HYGROSCOPIC AEROSOLS
Lothars H. Ruhnke and Adarsh Deepak (Eds.)
1984 BY A. DEEPAK PUBLISHING
ISBN 0-937194-02-6
The Properties and Climate of Atmospheric Haze
Rudolf B. Husar and Janet M. Holloway
Center for Air Pollution Impact and Trend Analysis (CAPITA)
Washington University, St. Louis, Missouri
The relationship between the haze and aerosol number concentration, mass concentration,
and the resulting optical properties is examined. The number concentration is a poor index of
aerosol optical properties. The fine particle mass concentration shows a remarkably high
correlation coefficient with the visible light scattering coefficient, having a slope of 3.5 m/g. The
spatial-temporal invariance of the mass efficiency factor is due to the relatively stable size
distribution of the fine particles and also because of the weak size dependence of the mass
efficiency factor. The aerosol light scattering phase function obtained by numerous investigators
shows a systematic shift toward the forward angles, with increasing extinction coefficient. On
the other hand, spectral extinction data show a systematic decline of wavelength dependence,
with increasing bext. Both of these regularities of haze aerosol optics are consistent with the
notion that with increasing bext, the characteristic aerosol size increased, causing forward shift
and discoloration of the light extinction. These regularities allow estimating both the shape of
the phase function and the spectral dependence of extinction once bext is established.
In the second part of this review the spatial-temporal pattern of atmospheric haze is reviewed,
with special emphasis on North America. The extent and limitations of visual range
observations and turbidity measurements are discussed. The worst visual range is recorded over
the eastern half of the United States, which also experienced the strongest increase of haziness
since 1948. These observations are supported by vertical depth data from the WMO network. It
is concluded that it would be most desirable to expand the establishment of a haze climate for the
regions of the world outside of North America.
INTRODUCTION
Aerosols severely perturb both human and electronic vision through the atmosphere in the
visible and near infrared spectrum. While clouds completely obscure vision in a large part of the
electromagnetic spectrum, the haze perturbation is extremely variable in wavelength, space, and
time. The image or other signal deterioration by a hazy atmosphere constitutes one of the main
limiting factors in the performance of many sophisticated sensors, including the human eye. By
establishing a climatology of haze, the potential performance of remote-sensing devices can be
evaluated for most locations of the world. Also the knowledge of haze characteristics can yield
input specifications to the design of new sensors either for minimizing or enhancing their aerosol
detection capabilities.
Over the past decade there has been an immense improvement in the understanding of aerosol
behavior and optical properties arising from research in the civilian sector. It is hoped that this
review can reduce unnecessary duplication of research effort and save future resources for new
developments.
Atmospheric aerosols under consideration include man-made haze, composed mainly of
sulfates, organics and soot, the natural continental haze from burning and decomposition, windblown dust, and sea salt aerosol. In most locations over the globe the atmospheric aerosol
consists of a mixture of all of these sources.
RELATIONSHIP BETWEEN AEROSOL NUMBER, MASS
CONCENTRATION, AND OPTICAL PROPERTIES
Ever since John Aitken developed a condensation nuclei counter, a quest has been in
progress to establish a generally applicable quantitative relationship between aerosol
concentrations, sizes, and their effects on atmospheric optics. Aitken (1888) himself noted that
at remote locations near the British seashore, an increase of the condensation nuclei count was
accompanied by a decrease of the visual range. In fact, he found that the product of the visual
range (V) and the condensation nuclei count (CNC) was constant. This first quantitative "law"
was supported by Wigand (1919) in Germany and he also refined Aitken's law by incorporating
the effect of the air humidity. The applicability and limitations of the visual range and CNC
relationship was reviewed by Burckhardt and Flohn (1939), in which they came to the reasonable
conclusion that the above product (V(CNC)) is highly variable in space and time and is of
limited general use.
Early in this century Kimball and Hand (1924) followed up this work in the United States
by determining the aerosol number concentration by microscopic counting of particles deposited
in the Owens impinger. The device is a cross between a condensation nuclei counter and an
impactor in that the air is first humidified, expanded, and then impacted. According to Junge
(1952), the Owens counter yields the total number concentration greater than approximately 0.2
m. For Washington, DC, and several other sites in the United States, the product of visual
range times the Owens count (OC) has remained remarkably constant even without consideration
of the relative humidity effect. In Britain, Wright (1935) conducted a similar study and
confirmed that Kimball's value of the V x OC was also applicable for the fine particle population
over Britain in the 1930s. These powerful regularities of aerosol optical behavior prompted
Linke (1943) to bring these developments to the attention of German scientists. In the postwar
period there was little attention given to these valuable earlier observations.
A relationship that has received much more attention is that between the slope of the
spectral extinction coefficient (Angstrom exponent) and the slope of the aerosol size distribution
(Junge exponent). According to Angstrom (1929), the extinction coefficient or aerosol optical
depth as a function of wavelength  may be empirically fitted by a power law expression -n
where n is the Angstrom exponent. The Junge aerosol size distribution law also assumes a
power law dependence of the aerosol concentration against particle size, n (log r)  r -m.
Mathematically, these empirical laws were consistent in that the power-law size distribution
along with the Mie theory for light scattering, in fact, yields a power-law wavelength dependence
of extinction (Junge, 1952). This consistency gave strong reinforcement to both Angstrom's and
Junge's laws. Deviations from either law were called "anomalies" and were supposed to happen
only "once in a blue moon".
