term). That is, we permit
the model to select either XSULFATE or XSULFATE/(1-RH) and either XNONSULFATE
or XNONSULFATE/(1-RH). We find that, in all cases, the model includes relative
humidity as part of the aerosol terms in order to obtain the best fit. Thus,
the final step is essentially just a bivariate regression of extinction against
XSULFATE/(1-RH) and XNONSULFATE/(1-RH).
Regression analysis is a purely statistical technique, and there is no
guarantee that the observed relationships represent cause-and-effect. However,
if -- as in the above analysis -- the regression is structured to reflect
fundamental principles, the results will strongly suggest certain physical
implications. In our analysis, the coefficients b.. or b../(l-RH) of the aerosol
concentration terms are readily interpreted as extinction coefficients per
unit mass (extinction efficiencies) for the aerosol terms.
3.2.2 Overall Correlation Levels
Before presenting the regression results, it is useful to discuss the
overall degree of correlation among the variables in the analysis. Table 3.1
lists the correlations between extinction coefficient (the dependent variable)
and the set of independent variables. Several patterns are obvious in the
table. First, as will be discussed later in more detail, extinction correlates
37
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better with FP than IP, and better with IP than TSP. Second, each aerosol
term attains a higher degree of correlation if the relative humidity factor,
(1-RH)" is included. (The relative humidity factor itself correlates with
extinction at a level of 0.52). Third, extinction correlates much better
with the sulfate fraction than the non-sulfate fraction. In fact, of all the
variables in Table 3.1, TSPNONSULFATE and IPNONSULFATE are the only two that
do not correlate with extinction at a 99% confidence level.
TABLE 3.1 CORRELATION OF EXTINCTION COEFFICIENT
WITH THE INDEPENDENT VARIABLES
INDEPENDENT VARIABLE: CORRELATION COEFFICIENT TO EXTINCTION
"SP
TSP: .30
TSP/(1-RH): .54
TSPSULFATE: .46 TSPNONSULFATE: .05
TSPSULFATE/(1-RH): .70 TSPNONSULFATE/(1-RH): .48
**
IP
IP: .51
IP/U-RH): .66
IPSULFATE: .52 IPNONSULFATE: .27'
IPSULFATE/(1-RH): .73 IPNONSULFATE/(1-RH): .61
FP
FP: .63
FP/d-RH): .70
FPSULFATE: .50
FPSULFATE/(1-RH): .75
FPNONSULFATE: .44
FPNONSULFATE/(1-RH): .63
'Does not attain 99% confidence level for statistical significance
Does not attain 95% confidence level for statistical significance
39
-------
It is also worthwhile to examine the degree of intercorrelation
(colinearty) between the "independent" variables that are linked together in
multiple regressions. As indicated in Table 3.2, the various terms XSULFATE
and XNONSULFATE correlate moderately with one another (R%.40). The terms
XSULFATE/(1-RH) and XNONSULFATE/(1-RH), however, correlate strongly together
(R-As.75). These strong levels of intercorrelation among the independent
variables can cause statistical problems in the sense that one of the inde-
pendent variables may be given more weight in the regression at the expense
of another. The colinearity problem will be discussed further in the later
section dealing with limitations of the analysis.
TABLE 3.2 INTERCORRELATIONS AMONG INDEPENDENT VARIABLES
USED TOGETHER IN BIVARIATE REGRESSIONS
TSPSULFATE vs. TSPNONSULFATE: 0.34
TSPSULFATE TSPNONSULFATE
™••""• r -~-~ V 5 « ~'" ~ " " " ~
(1-RH)
(1-RH)
IPSULFATE vs. IPNONSULFATE: 0.41
IPSULFATE IPNONSULFATE . n 77
/ 5 » ™«^—•— • U • / /
(1-RH)
FPSULFATE vs. FPNONSULFATE: 0.48
FPSULFATE e FPNONSULFATE . n 7A
VS • '' ~ ""' ." • U • /H
(1-RH)
(1-RH)
40
-------
3.2.3 Regression Results
Tables 3.3a, 3.35, and 3.3c summarize the results of the regression
analyses for TSP, IP, and FP, respectively. For each analysis, we have listed
the regression equation, the standard error of the coefficients, the sample
2
size, and the percent of variance explained (R , square of the correlation
coefficient). As noted previously, only those variables that meet a 95%
statistical significance level are retained in the equations.
The reader should be reminded that the units of extinction (B) are
flO m 1, and that the units of aerosol concentration are fug/m 1. Thus,
r 4 -1 3 ~i
the units of the regression coefficients are [(10 m) /(yg/m )J.
Comparing the upper portions of Tables 3.3a, 3.3b, ana 3.3c, it is
obvious that FP is related significantly better to visibility than IP, which
in turn is related significantly better to visibility than TSP. This makes
good physical sense because light scattering by particles — the dominant
part of aerosol extinction in the East -- is distinctly a fine particle
phenomenon. As shown previously in Figure 3.1, light scattering per unit
mass by aerosols exhibits a pronounced peak near a particle size of 0.5 urn
(at the wavelength of light). In addition, the absorption of light by
aerosols is basically governed by fine elemental carbon (Groblicki et al.
1980; Ferman et al. 1981; Weiss and Waggoner 1981). For these reasons, the
fine particle mass mode (typically residing in the 0.1-1.0 urn size range)
usually dominates over the coarse particle mass mode (typically residing in
the 3-50 ym size range) with respect to visibility reduction. It is not
surprising that -- as we better approximate fine particle mass by going from
TSP<50 ym to IP<15 ym to FP<2.5 ym -- the correlation to light extinction
improves significantly.
The upper right hand corners of Tables 3.3a to 3.3c show that the
performance of the regressions improves significantly if relative humidity
is added to the model. The lower left hand corners of the tables indicate
that the statistical fit does not improve very much (except in the case of
41
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TS?) if the participate concentration is divided into sulfates and nonsulfates.
When tne sulfate/nonsulfate division is made, the sulfate fraction is the more
important term and is, in fact, the only statistically significant term in the
case of TSP and IP. The regression coefficients (extinction efficiencies) for
sulfates in the lower left hand corners of the tables are significantly higher
than one would expect for pure (dry) sulfates based on theoretical principles.
The reason for this is obvious; the sulfate mass extinction efficiency reflects
the effect of water as well as the effect of sulfates.
Examining the lower right hand corners of Tables 3.3a - 3.3c, it is
apparent that the best statistical fits are obtained when both chemical
composition (sulfates) and relative humidity are included in the regression
models. The percent variance explained by these regressions is about 55%
(R^.75). In the case of TSP and IP, the sulfate fraction is the only sig-
nificant term. It is very noteworthy that, once sulfates and relative
humidity are added to the model, the fit does not improve that much as one
proceeds from TSP to IP to FP. These results suggest that the single most
critical step in analyzing Eastern visibility is to distinguish the combined
roles of sulfates and relative humidity. It is also noteworthy that the
extinction efficiencies for "dry" sulfates in the lower right-hand corners
2
of the tables agree very well with theoretical values of 2 to 4 m /g (Latimer
et al. 1978; White and Roberts 1977).
The performance of Che regression equations in Table 3.3 has also been
evaluated against ti.e i/-.dependent ''test" data set. Table 3.4 presents the
results of this evaluation. Somewhat surprisingly, the equations fitted to
the "development" data set usually achieve even higher percent variance
explained with the "test" data set. This unusual and encouraging result
indicates that there is not a serious "over-fitting" problem.
3.2.4 Conclusions Regarding Issues of Concern
The regression studies of Table 3.3 provide insights with respect to
several issues of current interest. One such issue is the relative extinction
efficiency per unit mass concentration of sulfates versus other aerosols.