In the early 1950s, Foitzik proposed that the measured spectral extinction data would be
better fitted with a comparatively narrow log normal distribution, centered (in size) around the
wavelength of incident radiation, rather than with a power-law function. Around 1960, Gotz and
co-workers developed an aerosol centrifuge with high size resolution in the 0.1 to 1.0 m
diameter range and found that the size distributions exhibit a variety of shapes (usually not
power law) depending on the sampling location and time. Fenn (1960, 1964) obtained high
resolution size distribution data (0.1 < Dp < 1.0 m) with a Gotz spectrometer and found, at a
variety of locations, mulitmodal size distributions. They supported the multimodality of the
"optical subrange" (0.1 to 1.0 m) by measured aerosol phase function data for which the "best
fit" size distributions were also multimodal. Foitzik (1965) used Fenn's size distribution data to
calculate the corresponding spectral extinction values and concluded that these values were more
typical for many locations than those satisfying Angstrom's law. Badayev and co-workers
(1975) at the Institute of Atmospheric Optic, Moscow, measured the spectral extinction
coeffecient with a transmissometer (0.25 <  < 2.2 m) and invented the size distribution of the
typical haze. Their result is again a bimodal volume distribution within the fine particle mode, in
accordance with Fenn's direct measurements. In spite of the compelling evidence presented by
Foitzik, Gotz, and Fenn on the limitations and use of Angstrom's and Junge's laws, they
prevailed as the "accepted" models well into the 1970s.
In 1969, Whitby and co-workers (Whitby et al., 1972 Husar et al., 1972) performed an
extensive set of size distribution measurements in the Los Angeles smog and proposed the
"bimodal" distribution of aerosol volume, consisting of the fine particle mode 0.1 < Dp < 2.0 m
and the coarse particle mode 2.0 < Dp < 20.0 m. The bimodal volume distribution has rapidly
gained acceptance because it was supported by size distribution data from other locations and by
the strong bimodality of the aerosol chemical composition. By the late 1970s, the "Whitby
distribution" was used for many applications where an aerosol "model" was required. It is, for
instance, being incorporated in atmospheric electro-optical transmission models, such as
LOWTRAN.
Unfortunately, the bimodal distribution still did not explain the observations of Fenn
(1964): that the aerosol distribution (if measured with sufficient resolution) is multimodal within
the fine particle mode (or "accumulation" mode), 0.1< Dp < 1.0 m. This fine structure in size
structure may be inconsequential for some applications, but has significance to aerosol optics: for
a given aerosol mass the aerosol backscattering is dominated by particles in the size range D p 
(0.2 to 0.5), whereas the extinction coefficient per mass is strongest for Dp  0.5 to 0.8 m.
By the 1970s, it was recognized that there are distinct advantages to monitoring the
aerosol mass concentration in at least two size-segregated classes, the fine particle mass 9FPM)
and the coarse particle mass (CPM). The separation size is normally set at the saddle point of the
biomodal mass distribution (1 to 3 m). From physical considerations, first stated explicitly by
Wegener (1911), most of the light scattering by haze is contributed by particles in the 0.2 to 2.0
m size range. More recently, there has been a statistical confirmation of the strong relationship
between the fine particle mass and the light scattering coefficient measured by the integrating
nephelometer. In various parts of the United States, simultaneous monitoring of bscat and FPM
yielded correlation coefficients exceeding 0.9, and the ratio of 3 to 4 m2/g, as given in Table 1.
The remarkable feature of these data sets is the narrow range of the ratio of b scat to FPM
for various sampling locations, times, etc. This ratio has units of m2 of light scattering per unit
mass of aerosol. It is thus a scattering efficiency factor, and its absolute magnitude is directly
comparable with calculations of light scattering using Mie theory. The spatial-temporal
invariance of the scattering efficiency factor has two possible explanations: either the size
distribution of fine particles that contribute to light scattering is invariant or the scattering per
unit mass of FPM is independent of particle size. As will be discussed later, it is a combination
of both.
Table 1. Correlation of Fine Particle Mass With Light Scattering Coefficient [r2 is coefficient of
determination, square of correlation coefficient; N is number of points]
Location
m2/g
r2
N
Reference
Riverside, CA
0.88
88
Lundgren, 1970
Los Angeles, CA 3.7
0.69
39
Samuels et al., 1973
Oakland, CA
3.2
0.62
20
Samuels et al., 1973
Sacramento, CA
4.4
0.96
6
Samuels et al., 1973
Los Angeles, CA 3.2
0.83
58
White and Roberts, 1981
Portland, OR
3.2
0.90
108
Waggoner and Weiss, 1980
Mesa Verde, CO
2.9
5
Waggoner and Weiss, 1980
Puget Isl., WA
3.0
0.94
26
Waggoner and Weiss, 1980
Seattle, WA
3.1
0.90
58
Waggoner and Weiss, 1980
Seattle, WA
3.2
0.94
64
Waggoner and Weiss, 1980
Denver, CO
3.3
0.96
268
Groblicki et al., 1981
Houston, TX
3.3
0.88
88
Dzubay et al., 1982
This brief review, along with numerous reports not discussed here, reveals a century-long
search for laws that describe the regularities of aerosol optics, aerosol size distributions, and their
interdependence. However, at this time, generally applicable laws that consistently describe the
size distribution and optical properties of atmospheric fine particles are not at hand. The
increasing need for electro-optical information about the atmosphere, along with the encouraging
hints gathered over the past century dictate that the search be pursued.
RELATIONSHIPS AMONG THE OPTICAL PARAMETERS
Data on the optical properties of atmospheric aerosols are much more abundant than
those for aerosol properties obtained by nonoptical methods. It is, therefore, beneficial to search
for regularities in the relationship of various aerosol optical parameters and ask what changes in
the size distribution can explain the observed regularities.
Special extinction
The spectral extinction data of Foitzik (1938) and Middleton (1935) (Fig. 1) show that the
blue/green extinction ratio and the red/green extinction ratio converge systematically to unity as
the extinction coefficient increases from near Rayleigh scattering to fog. Thus, the spectral
extinction of aerosol (defined by Middleton as air plus particles) becomes increasingly "white"
with larger values of extinction. An interesting abrupt transition from spectral to white scattering
at bext = 0.5 km-1 has been used by Eldridge (1969) to define "mist," that is, the transition phase
between haze and fog. Middleton (1952) also brings to our attention the compilation of spectral
extinction data by Wolff (1938) (Fig. 2) who points out that the Angstrom exponent is essentially
zero for visual range below 1 km but increases to about unity at 6 km visual range. For an
aerosol-free Rayleigh atmosphere (about 200 km visual range), the Angstrom exponent should
approach n = 4.
Figure 1. Mean values of relative extinction coefficient as a function of the mean coefficient for
the three colors (Foitzik, 1938).
Figure 2. Relation between V and n (Wolff, 1938, summary with additional observations by
Lohle, 1941).
Aerosol Phase Function
The regularities of the aerosol phase function have also been examined, at least since the
studies of Foitzik and Zschaeck (1953). They observed a systematic shift of light scattering
toward forward angles with increasing extinction coefficient (decreasing visual range) (Fig. 3).
Figure 3. Variation of volume scattering function with scattering angle for several visibilities,
measurements by Foitzik and Zschaeck (1953).
An impressive systematic follow-up investigation conducted by Barteneva (1960) at five
locations in the Soviet Union also shows the forward elongation of the phase function with
decreasing visual range. These regularities of phase function were also confirmed in the
OPAQUE measurement program conducted in the United States and Europe using a polar
nephelometer (Johnson, 1981) (Fig. 4).
Figure 4. Comparison of multispectral directional scattering ratios measured by Visibility
Laboratory airborne nephelometer (a) and ground-based nephelometer (b) with the photopic
ratios from Barteneva (1960).
Barteneva (1960) reported measurements of aerosol phase function, calculated the light
scattering asymmetry factor, and plotted the values against visual range. We first digitized her
data and subsequently transformed it to a new coordinate system: asymmetry factor versus
extinction coefficient (3.9/V)as given in Figure 5.
Figure 5. Asymmetry factor versus visual range (a) and extinction coefficient (b) using data
from Barteneva (1960).
Backscattering
One of the more convenient means of monitoring the light scattering by aerosols is by
detection of the light scattered backwards to the light source, whether the source be coherent or
incoherent.
Curcio and Knestrick (1958) monitored the backscattering intensity (R)
simultaneously with the visual range and obtained a remarkably well-defined relationship for
Arcotto and Washington, DC: V = Constant/R1.5. In the USSR, Gavrilov (1966) reported and
equally consistent relationship between total extinction and backscattering. The merging and
superposition of data sets from different investigators is illustrated in Figure 6. The
backscattering intensities plotted against the visual range data of Curcio and Knestrick (1958)
were first digitized, plotted against bext, and then superimposed on similarly processed
compatible data of Gavrilov (1966). The comparison showed the internal consistency of each set
but a discrepancy (due to differences of the monitoring angle?) between the data sets.
Figure 6. Backscattering intensities plotted against visual range; data from Curcio and Knestrick
(1958) and Gavrilov (1966).
Impressed by Barteneva's phase function data and the backscattering data of Curcio and
Knestrick, Fenn (1966) examined the consistency of these two data sets with favorable results.
Both the phase function and backscattering data show that with increasing extinction coefficient,
the backscattering phase function declines systematically.
PHYSICAL EXPLANATIONS FOR THE OBSERVED REGULARITIES
As soon as atmospheric data were generated showing well behaved relationships among
the aerosol optical parameters, attempts have been made to explain them based on fundamental
physical laws. By far the most important tool for that purpose is the "Mie theory" which
describes quantitatively the interaction of spherical particles with electromagnetic radiation.
Thus, the calculation of essentially all the optical properties (the Stockes matrix) can be
performed in a straightforward manner for any polydispersion provided the size distributions and
refractive indices are known. The main difficulty in performing consistency tests among the
aerosol optical parameters arises from the fact that the size distributions and refractive indices of
the aerosol population are not known. Also, the role of specific processes that shape the aerosol
size distribution was highly uncertain.