With respect to this issue, we will discuss only the regression equations
43
-------
TABLE 3.4 REGRESSION EQUATION PERFORMANCE EVALUATED
WITH THE INDEPENDENT TEST DATA SET
PERCENT VARIANCE EXPLAINED
EQUATION
B
B
B
B
B
B
B
B
= 1
= 1
= 0
= 1
= 0
= 1
= 0
= 1
.12 +
.09 +
.96 +
.17 +
.77 +
.05 +
.91 +
.18 +
.013
.064
.0062
.022
.030
.103
.011
.034 •
TSP
TSPSULFATE
TSP
1-RH
TSPSULFATE
1-RH
IP
IPSULFATE
IP
1-RH
IPSULFATE
1-RH
DEVELOPMENT
DATA SET
9%
21
30
49
26
01
/a
01
la
at
la
«/
la
27%
43
54
at
la
at
la
INDEPENDENT TES'
DATA SET
10%
20%
49%
53%
21%
24%
62%
54%
(9%*)
(49%*)
(64%*)
8 = 0.76 + .053 FP
B = 0.82 + .041 FPSULFATE + .075 FPNONSULFATE
FP
B= 1.03+ .016
8=1.04+ .034
1-RH
FPSULFATE
+ .0064
FPNONSULFATE
i-RH
40%
30%
50%
58%
27%
34%
67% (71%*)
81%
Values in parentheses are with removal of one outlier, 14 June 1980 at
Tarrant City, from the test data set. This day, which barely managed to
pass our data quality screen, had inconsistent values for particle concen-
trations: TSP =167 ^g/m3, IP = 216 ng/m2, and FP = 180 fcg/m3. The outlier
had a fairly strong influence on the evaluation test because of the very
high particle concentrations. It did not affect the evaluation of the
chemically-resolved regression formulae because no chemical participate
data were available on that day.
-------
involving the terms b. (aerosol concentration)/(l-RH), with b. represent!'i
the "dry" extinction efficiency and (1-RH)~ indicating the growth of
ing
i -1 :
'dry1 --• ' L L' '
extinction ef'ic^ency with relative humidity.
With respect to relative extinction efficiencies, we must distinguish
among three cases: sulfate versus nonsulfate TSP, nonsulfate IP, and nonsulfate
FP. In the case of TSP, the dry sulfate extinction efficiency is 0.022
lO'VVtyg/m3) or 2.2 m2/g. The TSPNONSULFATE coefficient is insignificant,
and in fact turned out slightly negative in the bivariate regression with
2
TSPSULFATE. However, total TSP has a dry extinction efficiency of 0.62 m /g.
Because total TSP includes sulfates, the actual nonsulfate TSP coefficient should
2 2
be significantly less than 0.62 m /g, much less than the 2.2 m /g for TSP sulfate.
This conclusion agrees with the findings of many previous statistical studies
that the extinction efficiency for sulfate is generally an order of magnitude
greater than the extinction efficiency for the remainder of TSP (White and
Roberts 1977; Trijonis and Yuan 1978; Cass 1979; Leaderer and Stolwijk 1979;
Trijonis 1979, 1980, 1982c; Trijonis et al. 1982).
~ne case of IP is somewhat parallel to that of TS?. "he extinction
2
efficiency of IP sulfate is 3.4 m /g. The extinction efficiency of total IP
2
is 1.1 m /g, implying a considerably lesser value for nonsulfate IP. Both
nonsulfate IP and nonsulfate TSP are expected, on theoretical grounds, to have
low extinction efficiencies because they include a coarse particle mode in the
size range above 2 urn (see previous discussion).
The case of FP is less obvious and more interesting. In the bivariate
regressions with FP, both the sulfate and nonsulfate terms are statistically
significant. With the development data set, we obtain a dry extinction
2 2
efficiency of 3.4 m /g for sulfate FP and 0.6 m /g for nonsulfate FP. This
suggests that fine sulfates are much more efficient at light extinction than
other fine particles. We are not, however, very confident of this conclusion.
As noted in Section 3.2.2, the FPSULFATE/(1-RH) and FPNONSULFATE/(1-RH)
variables are highly intercorrelated. It is very possible that the regression
may have yielded an inflated sulfate coefficient at the expense of a deflated
nonsulfate coefficient. This possibility seems all the more likely because
a separate regression applied to our "test" data set produces dry extinction
2 2
efficiencies of 2.8 m 7g for sulfate FP and 1.7 m /g for nonsulfate FP
(at a correlation of 0.92 for the sample size of 49). It seems very likely
45
-------
that sulfates are more efficient at visibility reduction than the remainder
of fine particles, but we cannot quantify the difference very precisely at
present.* A repeated analysis when more data are available should allow a
more precise characterization of this issue.
As indicated by the above paragraphs, the extinction efficiency for
TSPSULFATE (2.2 m2/g) is significantly less than that for IPSULFATE and
2
FPSULFATE (both 3.4 m /g). A major reason for this is that TSP sulfate con-
centrations are inflated due to artifact sulfate formation on Hi-Vol glass filters.
A second issue of concern is the fractional contribution of sulfates
(and associated water) to total visibility reduction in the East. Several
recent studies have found that sulfates contribute approximately 50 to 75%
of light extinction in the East (Trijonis and Yuan 1978; Leaderer and Stolwijk
1979; Pierson et al. 1980; and Ferman et al. 1981). The regression equations
in the lower right-hand corners of Tables 3.3a to 3.3c can be used to estimate
sulfate contributions to extinction by plugging in average values for the
variable "XSULFATE/(1-RH)". For the-cases of TSP, IP, and FP, we obtain
percentage sulfate contributions to extinction of 45%, 44%, and 40%, respec-
tively. As discussed in the section on limitations of the analysis, these
estimates are somewhat uncertain. Studies based on more detailed data sets
suggest significantly higher percentage contributions from sulfates (Pierson
et al. 1980; Ferman et al. 1981); however, the latter studies might be
strongly seasonally biased because they were conducted in summer, when sulfates
reach a distinct seasonal maximum.
A policy issue of major concern is the establishment of welfare standards
to protect visibility. With respect to this issue, our results indicate a
very simple but important conclusion. As recognized by EPA'(1982), if welfare
standards are to be established to protect visibility, such standards should
be set for FP and/or sulfates, not TSP or IP.
3.2.5 Geographical Variations
It is worthwhile to investigate whether the extinction/aerosol relation-
ship varies geographically. In a recent California study (Trijonis et al.
The reader should note that one specific subcomponent of nonsulfate FP,
elemental carbon, is known to have a very high extinction efficiency, about
12 m2/g, most of which is absorption. At a relative humidity of 70%, the
"dry" sulfate coefficient of 3.4 m2/g increases to 11 m2/g, still slightly
below the extinction efficiency for elemental carbon.
46
-------
1982), we found that aerosol extinction efficiencies showed significant and
physically reasonable geographical variations. For example, sulfate extinc-
tion efficiency exhibited consistent regional differences by a factor two or
more. These geographical differences were explained by region-specific
aerosol size distributions within the fine particle mode. In agreement with
theory (Faxvog and Roessler 1931; Ouinette 1981), regions where sulfates had
a relatively large average diameter (e.g. 0.6 ym) showed much greater extinction
efficiencies than regions where sulfates had a relatively small average
diameter (e.g. 0.2 ym).
In order to investigate geographical variations in the East, the
regressions of Table 3.3 were run individually for each of the six study
cities (Birmingham, Washington, Philadelphia, Buffalo, Boston, and Minneapolis).
The equations did change considerably from city to city, both in terms of the
variables chosen as statistically significant and in terms of the specific re-
gression coefficients ^extinction efficiencies/. Jsing these results, we formed a
"geographically >"9soived prediction model" ;based on six equations, one specific
to each city) and compared the performance of that model to the aggregate
model (based on the single overall equation listed in Table 3.3). Table 3.5
summarizes the comparison for the three approaches of B vs. concentration (X),
B vs. X(l-RH), and B vs. XSULFATE/(1-RH) and XNONSULFATE/(1-RH). Comparing
the first two columns, it is obvious that the geographically resolved model
performs much better on the development data set than the aggregate Eastern
model. However, when the geographically resolved model is applied to the inde-
pendent data set, there is essentailly no gain at all in performance. This
strongly implies that the geographical variations essentially just represent an
"overfit" to the development data set. That an overfit problem exists is not
surprising considering the very limited sample size per city, especially with
respect to chemical composition (see Table 2.4a). Based on these results,
we conclude that sufficient data are not now available to study geographical
variations in the relationships. Significant geographical variations may
actually exist, but they cannot be quantified until considerably more data are
compiled from the EPA IP Network.