Inversion of Optical Data
Following the Krakatoa eruption in 1883, the abundance of optical data prompted
investigators to seek information about the aerosol size distribution via "inversion" or
deconvolution of the optical data. As early as 1884, Kiessling concluded that the spectacular
optical phenomena worldwide were caused by the volcanic dust with characteristic size between
1 and 2 m. Wegener (1911) used simple but most elegant reasoning (without involving the Mie
theory) when he stated that most of the atmospheric haze that scatters the visible light is
comparable in size to the wavelength of light. Gotz (1934) brought to the attention of
atmospheric scientists the solution of the Mie equations by Stratton and Houghton (1931) who
calculated the scattering efficiency factors for increasing ratios of particle diameter to
wavelength. Given the haze optical properties and Mie theory, size distributions were sought
that "explained" the data. Penndorf (1957) "inverted" the spectral extinction data obtained in
Europe following a major forest fire in western Canada. He concluded that the peculiar blue sun
and blue moon phenomena of that time were due to aged forest fire smoke of 0.6 to 0.8 m.
Volz (1954) gave a most illuminating graphical illustration of the influence of individual particle
size classes on the extinction coefficient in different wavelengths. (The Volz technique of
"inversion" has been adopted for use with modern interactive graphics software and hardware
described in the next section.). In the 1940s, Siedentopf (1947) measured the aerosol phase
functions and concluded that they could be explained by a relatively narrow watery haze size
distribution in the diameter range 0.3 to 0.8 m, supplemented by a few opaque coarse dust
particles.
With the advent of computers, numerous inversion (automatic fitting) procedures were
developed to extract aerosol size distributions from spectral or angular scattering data. Although
the authors accept the utility of such automatic fitting procedures, they prefer inactive, iterative
fitting controlled by the investigator. Such an approach gives the user a much better feel for the
nonuniqueness and uncertainty range of the fitting process.
Humidity Effects
Well before the turn of the century, it was known that hygroscopic light scattering
particles will grow in size and scatter more light with increasing humidity. By the early part of
this century, Hagman recognized that the equilibrium size of hygroscopic particles is determined
by the balancing of two forces: surface tension that increases pressure within the particle, and
pressure depression due to dissolved salts. The theory of particle growth was first formulated by
Gudris and Kulikowa (1924). Kohler (1936) developed and applied salt solution theory for the
growth of sea salt particles with relative humidity. He also calculated the critical supersaturation
necessary for the nucleation to occur as a function of particle size. Since Kohler, the aerosol
growth with humidity is probably one of the most belabored subjects in the atmospheric sciences
literature. Major contributions on the subject have been contributed by G. Hanel and R.
Charlson, both presenting summary papers at this workshop.
Coagulation
Based on direct microscopic observations of the morphology and composition of urban
particles, Whytlaw-Gray and Patterson (1932) proposed that coagulation should be an important
mechanism shaping the size distribution of atmospheric aerosols. Later, Junge (1952) also
concluded that the lower end of the aerosol size distribution, below 1.0 m, is determined by the
balance between the sources and coagulation decay of the particles. Numerous studies since then
confirm that coagulation among the haze particles, particularly below 0.5 m, is an important
process shaping the size distribution.
Sedimentation
The removal of large particles by sedimentation is a plausible mechanism that determines
the shape of the size spectra for the range above a few microns. The atmospheric lifetime of
coarse particles is also determined by sedimentation. The first physical explanation of Junge’s
power law size distribution laws was provided by Friedlander (1960). He proposed that the
atmospheric aerosols exhibit self-similar and power law shapes because the input rate of fine
particles from various sources is balanced by the course particle removal rate by sedimentation.
He also proposed that coagulation provides the mechanism for the transfer of particles from fine
to coarse sizes. Taking the analogy from the cascading transfer of turbulent energy from large to
small atmospheric eddies, Friedlander termed the equilibrium size distribution, balanced by
coagulation and sedimentation, as the “self preserving distribution.”
Gas-Particle Conversion
Observing the rays of sun soon after rain, Rafinesque (1819) was forced to the
conclusion, as many “philosophers” before him, that in the “chemical laboratory of the
atmosphere” there must be “in situ” processes responsible for the haze visualizing the rays of
sunlight. Tyndall (1869), Aitken (1888) and many others confirmed that chemical aerosol
formation occurs in the atmosphere at significant rates. Haagen-Smit (1952) in his key
contribution to the chemistry and physiology of the Los Angeles smog documented that man’s
activities contribute significantly to this chemically formed haze. The roles and interaction of
nucleation, coagulation, and condensation in the dynamics of the smog aerosol were established
and numerically formed by Husar et al. (1972).
Cloud Scavenging and Removal
Cloud processes influence the aerosol dynamics in two ways. In a volume of
atmospheric air, once cloud or fog condensation occurs, the much finer haze particles are
scavenged efficiently by the less populous cloud droplets. This condensation results in a
collection and mixing of several haze particles into one cloud droplet. After the evaporation of
the cloud droplets, the new, internally mixed, fine particles will be much larger in size than
before the scavenging. This physical collection and growth is frequently augmented by chemical
conversion of gases within the cloud droplets. Secondly, cloud scavenging and subsequent
rainout constitutes the most important removal mechanism for atmospheric haze particles.
Although both of the processes are of vital importance for the dynamics of the fine particle
spectrum, they have eluded quantification in the past.
It is apparent that the current understanding of the processes that determine the fine
particle size distribution at any given time and location is inadequate. The fundamental physical
laws that dominate the size spectrum such as source strength, atmospheric mixing, nucleation,
condensation, coagulation, and cloud processes are, in principle, understood. The problem lies in
knowing the proper sequence and rate at which these processes act under ambient conditions.