47
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3.2.6 Limitations of the Analysis
There are several noteworthy limitations in using regression models to
quantify the dependence of extinction on aerosol concentrations. One limitation
involves random errors in the data base produced by imprecision in the measure-
ment techniques (for airport visibility or particulate concentrations) and by
the fact that the airport and particulate monitor are often located several
kilometers apart. Random errors in the data tend to weaken the statistical
relationships, leading to lower correlation coefficients and lower regression
coefficients. This causes an underestimate of the extinction efficiencies
for the aerosol species (e.g. sulfates) and, therefore, an underestimate of
the contributions of the aerosol species to total extinction-
Incompatibilities between the airport visibility data base and the
particulate data base can lead to at least two types of systematic bias.
The particulate measurements, usually made at center-city locations, may be
systematically higher than the aerosol concentrations averaged over the visual
range surrounding the airports (usually suburban). The bias caused by relatively
high particulate concentrations would result in an underestimate of extinction
efficiencies for the aerosol species. A reverse type of bias, e.g. an over-
estimate of extinction efficiencies, would result if daytime aerosol levels
(corresponding to the time period of the visibility measurements) were higher
than the 24-hour average aerosol levels measured by the particulate monitors.
Although these systematic errors could bias the extinction efficiencies, they
should not bias the allocation of extinction among aerosol species (which is
based on a multiplication of extinction efficiencies times the measured mass of
the aerosol).
Artifact sulfate, formed by SCL collection on glass-fiber filters, is a
systematic error affecting the Hi-Vol (TSP) data. Artifact sulfate should
cause a slight to moderate underestimation of the extinction efficiency for
sulfates but, again, should not distort the allocation of total extinction
among sulfates and other aerosols.
Perhaps the most important difficulty in the regression analysis is the
problem of colinearity, i.e. the intercorrelations that exist among the
"independent" aerosol variables. Multiple regression is designed to estimate
*
the individual effect of each variable, discounting for simultaneous effects
49
-------
of other variables, but the colinearity problem can still lead to distortions
in the results. Specifically, in some cases, it is possible that nonsulfate
particles are assigned lower regression coefficients or even statistical in-
significance because they are colinear with sulfate which bears a stronger
relation to extinction.
Although not a conceptual limitation, data availability was certainly a
practical limitation in this study. As indicated in the discussions above, we
think that many of our results can fae made more certain and more precise when
larger data sets become available. The size of the data set could be increased
somewhat by moderately relaxing our criteria that eliminate days with fog or
precipitation. Also, another year or two of measurements from the EPA IP Net-
work would be extremely useful.
3.3 PREDICTION OF DICHOTOMOUS FP AMD IP FROM AIRPORT DATA AND HI-VOL DATA
As demonstrated in the previous sections of this chapter, visibility
is closely related with fine particle concentrations and, to a lesser
extent, with inhalable particle concentrations. In fact, in the next
chapter, we will show that — because of this close relationship —
visibility can be used qualitatively to study the spatial/temporal
extent, transport patterns, and origins of large scale fine particle
episodes. It is also worthwhile to investigate if the relationship can
be carried even further. Specifically, it is of interest to examine the
feasibility of using airport data for visibility and relative humidity,
in conjunction with Hi-Vol data for TSP, as quantitative predictors of
FP and IP. The reason for the interest is that the geographical and
historical record of dichotomous data is quite small compared to the
available record of airport and Hi-Vol data. Empirical formulae for
predicting dichotomous parameters from airport and Hi-Vol data would
greatly augment historical information regarding FP and IP and would
ease the expansion from TSP standards and monitoring to TSP/IP/FP
standards and monitoring.
A study of relevance to this issue was recently reported by Trijonis
and Davis (1981) and Trijonis (19835). The prior study differed from the
current investigation in three major respects. First, the prior study used
50
-------
a large national data base (930 simultaneous dichotomous and Hi-Vol samples
at 75 nationwide locations), rather than a smaller Eastern data set.
Second, the prior study related FP and IP to Hi-Vol data for TSP, SO^, and
Pb, rather than to Hi-Vol data for TSP in conjunction with airport data
for extinction (visibility) and relative humidity. Third, the predictive
formulae of the prior study were based on a hybrid methodology — pre-
specifying the SOT and Pb coefficients based on physico-chemical principles,
and fitting just one free statistical coefficient to the remainder of TSP
through a zero-intercept regression -- rather than on a purely statistical
approach.
To facilitate comparisons, we will use the same performance measure
as the prior study -- percent error, which is simply the standard error of
the predictions (appropriately adjusted for degrees of freedom) divided by
the mean value of FP or IP. This is a very appropriate performance measure
because the absolute predictive error increases nearly in proportion with
the magnitude of FP or IP (Trijonis and Davis 1981; Trijonis 1983b).
In order to put our current results in perspective, it is worthwhile
to briefly summarize the findings of the prior study. It was found that
a Hi-Vol measurement of TSP alone is not a good predictor of FP. The
national aggregate equation,
FP = 0.30 TSP, (3-6)
yielded a 56% error for individual daily values of FP and a 30% error for
annual mean values. Predictions of FP were improved substantially by adding
sulfate and lead data. The national aggregate equation,
FP = 1.1 SO* + 11 Pb + 0.14 (TSP - 1.4 SO^ - 15 Pb), (3-7)
had a 40% error for daily values of FP and 17% error for annual mean values
of FP. Disaggregating the FP predictive scheme by region and season did
not further reduce the error significantly. Concentrations of IP were
predicted fairly well from TSP data alone, the national aggregate equation,
IP = 0.61 TSP, (3-8)
51
-------
achieving a 31" error for daily valjes of IP and a 16% error for annual mean
values. The error for IP could be further reduced marginally, to 26% for
daily values and 13% for annual mean values, by adding the SOT and Pb
variables and by disaggregating the predictive scheme by region and season.
3.3.1 Prediction of FP
Table 3.5 summarizes the results of predictive schemes for FP based
on the following types of data (i.e.. independent variables):
A. Hi-Vol data for TSP,
B.I Airport data for extinction (visibility),
3.2 Airport data for extinction and relative humidity,
C.I Hi-Vol data for TSP with airport data for extinction,
and C.2 Hi-Vol data for TSP with airport data for extinction and relative
humidity.
Furthermore, to serve as a reference point, the first row of the table
lists the "coefficient of variation'" for FP, defined as
standard deviation of FP concentrations (over all sites and all days or years)
average FP concentration (over all sites and all days or years)
The coefficient of variation, about 60% for daily values of FP and about
25% for annual mean values of FP, represents the standard error if the
composite mean (over all sites and days) were used as the most simplistic
predictor of FP concentrations.
It should be noted that, in addition to the linear terms and simple
combinations of variables in Table 3.6, various other polynomial terms were
tried in the regression equations. The other polynomial terms did not
yield significantly better statistical fits to the data. Thus, the
equations in Table 3.6 represent about the best predictive accuracy possible
for each set of independent variables.
There are several important conclusions evident in Table 3.6. The
first is that TSP alone is not a good predictor FP (Scheme A). This is
especially apparent in the poor performance of Equation A against the
independent daily and annual data sets. This finding is consistent"with
the conclusions of the study discussed earlier (Trijonis 1983b).
52
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The second notable conclusion is that airport data for extinction
and relative humidity (Schemes B.I and B.2) do not yield good quantitative
predictions of FP (at least on a daily basis). This conclusion deserves
an extended discussion because it is somewhat surprising, considering that
we earlier found extinction to be fairly closely related to fine particles
and relative humidity. Basically, there are two fundamental impediments to
predicting FP accurately from airport data. The first impediment concerns
the intrinsically high fluctuation in daily FP concentrations — a
coefficient of variation of about 60%. In order to reduce this standard
error down to a more reasonable value, such as 20-30%, we would require a
regression result with 75-89% variance explained* (R = .87- .94). As
indicated by the following discussion, achieving such a good fit with the
airport data alone seems impossible. The second impediment is that the
inverse process of predicting fine particles from extinction and relative
humidity is not as physically straightforward or statistically well-fitting
as the more direct process of predicting extinction from fine particles
and relative humidity. For example, based on atmospheric optics considera-
tions, we found that it was very reasonable to propose the relationship:
8 ' a * bi ' (3-9'
Indeed, Equation (3-9) yielded a good statistical fit, about 50 to 70% variance
explained (R = .70- .84) based on the development and test data sets. But,
how does one invert that relationship physically to serve as a predictor of
FP? If we conduct an algebraic manipulation and try zero-intercept
regressions of the form,
FP = CjBd-RH) - C2(1-RH) (3-10)
we find that such an equation achieves a poor statistical fit, explaining
very little of the variance in FP. Adopting a blind statistical approach,
and performing multiple regressions relating FP to an extensive list of
*
Note that the standard error is the square root of the variance. Thus,
to reduce the standard error by one-half, one must explain 75% of the
variance (R=.87).
54
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potential predictors (Scheme B.2), we find fair but not excellent statistical
fits, about 45% variance explained for FP (R = .68). 'To illustrate the
difficulty involved, we note that -- in the direct process -- FP/(1-RH)
explains 50% of the variance in B (R =.70) with the development data set,
but that — in the inverse process — B(l-RH) explains only 35% of the
variance in FP (R = .59).
As a corollary to the above conclusion, we note that -- if FP were to
be predicted from airport data alone -- then including relative humidity
adds rather little (as long as the data are pre-screened for precipitation
and fog as are our data). The complicated regression including relative
humidity (Scheme B.2) does not perform much better than the simple linear
regression of FP versus extinction (Scheme B.I).
The third major conclusion is that we can obtain fairly reasonable
predictions of FP using airport visibility data in conjunction with Hi-Vol
particulate data. The linear regression equation of FP versus B and TSP
(Scheme C.I.a) yields only a 36% standard error for daily FP with the
development data set. There appears, however, to be an "overfitting"
problem in this equation, as evidenced by the poorer performance with the
annual and independent daily data sets. In an attempt to overcome the
overfitting problem, we reduced the number of regression variables to one
by pre-specifying the ratio of the B and TSP coefficients according to their
individual (zero-intercept) relationships with FP. This produced a slightly
greater error with the development data set (as it must) but improved
performance considerably with the annual and independent daily data sets.
As was the case with airport data alone, adding relative humidity (Scheme C.2)
led to little improvement in predictive accuracy.
Because of limitations in the size and coverage of the current data
set, the above evaluation of predictive schemes for FP cannot be regarded
as fully definitive. At present, we only conclude that airport visibility
data in conjunction with Hi-Vol TSP data can serve as a reasonably good
predictor of FP, with an error of about 35-50% for daily FP values and about
15-20% for annual mean FP. These error levels are nearly identical with those
achieved in the nationwide study using Hi-Vol data for TSP, SO^, and Pb
(Trijonis 1983b). When more data are available from the EPA IP Network,
the error levels in Table 3.6 as well as the coefficients for the equations
can be determined with greater precision.
55
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3.3.2 Prediction of IP
Table 3.7 presents an evaluation of various predictive equations
for IP. This table is similar to Table 3.6, except no results have been
included for predictive schemes involving relative humidity data. We
found that the relative humidity variable was even less significant for
IP than it was for FP.
In agreement with the conclusions of the nationwide study (Trijonis
19835), Table 3.7 indicates that IP concentrations can be predicted fairly
well from TSP data alone (Scheme A). In fact, the regression coefficient,
0.61, is identical to that of the nationwide study. Furthermore, the
predictive errors listed for Scheme A in Table 3.7 are generally consistent
with those reported in the nationwide study (31% for daily IP and 16% for
annual mean IP).
The results for Scheme B show that airport extinction alone is a
poor predictor of IP concentrations. However, using both airport extinction
data and Hi-Vol TSP data (Scheme C) does appear to improve performance
somewhat over the predictions based on TSP alone. Table 3.7 suggests that
IP can be estimated with less than a 30% error on a daily basis and less
than a 15% error on an annual basis using TSP data in conjunction with
airport extinction data. As was the case with the FP predictive schemes,
the IP predictive schemes can be formulated and evaluated more pre-
cisely when more data become available.
56
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53
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4.0 CHARACTERIZATION OF PARTICIPATE EPISODES USING AIRPORT DATA
The purpose of this chapter is to study the feasibility of using airport
data to characterize the spatial extent, transport patterns, and origins of
fine particle episodes. Section 3.1 discusses the information base and proce-
dures for the analysis. Section 3.2 presents several case studies of fine
particle episodes. Section 3.3 discusses our conclusions regarding the useful-
ness of the approach.
4.1 INFORMATION BASE
Three types of data are used in this chapter — maps of daily particulate
concentrations at EPA IP Network sites, maps of daily visibility levels at air-
ports, and information on daily regional wind patterns. As indicated earlier
in Section 2.4, the particulate concentration maps include only the 25 EPA
IP Network sites (see previous Figure 2.1) that provided adequate amounts of
data for the year, April 1980 - March 1981. Also, maps are prepared only on
days when most of those 25 sites reported data. Each map basically consists
of a listing of TSP, IP, FP, SO,, and Pb concentrations at the monitoring sites.
A single asterisk is used to denote concentrations a factor of 1.75 greater
than the region wide annual mean, and a double asterisk indicates concentrations
a factor of 2.50 greater than the region wide annual mean. Appendix A presents
a complete tabulation of all the particulate maps.
Maps of average daytime visibility levels were prepared using data from
70 carefully selected airports (see previous Figure 2.2). We decided to use
just visibility — without discounting for relative humidity — in these maps
because, as discussed in Section 3.3, simple regressions of fine particle
concentrations against extinction (i.e. visibility) alone yielded nearly as
good a correlation as the more complicated and less physically interpretable
regressions of fine particles against both extinction and relative humidity.
The visibility maps are shaded to indicate areas where visual range is in the
categories 0-3 miles, 3-6 miles, 6-10 miles, and above 10 miles. The process
of shading the maps (done manually) is quite straightforward because the airport
data are very consistent with respect to geographical variations. That is --
59
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we usually find large areas containing many sites with low visibilities
gradually blending into other large areas containing many sites with high
visibilities. The consistency and quality of airport data with respect to
geographical variations (Trijonis and Yuan 1978; Trijonis 1979, 1982a,
1982c; Husar et al. 1976a, b, 1979) is what makes this investigation
possible and useful.
In the original study plan for this project, we did not intend to
use wind data. However, we found that we could interpret transport pat-
terns on the visibility maps much more meaningfully with some knowledge of
overall wind conditions. To obtain an approximate indication of air flows,
24-hour average surface wind vector;; at 35 airports were examined. We
then prepared a qualitative description of wind flows (direction and
strength) based on the magnitude of the wind vectors and on the agreement
among the various wind stations. These qualitative descriptions are in-
cluded in the discussions of the next section and are summarized in rough
surface wind maps presented along with the visibility maps. We recognize
that surface wind data do not adequately represent synoptic scale transport
conditions, but the surface wind study was the only wind study possible
within the budget of the project.
The procedure for the case studies is as follows. First, episode
days of high fine particle concentrations are identified using the particulate
maps (Appendix A). Regional visibility maps are then prepared for several
days surrounding the episode day. The spatial extent, transport patterns,
and origins of the episode are interpreted using the visibility maps and
knowledge of general wind conditions. A specific comparison is also made
between the visibility map and particulate map on the episode day. It
should be noted that our interpretation of the visibility maps and transport
conditions is not without ambiguity. The uncertainties include the lack of
a precise quantitative relationship between fine particles and visibility,
the use of surface wind maps rather -:han synoptic trajectories, and the lack
'in an attempt to avoid serious local distortions produced by sea breezes or
mountain drainage flows, we excluded airports near large water bodies or in
mountain ranges.
60
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of a full, quantitative accounting of meteorological influences on visi-
bility (Martinez 1983).
As part of the interpretive analyses, it is useful to know important
emission source areas, especially for sulfates which are a major component of
fine particles and which tend to dominate visibility in the East. Figure 4.1
illustrates approximate sulfur oxide emission density by state. The greatest
emissions occur in the triangle from Illinois to Massachusetts to Tennessee.