Furthermore, the long atmospheric lifetime of fine particles ranging from a day to weeks requires
the knowledge of the previous history of the aerosol population over days or weeks. Although
this task may appear to be formidable, it is our judgement that the quest for development of
atmospheric aerosol models based on physical rather than empirical laws is a meaningful and
rewarding undertaking.
INVERSION OF OPTICAL DATA USING INTERACTIVE GRAPHICS
Measurements of spectral extinction and aerosol phase function are inverted to aerosol
size distributions using CAPITA’s interactive graphics capabilities. The approach is illustrated
in Figure 7 and uses spectral extinction data. Conceptually, the scheme is identical to that
described by Volz (1954). The aerosol size distribution, displayed as a volume distribution
function (dv/d log Dp), is expressed as a sum of a set (11) of narrow distributions, covering the
size range 0.1 to 10 m. The contribution of each size range to the light extinction (per unit
aerosol volume) is precalculated for several values of imaginary and real refractive index. At
fitting time, the investigator uses the refractive index of his choice. The thus calculated
contributions to light extinction by each size class are shown as thin lines in Figure 7. The fine
tuning of the fitting is facilitated by a cross hair on the terminal that allows raising or lowering
the concentration in each of the 11 size ranges until a satisfactory match of data points and the
calculation is obtained.
Figure 7. Aerosol size distributions from spectral extinction data.
For illustration purposes, the inversion of spectral extinction data reported by Curcio et
al. (1961) is used herein. The heavy solid lines indicate the best fit extinction coefficients
obtained for Curcio’s data sets numbered 1, 2, and 3 (Fig. 8). Fit of spectrum 1, for instance,
shows that in order to fit the data points at 0.4 and 0.5 m wavelength, it was necessary to add a
rather large amount of aerosol volume at around 0.2 to 0.3 m. Also, the change in the slope of
the Angstrom exponent for 2 m wavelength necessitated the addition of a large aerosol volume
at about 5 m.
Figure 8. Spectral extinction and corresponding size distributions; data from Curcio et al.
(1961).
Another interesting data set was reported by Badayev et al. (1975) from the Institute of
Atmospheric Optics, Moscow. As pointed out by the authors themselves, the spectral extinction
data for type A haze can only be fitted by a multimodal volume distribution with modes at 0.15,
0.7, and 4 m (Fig. 9). Badayev et al. Noted that such spectral extinction data which do not
satisfy Angstrom’s power law occur frequently.
Figure 9. Spectral extinction and corresponding size distribution; data from Badayev et al.
(1975).
The interactive graphics fitting approach also gives an immediate illustration for the
uncertainties of the inversion. An alternative set of volume distributions can provide an identical
quality of fit. It is apparent that the extreme volume modes, at 0.15 and 4 m (Fig. 9), constitute
a “minimum” volume necessary for the fit. The actual volume distribution below 0.3 and above
2 m is probably more, but cannot be determined because of the nonuniqueness of the inversion.
This feature also demonstrates that spectral extinction data can only yield information about the
size distribution range that corresponds roughly to the wavelength range of the spectral
measurements. If, for instance, the spectral extinction data are obtained for the 0.4 to 1.0 m
wavelength range, it is hopeless to seek inverted size distribution values below 0.4 and above 1.0
m ( a fact frequently overlooked by investigators). It is, for this reason, that Badayev et al.
Strongly recommended measurement of spectral extinction from about 0.25 to 0.3 m upward.
The spectral extinction data for haze types B and C can be fitted with much broader and less
bumpy aerosol volume distribution functions.
Aerosol size distributions can similarly be fitted iteratively to measured phase function
data. An attractive data set for illustration and other purposes is the set measured and tabulated
by Barteneva (1960). Figure 10 shows the measured phase functions (crosses), the calculated
phase functions for three values of the real part of the refractive index (solid line), the best-fit
volume distribution function, and finally the spectral extinction coefficient calculated from the
volume distribution function. This numerical fitting process illustrates that for light hazes the
choice of the real refractive index is not crucial. For heavy haze (type 8), on the other hand, a fit
can only be obtained with n x 1.4 or 1.33 but not with 1.5 or 1.6. The best size fit distributions
also show that for light haze (types 4 and 5), an appreciable quantity of 0.15-m size particles is
required in addition to Rayleigh scattering in order to obtain the measured amount of
backscattering. Heavy hazes (types 7 and 8) can be fitted with a narrower size distribution with
volume mean diameter centered at the wavelength of light. It is also instructive to note that the
calculated spectral extinction data corresponding to Barteneva’s light hazes (types 4 and 5)
roughly satisfy Angstrom’s law with the exponent declining from 1.5, for heavy hazes, to 0 for
haze. The haze (type 8) actually exhibits “anomalous extinction” between 0.3 and 0.6 m
wavelength.
This described interactive fitting procedure can be used as a vehicle to unify the extensive
data bases available in the literature on various measures of spectral and directional scattering
properties of atmospheric haze.
COMPARISON OF MEASURED AND INVERTED SIZE DISTRIBUTIONS
There are two “regularities” emerging from the extensive aerosol-optical data sets on fine
particles: the systematic forward elongation of the aerosol phase function with increasing
extinction coefficient (Foitzik, 1965; Barteneva, 1960; Johnson, 1981) and the existence of the
“accumulation” mode, that is, condensational-coagulation aerosol growth into a relatively narrow
size range (Whitby et al, 1972; Husar et al., 1972). For some time it was felt that the fine particle
mass mode is rather invariant in volume mean diameter. This belief, however, is inconsistent
with the inverted size distributions from Barteneva’s phase function data (Fig. 10). There, it was
necessary to increase the volume mean diameter from 0.3 to 0.6 m corresponding to light and
heavy haze, respectively.