The large states in the upper Ohio Valley exhibit particularly strong SCL
emission density.
It should be acknowledged that Husar et al. (1976a,b) were the first
to show that haze episodes in the East could be successfully tracked with air-
port visibility data. The present study attempts to extend the work of Husar
et al. by using more refined data sets (for both particulate matter and visi-
bility) and by formulating an operational procedure for EPA to use in
characterizing fine particle episodes.
4.2 EPISODE CASE STUDIES
This section presents several case studies to illustrate the use of
visibility maps in characterizing fine particle episodes. In analyzing these
case studies, we were generally very encouraged by the spatial correspondence
between visibility maps and particulate maps as well as by the reasonable
agreement between haze movement and wind direction. To avoid any confusion,
the reader should note that all wind directions discussed below refer to
winds from the specified direction.
4.2.1 30 June 1980 to 6 July 1980
The first particulate episode that we selected for study occurred on
2 July 1980. As shown in Figure 4.2, this day exhibited high fine particle
levels and very high sulfate levels in western Ohio as well as moderately
high sulfate and fine particle levels along the Eastern seaboard. As will be
discussed below, this episode is interesting because of its dramatic and
similar beginning and end.
Figures 4.3a to 4.3g present visibility maps for 30 June 1980 through
6 July 1980. As seen in Figure 4.3a, the East was relatively free,of haze on
61
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Figure 4.1 Sulfur oxide emission density map (NRC 1978; EPA 1976),
62
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122
79* 58 98
46* 39 59
21** 21 31
All units in ug/mj.
*: Value 1.75 times greater
than region*!de annual
mean.
: Value 2.5 times greater
than regionwide annual
mean.
Figure 4.2 Particulate concentration map for 2 July 1980.
63
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> 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded s :s?
Figure 4.3a Visibility map for 3C June 1980.
64
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V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
"v p: precipitation recorded
f: fog recorded xf 25
O''9ht ana variable *inds
(^/moderate flow
mstrong and sersistent flow
Figure 4.3b Visibility map for 1 July 1980.
65
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V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded
Figure 4.3c Visibility map for 2 July 1930.
66
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V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded
and variaole winds
moderate flow
ms;rong and sersistent flow
Figure 4.3d Visibility map for 3 July 1980.
67
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V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded 22
Figure 4.3e Visibility map for 4 July 1980.
-------
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15r
14
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visil
ities indicated in miles
precipitation recorded
_f: fog recorded
12" 10'
16"
13"
14"
Qlignt and variacle winds
P)moderate ^law
and aersistent flow
12
Figure 4.3f Visibility map for 5 July 1980.
-------
V > 10 miles
10 miles > V > 5 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded
in<3 ''ariaole winds
mode-ate
• scrong ana sersiscant flow
Figure 4.3g Visibility map for 6 July 1980.
7D
-------
30 June. The wind pattern for. this day was a very strong flow from the north-
west, except for the Gulf States and New England where the flow was somewhat
weaker. The strong northwesterly winds evidently had cleared the East of haze
except for the latter two subareas.
On 1 July, the wind flow of the previous day stagnated. The only major
transport evident was a southerly flow in the Great Lakes area. On this
day, visibility began to decrease over much of the East.
Winds were again generally light on 2 July, the only notable flow being
from the south up along the Appalachian and coastal area. The haziness
increased to a moderate episode, with some of the haze possibly transported
up to the New England area from the Central Atlantic states. Comparing 4.3c
to 4.2, it is apparent that the visibility data conform rather well with the
sulfate and fine particle data. At this point, the two most intense haze
pockets in the Ohio Valley and along the Eastern seaboard appear to have
accumulated separately from emissions in the two respective areas.
On 3 July, the relatively stagnant winds continued except for a minor
drift from the southwest. The haziness continued to increase, reaching very
high levels along the North Central Atlantic from New Jersey to Rhode Island,
The haze pocket from the Ohio Valley has apparently connected with the seaboard
pocket at this time.
A persistent northwesterly flow started to affect the New England and
North Atlantic area on July 4th. This area was blown relatively free of haze.
The remainder of the East experienced a less pronounced southerly flow, and
the haze layer expanded toward the north.
July 5th witnessed a strong westerly wind over the East. The wind turned
from westerly to southwesterly along the northeast seaboard. Apparently, this
transport wind pushed the hazy air mass toward the east. The haze pocket in
New Jersey on this day appears to be the remainder of the intense haze found
on the previous day south of the Great Lakes.
By 6 July, a dominant northwesterly flow again prevailed over most
of the area, bringing clear air over the Ohio Valley and Northeast. Very
similar to 30 June, little haze was left in the region; the remainder of
the episode seemed to be pushed to the south and east borders.
71
-------
4.2.2 11 July 1980 to 18 July 1980
The second episode selected for a case study occurred on 14 July 1980.
Figure 4.4 shows that this episode involved a band of moderately high fine
particle levels down the center of the region, with very high sulfates in
St. Louis. The far western and far eastern portions of the region exhibited
rather low particulate concentrations. This day has a puzzling aspect in the
sense that some locations in the eastern Great Lakes region recorded low
concentrations while other nearby locations recorded high concentrations.
Figure 4.5a shows that, on 11 July, a moderately intense haze covered
the entire area east of the Mississippi. Wind flows were from the west at
moderate to strong intensity on this day.
On 12 July, the general wind pattern continued westerly at reduced strength,
except in the New England/North Atlantic area where a strong northwesterly flow
prevailed. The haze on 12 July appears to have shifted slightly toward the
east from the previous day. Also, the New England and North Atlantic states
appear to have been cleared by the strong northwesterly flow in that area.
On July 13th, the strong northwesterly winds continued in the North
Atlantic and that area remained free of haze. Winds over the remainder of the
area were moderately intense -- northeasterly over the Ohio Valley and westerly
over the Gulf States. On this day, a severe haze pocket began to accumulate
in the western Ohio Valley.
Winds stagnated on 14 July, except for a strong southerly flow from the
Ohio Valley up through the Great Lakes area. This flow evidently transported
haze from the Ohio Valley up to Lake Superior and produced the elongated pat-
tern of Figure 4.5d. The spatial visibility pattern is generally consistent
with the fine particle concentrations of Figure 4.4, especially with respect
to the high sulfate in St. Louis and the low particulate concentrations in the
far western and far eastern borders of the region. The only anomaly concerns
trie moderately high particulate concentrations near Lake Erie in Figure 4.4.
A strong southwesterly flow became established in the northern two-thirds
of the study area on July 15th. This wind pattern evidently oushed the north-
72
-------
All units in ug/nr.
*: Value 1.75 times greater
than regionwide annual
mean.
: Value 2.5 times greater
than regionwide annual
mean.
Figure 4.4 Particulate concentration map for 14 July 1980,
73
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
_f: fog recorded
O''Sn: ana vartaole <
Qmoderjts flow
J
Figure 4.5a Visibility map for 11 July 1980.
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded
Qlignt and variaOle winds
flow
mstrong and sersistent flow
Figure 4.5b Visibility map for 12 July 1980.
75
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded /- 36
Qlignt ana var'aole winds
•node'ate Haw
ms;rong ana jersiswt -"low
Figure 4.5c Visibility map for 13 July 1980.
76
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded
ana variable winds
moderate flow
strong and oersistent flow
Figure 4.5d Visibility map for 14 July 1980.
77
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded / i 22
Qlignt ana vanaole wines
'low
msirjng ana 3«rsistsrt *'
Figure *.5e Visibility map for 15 July 1980.
/'b
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
r\ p: precipitation recorded
f: fog recorded
Qlignt ana variable *inds
moderate flow
ana sersistent flow
Figure 4.5f Visibility map for 16 July 1980.
79
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded
Qlignt ana vanaols -nnas
low
ana sersiscent -"To
Figure 4.5g Visibility map for 17 July 1980.
SO
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
visibilities indicated in miles
p: precipitation recorded
f: fog recorded
light and variable winds
r>moderate flow
ana sersistsnt flow
Figure 4.5h Visibility map for 18 July 1980.
81
-------
south haze band eastward, spread it out, and dispersed it somewhat (see
Figure 4.5e). The southern part of the region did not change much from the
14th to the 15th of July; winds there were light and variable.
On 16 July, a weaker southwesterly flow continued over the northern two-
thirds of the region, with variable winds still persisting in the south. The
winds turned more from the south and grew in strength along the Atlantic coast-
line. Figure 4.4f shows that this flow pushed the northern part of the haze
mass further east. Also, the more dense haze area in the south was evidently
transported northward up the Atlantic coast. The haze in the North Atlantic
states on this day appears to be a distinct transport phenomenon from the
south and west.
On July 17th, winds were westerly in the north and westerly to variable
in the south. The hazy airmass was evidently being shifted off into the
Atlantic. By July 18th,. most of the intense haze had left the region.
4.2.3 24 July 1980 to 3 August 1980
The third study period includes two sampling dates for the EPA IP Network
26 July (Figure 4.6) and 1 August (Figure 4.7). July 26th was the most severe
particulate episode that we found during the year. As seen in Figure 4.6,
extremely high fine particle and sulfate concentrations occurred in the Ohio
Valley, with moderately high sulfate levels stretching west to St. Louis and
east to the Central Atlantic states. On August 1st, very high fine particle
levels were measured in the North Atlantic area, and moderately high fine
particle concentrations -were recorded at Birmingham, Al.
We began tracking the episode on July 24th, when a moderately hazy
air mass resided in the southeast portion of the region (see Figure 4.3a).
Winds on that day were moderate and from the north along the Atlantic coast,
light to moderate and from the northeast in the center of the region, and
fairly strong from the southeast in Missouri through Wisconsin.
On July 25th, winds over the entire study area were fairly light and
mostly from the south. The haziness accumulated and shifted northward under
these light wind conditions.
32
-------
All units in ug/m .
: Value 1.75 times greater
than regionwide annual
mean.
: Value 2.5 times greater
than »-egionwide annual
mean.
Figure 4.6 Particulate concentration map for 26 July 1980.
33
-------
All units in ug/ra"'.
*• Value 1.75 times greater
than regionwide annual
mean.
Value 2.5 times greater
*han regionwide annual
siean.
Figure 4.7 Participate concentration map for 1 August 1980.
34
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded
Qlignt and variable winds
moderate flow
and sersistent *low
Figure 4.8a Visibility map for 24 July 1980.
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded
'-.^-te
;
Figure 4.3b Visibility map for 25 July 1980.
36
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded
Qlignt and variable winds
r/moderate flow
ng and oersistent flow
Figure 4.8c Visibility map for 26 July 1980.
37
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded
Figure 4.3d Visibility map for 27 July 1980.
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
visibilities indicated in miles
p: precipitation recorded
f: fog recorded
light and variable winds
moderate flow
mstrong and sers'istent flow
Figure 4.8e Visibility map for 28 July 1980.
89
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded
O'tgnt ana var-aoie w
r
flaw
Figure 4.8f Visibility map for 29 July 1980.
90
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded /\ M 10
and variable winds
moderate flow
ng and aersistent flow
Figure 4.8g Visibility map for 30 July 1980.
91
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded
sirong and sersistsnt fTow
Figure 4.8h Visibility map for 31 July 1980.
92
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded
var-iaole winds
moderate flow
mstrong ana serslstant flow
Figure 4.8i Visibility map for i August 1980.
93
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded
P>moderate 'low
Figure 4.3j Visibility map for 2 August 1980.
94
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Hies indicated in
precipitation recorded
recorded
Olight and var-aole winds
e flaw
and sersistent flow
Figure 4.8k Visibility map for 3 August 1980.
95
-------
Winds were even milder on July 26th, and the haze grew considerably more
severe. As seen in Figure 4.8c, a large area of reduced visibility covered
the Mississippi and Ohio River Valleys and extended eastward nearly to the
Atlantic. There was very intense haze from southern Wisconsin to central Ohio.
The spatial pattern of the visibility reduction corresponds quite well to the
sulfate and fine particle patterns of Figure 4.6.
On July 27th, winds were again generally light, except for transport from
moderate southerly wjnds along the northern Atlantic coastline and from moderate
northerly winds down the Mississippi. The hazy air mass continued to intensify
as it shifted slightly with the wind patterns. The northern Ohio Valley
experienced very extreme visibility reduction.
On 28 July, a strong southeasterly flow became established along the
Atlantic coastline. This flow was more easterly in the mid-Atlantic States
and more northerly in New England. Winds over the remainder of the study area
were variable. As seen in Figure 4.8e, the winds from the Atlantic evidently
pushed the haziness partly above New England and partly back westward.
Winds were light and shifting westerly on 29 July. The hazy airmass
moved accordingly slightly east. As the slight westerly winds continued on
the 30th and 31st, the haziness shifted further east. The 30th and 31st witnessed
very intense visibility reduction in Virginia. Some, if not most, of this haze
was evidently transported from the Ohio Valley.
On August 1st, winds grew in strength in the northern part of the
region and continued from approximately the same direction (west-southwest).
The area of most intense haze shifted northeast to the North Atlantic states
while some haze still persisted in the Gulf States. Again, there is good agree-
ment between tne visibility map (Figure 4.3i) and the particulate data (Figure
4.7). On August 2nd and 3rd, moderate to strong winds prevailed from the south-
west. The haze areas shifted and elongated to the northeast on August 2nd and
pushed eastward and northeastward on the 3rd.
4.2.4 22 January 1981
"he final case study involves an episode on 22 January 1981. As shown
;r, Figure
-------
All units in ug/m .
*: Value 1.75 times greater
than regionwide annuai
mean.
**. Value 2.5 times greater
than regionwide annual
mean.
Figure 4.9 Particulate concentration map for 22 January 1981:
97
-------
Nortnern Atlantic states. There were also moderately high fine particle levels
in the Ohio Valley. This episode is very different from the three previous
examples in several respects: (1) it is winter rather than summer, (2) lead
concentrations are at very high levels, and (3) sulfate concentrations are
moderate.
Figure 4.10 presents a visibility map for January 22nd. It is evident
that the spatial pattern of visibility reduction does not agree that well with
the spatial pattern of fine particle concentrations. The greatest fine particle
concentrations occur in the North Atlantic states where haze is only moderate.
Conversely, moderately high fine particle concentrations occur along Lake Erie
where visibility reduction is very intense. The basic problem is that precipi-
tation and fog are occuring throughout the East on this day. Although some of
the visibility reduction is arising from fine particles, much of the visibility
reduction also is being produced by weather events. Because of the important
role of natural visibility reduction on this day, it is not possible to use
visibility maps to trace the transport and spatial extent of fine particles.
This example serves as a caveat against blind application of the visibility
mapping technique.
4.3 POTENTIAL USES FOR THE METHODOLOGY
The preceding case studies have shown that airport visibility data are
of good quality and utility for studying geographical variations of haziness
and fine particle concentrations. In several cases, the visibility data have
provided a qualitative description of the spatial extent, transport patterns,
and origins of large-scale fine particle episodes. It remains to be answered,
however, how the results might be of operational use to EPA.
One potential use of the approach is to establish the general necessity
for interstate considerations in control regulations for fine particles or
sulfates. Based upon our results arid other previously cited publications,
it is evident that sulfates and fine particles can accumulate and transport
on a spatial scale that far transcends state boundaries. Controlling fine
particles and sulfates requires a large-scale regional policy.
The approach may also be useful in seme specific circumstances. One
example involves regulations to achieve short term standards for fi.ne or
98
-------
V > 10 miles
10 miles > V > 6 miles
6 miles > V > 3 miles
3 miles > V
Visibilities indicated in miles
p: precipitation recorded
f: fog recorded
Qlignt and vanaDle winds
n>moderate flow
»strong and persistent flow
Figure 4.10 Visibility map for 22 January 1981.
99
-------
inhalable particles. Such regulations would be based on an assessment of the
sources for specific short term particulate episodes. The visibility mapping
technique could help to establish whether or not the origins for the specific
episodes were local or interstate. In fact, anytime EPA or a state conducts
an extensive study of a short-term particulate episode, visibility maps (with
even greater local resolution than our maps) could orovide very useful insights
at very little cost.