More aerosol-optical data bases should be compared in this manner and explanations sought
for the remaining discrepancies. One possible explanation of the discrepancy may be due to
experimental measurements in either set. It is known, for instance, that size distributions
measured by the electrical mobility analyzer tend to be broadened by cross-channel interference.
Interesting new data sets on the mass-size distribution on particulate sulfur (Reible et al., 1982)
to another (Fig. 11). It is therefore conceivable that the broad “grand average” fine particle
distribution (the “Whitby distribution”) with Dp = 0.3 m and g = 2 may be better represented
by a combination of several distributions (say a sum of 2 to 3 log-normal distributions) within
the fine particle mode (Fig.12). It is hoped that the relative magnitudes of these fine particles
modes will be relatable to easily measurable atmospheric parameters such as visual range.
Figure 10. Phase function data with corresponding size distributions and resulting spectral
extinction; data from Barteneva (1960).
Figure 11. Normalized Ambient Aerosol sulfur mass distribution (Reible et al., 1982)
Figure 12. "Grand Average" fine particle distribution ("Whitby distribution") (Whitby et al.,
1972).
PLAUSIBLE EXPLANATIONS FOR THE OBSERVED SIZE
DISTRIBUTION DYNAMICS
Field measurements and the empirical laws that summarize them provide us with the facts,
that is, the pattern of aerosol behavior, but they do not explain their cause. Diagnostic models of
the aerosol size spectrum dynamics can provide plausible explanations (but generally no proof)
for the observed phenomena. The diagnostic modeling approach is illustrated in Figure 13 and
shows simulations of atmospheric aerosol coagulation and condensation. During the 1969
Pasadena smog study, it was observed that the aerosol size distribution below 0.2 m has
decayed systematically every night. In order to test whether coagulation could explain the
observed decay, a laboratory and a numerical Monte Carlo simulation of the observed process
was conducted. It was concluded that coagulation could indeed decay the lower part of the size
distribution spectrum night after night. Similarly, the daytime aerosol growth of the size
distribution was simulated by diffusional deposition of condensable gaseous products (Fig. 13).
Figure 13. Volume and number distribution computed by the Monte Carlo size distribution
simulation code (Husar et al., 1972).
The diagnostic growth simulation revealed that diffusion controlled deposition is indeed a
likely and plausible explanation for the growth of 0.1 to 1.0 m aerosol. It also showed,
however, that it was necessary to impose a cutoff at 0.09-m particle size, below which growth
did not occur. Tentatively, the physical explanation given was the "Kelvin" cutoff; that is, the
surface tension does not allow growth of particles below a given size.
Recently, additional numerical experiments were conducted with Monte Carlo size
distribution simulation code, with objectives similar to the ones set out by Junge and Abel
(1965): given an initial volume distribution function, what are the aerosol growth kinetics due to
coagulation for 3 to 5 days? This simulation suggests that given an aging time of 5 days at 10
g/m3 (or 0.5 day at 100 g/m3), the resulting volume distribution function will be similar
regardless of the initial volume distribution function. Thus, the aerosol will approach a selfsimilar shape (Fig. 14). It is important to recognize, however, that wet and dry removal and
cloud scavenging-evaporation processes also play a very significant role in the aerosol size
spectrum dynamics.
Figured 14. Simulations of atmospheric coagulation and condensation.
For future work, the most challenging and most rewarding task will be to give physical
explanations for the regularities documented by Barteneva (1960) and Johnson (1981). The
puzzling feature of their observations is the lack of the explicit dependence of the phase function
on relative humidity. Evidently, it is not important whether the increase of extinction coefficient
is due to water uptake by hygroscopic salts, coagulation, gas-particle conversion of condensable
species, or aggregation by cloud droplets. The resulting growth in aerosol (and forward shift in
light scattering) is roughly the same regardless of the mechanism as pointed out by Johnson
(1981). This result has exciting implications to the modeling of fine-particle optics.
The ultimate objective of this type of research is to develop a well tested aerosol-optical
model that could serve as a submodel for larger radiative models. The aerosol model has to be
sufficiently compact to be suitable for inclusion into larger atmospheric transmission models
such as LOWTRAN, developed at Air Force Geophysics Laboratory (AFGL). A well-calibrated
aerosol-optical model could also be utilized as a module in a dynamic continental or global scale
model of atmospheric haze. The input parameters required by this type of model have to be
readily available from routine observation networks or possibly from remote sensors. Aerosol
source type, visual range, relative (or absolute) humidity, percent cloud cover, and mixed layer
height and candidate input parameters.
HAZE CLIMATE OF NORTH AMERICA
Historical Notes
The optical properties of the atmosphere have interested observers of nature since time
immemorial. By the mid-nineteenth century, there were numerous qualitative observations,
particularly in Europe, of atmospheric transparency, either by measuring the depletion of solar
radiation or visual observations of distant mountains and islands. In that time period, most of
these records were kept for their own sake. By the 1880s, however, much attention was focused
on the sun as a climatic factor. The immense global haziness caused by the spreading of
Krakatoa volcanic dust generated much research attention. The dust cloud circling the earth, and
observed quantitatively at many locations, provided contemporary researchers with a first
glimpse of the global circulation patterns of the upper atmosphere.