The main drawback of the approach is that it is not quantitative. As
noted in the previous chapter, airport visibility data cannot be used as a
precise substitute for daily fine particle data. Furthermore, although
visibility maps can indicate general transport patterns and source areas,
they do not provide a quantitative breakdown as to contributions from local
sources versus transport. Thus, the visibility mapping technique is most
useful only in providing a qualitative understanding of spatial scales and
transport relationships.
100
-------
5, REFERENCES
Allard, D. and I. Tombach, "Evaluation of Visibility Measurement Methods in
Eastern United States," Presented at the 73rd Annual Meeting of the Air
Pollution Control Association, Montreal, June 22-27, 1980.
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With Examples in Los Angeles," Atmospheric Environment, Vol. 13, pp. 1069-
1084, 1979.
Cass, G.R., P.M. Boone, E.S. Macias, "Emission and Air Quality Relationships
for Atmospheric Carbon Particles in Los Angeles," in Particulate Carbon:
Atmospheric Life Cycle, G.T. Wolff, R.L. Klimisch, eds., Plenum Press,
New York, 1981.
Conk!in, M.C., Cass, G.R., Chu L-C, and Macias, E.S., "Wintertime Carbonaceous
Aerosols in Los Angeles, An Exploration of the Role of Elemental Carbon,"
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E.S. and Hopke, P.K., ACS Symposium Series, 167, American Chemical
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Countess, R.J., S.H. Cadle, P.J. Groblicki, and G.T. Wolff, "Chemical Analysis
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Faxvog, F.R. and D.M. Roessler, "Carbon Aerosol Visibility Vs. Particle Size
Distribution," Applied Optics, Vol. 17, 1978.
Ferman, M.A., G.T. Wolff, and N.A. Kelly, "The Nature and Sources of Haze in
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101
-------
Ho W W G.M. Hidy, and R.M. Govan., "Microwave Measurements of the Liquid
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Range Transport from Visibility Observations, Trajectory Analysis and
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M.A. Wojcik, and M.J. Hillyer, "Tne Development of Mathematical Models for
the Prediction of Atmospheric Visibility Impairment," EPA-450/3-78-110a,b,c,
1978.
Leaderer, B.P. and J.A. Stolwijk, "Optical Properties of Urban Aerosol and Their
Relation to Chemical Composition," Presented at the New York Academy of
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January 9-12, 1979.
Malm, Wm. C., "Visibility: A Physical Perspective," Proceedings of the Work-
shop in Visibility Values, Fort Collins, Colorado, January 28-February 1,
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Measurements Made by Various Visibility Monitoring Instruments," Proceedings
of Conference on View on Visibility -- Regulatory and Scientific, pp. 222-
u o x j i, y /./ •
Martinez, E.L., EPA Office of Air Quality Planning and Standards, Personal
Communication, January 1983,
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National Academy of Sciences, Washington, D.C., 1978.
NOAA (National Oceanic and Atmospheric Administration), Climatic Atlas of the
United States, National Climatic Center, Asheville, NC, 1977.
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Pierson, W.R., W.W. Brachaczek, T.J. Truex, J.W. Butler, and T.J. Korinski,
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Pitchford, M., "The Relationship of Regional Visibility to Coarse and Fine
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102
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Stelson, A.W. and J.H. Seinfeld, "Chemical Mass Accounting of Urban Aerosol,"
Envir. Sci. Techno., 15_, pp. 671-679 , 1981.
Tang, I.N., "Deliquescence Properties and Particle Size Change of Hygroscopic
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Trijonis, J., "Existing and Natural Background Levels of Visibility and Fine
Particles in the Rural East," Atmospheric Environment, Volume 16,
pp. 2431-2445, 1982a. "
Trijonis, J., "Impact of Light-Duty Diesels on Visibility in California,"
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Trijonis, J., "Development and Application of Methods for Estimating Inhalable
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Trijonis, J., "Visibility in California," Journal of the Air Pollution Control
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Trijonis,J. and D. Shapland, "Existing Visibility Levels in the U.S., Isopleth
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450/5-79-010, 1979.
Trijonis, J. and K. Yuan, "Visibility in the Northeast: Long-Term Visibility
Trends and Visibility/Pollutant Relationships," EPA-600/3-78-075, 1978.
Weiss, R.E. and A.P. Waggoner, "The Importance of Aerosol Absorption and
Graphitic Carbon in Visibility and Atmospheric Optics," Presented at the
74th Annual Meeting of the Air Pollution Control Association, Philadelphia,
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White, W.H. ed., "Plumes and Visibility: Measurements and Model Components,"
Atmospheric Environment (Special Issue, 15-10/11. pp. 1785-2406, 1981.
1C3
-------
White, W.H. ana P.T. Roberts, "On trie Nature and Origins of Visibility-
Reducing Aerosols in the Los Angeles Air Basin," Atmospheric Environment
Vol. 11. p. 803, 1977.
Williamson, S.J., Fundamentals of Air Pollution, Addison Wesley, Reading,
Massachusetts, 1973.
Wolff, G.T., R.J. Countess, P.J. Groblicki, M.A. Ferman, C.H. Cadel, and
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Sources and Temporal Patterns," General Motors Research Laboratory
Publication GMR-3391, 1930.
104
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APPENDIX A
PARTICULATE CONCENTRATION MAPS FOR THE EPA IP NETWORK
-------
75 times greater
tfian regionwide annual
mean.
: Value 2.5 times greater
thin rsgionwide annual
mean.
3 April 1930
A-l
-------
NOTES
All units in ug/mj.
*: Value 1.75 times greater
than regionwide annual
mean.
: Value 2.5 times greater
than regionvnde annual
mean.
21 April 1980
A-?
-------
NOTES
All units in ug/m .
*: Value 1.75 times greater
tnan regionwide innual
•nean.
•. Value 2.5 times greatar
tnan regionwide annual
nean.
3 May 1980
A-3
-------
All units in ug/m .
*: Value 1.75 times greater
than regionwide annual
mean.
Value 2.5 times greater
tftan regionwide annual
mean.
15 May 1980
A-4
-------
Al 1 jnlts in ug/m .
Value 1.75 times 3r«acsr
tnan rsgionwide annual
nean.
Value 2.S times jrester
than r«gionwiae annual
2 Julv 1S80
-------
All units fn ug/ra .
*: Value 1.75 times greater
than regionvn'de annual
mean.
: Value 2.5 times greater
than regionwide annual
mean.
14 July 1980
A-fi
-------
All units frt ag/ra .
*: Value 1.75 times greater
tnan regtorrwide annual
•near.
: Value 2.5 times areater
than regionwide annual
mean.
2fi .luiv 1980
A-7
-------
All units in ug/ra .
• Value 1.75 times greater
than regionwide annual
mean.
Value 2.5 times greater
than regionwide annual
mean.
August 1 1980
-------
MOTES
ATI jnlts in ug/rnj.
: Value 1.75 times greater
than regionwide annual
mean.
*: Value 2.5 times greater
than '•egionxide annual
"lean.
13 Auqust 1980
A-9
-------
All units in ug/m .
*: Value 1.75 times greater
than regionwide annual
mean.
: Value 2.5 times greater
than regionwide annual
mean.
31 August 1980
A-10
-------
All jnlts in ug/m .
*: /alue 1.7S times greatsr
*fian regionwide annual
iwan.
: Vaiue 2.5 times greater
t.lan r«gionwide annual
mean.
6 September 1980
A-11
-------
All units in ug/m .
*: Value 1.75 times greater
than regionwide annual
mean.
Value 2.5 times greater
than regionwide annual
mean.
12 Seotember 1980
-------
Value 1.75 t-.mes greater
Man regionwtae jnnual
nean.
Vaiue 2.5 limes 3r»ater
than ••efl'inwide annual
mean.
12 October 1930
-------
All units in ug/mj.
Value 1.75 times greater
than regionwide annual
mean.
**: Value 2.5 times greater
than regionwide annual
mean.
18 October 1980
-------
All jnlts in ug/m .