The turn of the century marked the beginning of a long term investigation of the spectral
transparency of the atmosphere. Following the pioneering work of Langley (1881, 1900), the
Astrophysical Observatory of the Smithsonian Institute began systematic monitoring of the
“solar constant.” The hope was that the well-established climatic cycles could be related (or
attributed) to the cyclic variations of the “solar constant.” Abbot (1900, 1940) dedicated
essentially all of his 50-year scientific career to this quest. The solar link to the terrestrial
climate is still debated extensively. However, as a byproduct of the careful long-term
Smithsonian observations, there is a historical record of atmospheric spectral transparency for
several remote monitoring sites over the world. Another product of a lifetime effort is the data
base of total (not spectral), direct (not diffuse) daily solar radiation over several sites in the
United States since about 1910. H. H. Kimball (1908) initiated the pyranometer network at Mt.
Weather near Washington, DC, in 1906 and expanded it to Madison, WI, Lincoln, NE, Table
Mountain, CA, Blue Hill, MA, etc. These measurements were conducted with extreme care
compared with the performance of most current “automated” networks. The daily data were
routinely reported in the Monthly Weather Review from 1910 to 1950. It is most unfortunate that
such a valuable data base has faded into obscurity in the postwar years.
Lohle (1941) provided a valuable review of historic visibility observations for Europe. To the
authors’ knowledge, the first systematic observations of visual range in North America were
initiated at Blue Hill Observatory, Milton, MA, in 1884, by recording to what distance hills or
mountain peaks were discernable (Husar and Holloway, 1982).
Since time immemorial, the transparency of the atmosphere and the color of the sky have
been guides in the folklore about the weather to come To many amateur weather watchers, a red
sunset is still a meaningful indicator of tomorrow’s weather. In the 1920s, the Bergeron school
of meteorologists initiated a systematic classification of air mass types, and the horizontal and
vertical transparency of the atmosphere were used extensively as one of the indicators of air
mass type. Wexler (1934) used vertical atmospheric transparency (measured by depletion of
direct solar radiation) for the classification of turbidity values in accordance with the North
American air mass type. The monographs by Lohle in Germany (1941) and Middleton in
Canada (1935) summarize the understanding of the atmospheric transparency - air mass
relationship in the late 1930s. However, by this time, the visibility and turbidity as weather
forecasting tools had faded. In fact, Middleton (1941), in preface of the second addition to his
admirable book on visibility, states that his “enthusiasm for the use of visual range as a synoptic
element has, frankly, been shared by no one else.”
In the 1930s, there was a new emphasis on obstruction to vision through the atmosphere,
particularly as it impaired traffic safety in air, sea, and on land. This time period was also
marked by the appearance of the first regional climatologies on visibility: one assembled by
Wright (1939) for the British Isles and another for pre-War Germany by the German weather
service (Reichsamt für Wetterdienst, 1938). In a most ambitious and tedious undertaking, the
United States Navy compiled a seasonal atlas of haze frequency over the seas of the world (Fig.
15). Even today these maps constitute a fascinating qualitative picture of global haziness.
During World War II much of the visibility research in the United States and Great Britain was
focused on the detector: establishing the limiting performance of the human eye-brain system.
The results of the physiological studies are given by Blackwell (1946) and incorporated in the
theory of atmospheric vision by Duntley (1948). The best summary of these wartime research
efforts is given by Middleton (1952).
Figure 15. Frequency of haze over the ocean produced by windborne dust from the continents.
There is evidence that some dust from Africa travels as far west as Barbados (Turekian,
1968).
In the two decades after World War II, the visibility and atmospheric transparency was
viewed almost exclusively from the point of view of aircraft safety. Although in the 1960s there
were many informal reports from airline pilots on the deteriorating visual environment in eastern
North America, there was little attention given to the subject. Eldridge (1966) produced the first
climatological maps of visibility for the United States (Fig. 16) (frequency of days with less than
5-, 10-, 20-, and 40-km visibility), followed by a Canadian haze climatology by Munn (1973),
and a Unites States haze (bext) corrected for precipitation and relative humidity effects by Husar
et al. (1976). A summary of more recent investigations is given by Husar et al. (1981).
Figure 16. Average percent of hours with visibility less than 5 km, 1948 to 1958, by seasons
(Eldridge, 1966).
In the 1960s, there was a revived interest in aerosol effects on atmospheric transparency. The
development of new devices such as the narrow wavelength photometer (Volz, 1960) allowed
the specific detection of aerosol transmission without the interference of water vapor or ozone
absorption. McCormick and Baulch (1962) initiated a network in the United States, the results
of which gave the first continental-scale view of vertical atmospheric transparency (Flowers et
al., 1969). In the 1970s, the United States turbidity network was expanded under the auspices of
the World Meteorological Organization (WMO). In the northern hemisphere there were about
100 stations operational. As with every network, the data quality and quantity from individual
stations is highly variable. It is distressing to note, however, that the Unites States turbidity
network has been deteriorating since 1976 and it is not certain whether it will survive the decade
of the 1980s.
The 1970s marked a beginning of another new line of interest in visibility caused by the
"environmental movement." Deterioration of visual air quality due to man-made aerosols was
studied extensively, since the passage of the Clean Air amendments of 1977 (section 169A)
stating that the scenic beauty of national parks and wilderness areas should be protected. A
status review on the subject was given at the Grand Canyon Conference on Plumes and Visibility
(White, 1981).