: Value 1.75 times greater
than regicmnae annual
mean.
**: Value 2.S times greater
than regionwlde annual
.•nean.
30 October 1980
A-1
-------
All units in ug/m .
*: Value 1.75 times greater
than regionwide annual
mean.
**: Value 2.5 times greater
than regionwide annual
mean.
5 November 1980
-------
All units in ug/mj.
"*: Value 1.75 times greater
than regionwide annual
mean.
Value 2.5 times greater
than regionwide annual
nean.
11 November 1980
A-17
-------
All units in ug/m .
: Value 1.75 times greater
than regionwide annual
mean.
Value 2.5 times greater
than regionwide annual
mean.
17 November 1980
-------
Value 1.75 times greater
than regionwiae annual
nean.
Value 2.S times greater
tJian regionwide annual
mean.
23 Novemoer 19SC
A-19
-------
AH units in
*: Value 1.75 times greater
than regionwide annual
mean.
. Value 2.5 times greater
than regionwide annual
mean.
5 December 1980
A-20
-------
FORMAT
TSP
IP
FP
> so=
/ Pb
\ 57
/ 31V
/ ^
rz^<**
-^ , w w.
iS 23-o 1 «
15 13 27 /
7 10 14
? — 4 ' '—
O " --/'
=
9^— w'
a i
/
{
_^
H
/=3 ^
^ « — """
__
c
_— — ""~T"
.Vo^
32 '
13 *
/ " ;-j
40
20
All jnits in ug/m .
*: Value 1.75 times greater
tnan r^gionwide annual
•nean.
Value 2.5 times area tar
than ragionwide annual
mean.
11
1980
A-21
-------
All units in ug/mj.
*: Value 1.75 times greater
than regionwide annual
mean.
Value 2.5 times greater
than regionwide annual
mean.
17 December 1980
A-22
-------
AIT units in ug/m .
*: Value 1.73 times grsater
than rsgtonwide annual
mean.
Value 2.5 times jrwter
rsgionwide annual
22 January 1981
-------
All units in ug/m .
: Value 1.75 times greater
than regionwide annual
mean.
**: Value 2.5 times greater
than regionwide annual
mean.
9 February 1981
-------
All units In ug/mj.
x: Value 1.75 times greater
than reoionwice annual
: Value 2.5 times greater
than i-egionwide annual
flean .
15 February 1981
-------
All units in ug/m .
: Value 1.75 times greater
than regionwide annual
mean.
: Value 2.5 times greater
than regionwide annual
mean.
5 March 1981
A-26
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
REPORT NO.
EPA-450/4-84-008
3. RECIPIENT'S ACCESSION NO.
..TITLE AND SUBTITLE
Analysis Of Particulate Matter Concentrations And
Visibility In The Eastern U. S.
S. REPORT DATE
August 1984
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
John Trijonis
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Santa Fe Research Corporation
Santa Fe, NM 87501
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
68-02-3578
12. SPONSORING AGENCY NAME AND ADDRESS
U. S. Environmental Protection Agency
Office Of Air Quality Planning And Standards
Monitoring And Data Analysis Division
Research Triangle, NC 27711
13. TYPE OF REPORT AND PERIOD COVERED
14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
EPA Project Officer: Thompson G. Pace
16. ABSTRACT
An analysis is conducted of EPA IP Network data for the eastern U.S. of participate
concentrations and airport data for visibility and relative humidity. Physically meaning-
ful regression equations are used to determine light extinction (visibility degradation)
as a function of aerosol concentrations and relative humidity. As expected from optical
theory, the results indicate that fine particles (FP) are much more closely tied to
visibility than inhalable particles (IP) or total suspended particles (TSP). Sulfate
particles have a much greater extinction efficiency (per unit mass) than nonsulfate TSP
and nonsulfate IP; fine sulfate particles also appear to have a somewhat greater extinc-
tion efficiency than fine nonsulfate particles. The results suggest that sulfates and
associated water account for approximately 45% of total light extinction in the East.
Dichotomous FP and IP concentrations can be predicted fairly well from airport visibility
data in conjunction with Hi-Vol TSP data, with standard errors of prediction about 30-40%
on a daily basis and 16-17% on an annual basis. It is shown that airport visibility data
are of good quality for analyzing the spatial/temporal extent of fine particle episodes
in the East. Visibility maps provide important qualitative insights regarding the spatial
extent, transport patterns, and origins of fine particle episodes.
17.
K€Y WORDS AND DOCUMENT ANALYSIS
a.
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS C. COSATI Field/Group
18. DISTRIBUTION STATEMENT
19. SECURITY CLASS (This Report)
21. NO. OF PAGES
138
20. SECURITY CLASS
22. PRICE
EPA Form 2220-1 (R**. 4-77) PREVIOUS EDITION is OBSOLETE
-------
INSTRUCTIONS
1. REPORT NUMBER
Insert the EPA report number as it appears on the cover of the publication.
2. LEAVE BLANK
3. RECIPIENTS ACCESSION NUMBER
Reserved for use by each report recipient.
TITLE AND SUBTITLE
^itle should indicate clearly and briefly the subject coverage of the report, and be displayed prominently. Set subtitle, if used, in smaller
pe or otherwise subordinate it to main title. When a report is prepared in more than one volume, repeat the primary title, add volume
mber and include subtitle for the specific title.
5. REPORT DATE
Each report shall carry a date indicating at least month and year. Indicate the basis on which it was selected (e.g., date of issue, date of
.••Pj.:o\-al, date of preparation, etc.).
6. PERFORMING ORGANIZATION CODE
Leave blank.
7. AUTHOR(S)
Give name(s) in conventional order (John R. Doe, J. Robert Doe, etc.}. List author's affiliation if it differs from the performing organi
zauon.
8. PERFORMING ORGANIZATION REPORT NUMBER
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9. PERFORMING ORGANIZATION NAME AND ADDRESS
Give name, street, city, state, and ZIP code. List no more th:m two levels of an organizational hirearchy.
TO. PROGRAM ELEMENT NUMBER
Use the program element number under which the report was prepared. Subordinate numbers may be included in parentheses.
11. CONTRACT/GRANT NUMBER
Insert contract or grant number under which report was prepared.
12. SPONSORING AGENCY NAME AND ADDRESS
Include ZIP code.
13. TYPE OF REPORT AND PERIOD COVERED
Indicate interim final, etc., and if applicable, dates covered.
*
14. SPONSORING AGENCY CODE
Insert appropriate code.
15. SUPPLEMENTARY NOTES
Enter information not included elsewhere but useful, such as: Prepared in cooperation with, Translation of, Presented'at conference of,
To be published in. Supersedes, Supplements, etc.
16. ABSTRACT
Include a brief (200 words or less) factual summary of the most significant information contained in the report. If the report Contains a
significant bibliography or literature survey, mention it here.
17. KEY WORDS AND DOCUMENT ANALYSIS
(a) DESCRIPTORS • Select from the Thesaurus of Engineering and Scientific Terms the proper authorized terms that identify the major
concept of the research and are sufficiently specific and precise to be used as index entries for cataloging.
(b) IDENTIFIERS AND OPEN-ENDED TERMS - Use identifiers for project names, code names, equipment designators, etc. Use open-
ended terms written in descriptor form for those subjects for which no descriptor exists.
(c) COSATI FIELD GROUP - Field and group assignments ar: to be taken from the 1965 COSATI Subject Category List. Since the ma-
jority of documents are multidisciplinary in nature, the Primary Field/Group assignment! s) will be specific discipline, area of human
endeavor, or type of physical object. The application(s) will be cross-referenced with secondary Field/Group assignments that will follow
the primary posting(s).
18. DISTRIBUTION STATEMENT
Denote releasability to the public or limitation for reasons other than security for example "Release Unlimited." Cite any availability to
the public, with address and price.
19. &20. SECURITY CLASSIFICATION
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21. NUMBER OF PAGES
Insert the toul number of pages, including this one and unnumbered pages, but exclude distribution list, if any.
22, PRICE
Insert the price set by the National Technical Information Service or the Government Printing Office, if known.
EPA Form 2220-1 (R«v. .4-77) (R«»«fi«)
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