In the 1970s, it was also recognized that the synoptic network of visual range observations
provides an attractive data base for study of the evolution, transport, and dissipation of synopticscale hazy air masses (Hall et al., 1973). This approach is being used extensively at CAPITA
(Husar et al., 1976; Patterson et al., 1981) for many purposes, such as illustration of long-range
aerosol transport, continental-scale dynamic model calibration, a surrogate data set for regional
sulfate, etc.
Predicting the developments in the 1980s and 1990s is a risky undertaking. It is safe to
assume, however, that beyond the lines of interest listed, there will be at least one new concern-interference of haze with remote sensing or "electronic vision." In both the civilian and military
sectors remote sensing through the atmosphere from surface, air, or space platforms is becoming
increasingly important. Furthermore, the performance of these high technology devices is
hampered by the unpredictable atmospheric effects much more than by the sensitivity of the
detector systems. The implications are that the spectrum range of interest will be much
expanded beyond the visible range, particularly into the infrared.
Optical Data Bases
There are currently two data bases of global dimension that can be utilized to assemble an
optical climate of the world. The most extensive and probably most valuable data base arises
from the surface synoptic observations at about 6000 locations, reporting to the World
Meteorological Organization (WMO) network. A global network of about 100 stations also
reports the daily aerosol vertical optical depth, as turbidity, when the sun is unobscured by
clouds.
Surface observations by human observers are reported to and disseminated by the World
Meteorological Organization. The data are available from the United States National Climatic
Center, Asheville, NC, on magnetic tapes in easily accessible and processable format. An
illustration of CAPITA's work on haze climate is given by using North American surface
observations. A map of analyzed station locations form the Unites States and Canada is given in
Figure 17.
Figure 17. Location of U.S. and Canadian surface meteorological stations included in the
CAPITA data base.
Limitations of Visibility and Turbidity Data
On a global scale the visibility and turbidity data have severe limitations. Prior to any
statistical or other analysis, it is essential to scrutinize the data.
The extinction coefficients for the surface layer of the atmosphere are calculated from quantized
values of visual range recorded routinely by human observers as part of the global network. The
list of visual range numbers recorded at the United States, Canadian, and WMO stations are listed
in Table 2.
Table 2. Visual Range Numbers
United States stations
Canadian stations
WMO stations
Miles
Increment
km
Increment
Km
Increment
0-3/8
1/16
0-1.2
0.2
0-5
0.1
3/8-2
1/8
1.2-4.8
0.4
5-30
1.0
2-3
1/4
4.8-24.0
1.6
30-70
5.0
3-5
1
25.0-160
8.0
15-95
5
The conversion from visual range V to extinction coefficient bext is by the Koschmieder
relationship, 3.9/V (km) or 2.4/V (miles). Since the extinction coefficient, in units of km -1, is
proportional to the concentration of light scattering and absorbing aerosols, it is more appropriate
for trend and spatial analysis than visual range. In what follows, haziness is expressed in units of
the extinction coefficient (km-1) calculated directly from the visual range data.
The synoptic records of surface visibility contain the value of minimum visual range as well
as the cause of visibility obstruction: rain, snow, fog, dust, sand, smoke, and haze. Since
precipitation, fog, and blowing dust can be reasonably assumed to be caused by nature, it is
useful to eliminate those data from further analysis.
The first tentative criterion applied to the station selection is that the visual range record is
available at least two-thirds of the time.
A systematic bias of visual range data at a given stations arises from the fact that the targets
are in uneven intervals and there is an upper limit of visual range beyond which the data are not
resolved. As pointed out by Middleton (1941), "Anyone who compiled frequency tables of
visual range will be aware of the shortcomings…some values may show a minimum
accompanied by an excess of observations of neighboring values; the effect may probably be
traced tot the absence of good markers in the series." In a remote flat terrain the maximum
visual range reported may be as low as 10 km, in which case most data are below this threshold.
On strip charts of extinction coefficient, this value corresponds to a flat lower threshold. The
distribution function of extinction coefficient is then obviously incomplete at low values of bext.
In the Koschmieder relationship, the minimum detectable contrast is 2% and the visual targets
are assumed to be black and occupy at least an angle of 30 minutes of arc against horizon
background. There is considerable evidence (Johnson, 1981) that during routine visual range
observations, the contrast thresholds are about 5% and change the Koschmieder relationship to
bext=3.0/V (km). It is likely, however, that the equivalent contrast threshold will vary with
viewing, target, and illumination conditions. For the sake of consistency, therefore, the original
value of the Koschmieder constant 3.9 is used throughout this work. It has to be kept in mind,
however, that a correction factor is necessary if the absolute value of bext from visual-range data
are to be compared with extinction coefficient measurements by other methods.
In most cases the extinction coefficient distribution function for a given site is truncated
because of the maximum reported visual range. It is, therefore, improper to calculate averages,
standard deviations, etc., on such data because all the subthreshold values of bext would be
grossly overestimated. It is, therefore, desirable to estimate the distribution function below the
threshold by extrapolating the known portion of the distribution function by assuming a certain
functional form. In our experience a log-normal distribution constitutes a reasonable functional
form for bext as well as for most pollutant concentrations. The cumulative bext probability
distribution functions for Charlotte, NC (1952, 1972), and for Albany, NY (1974) are shown in
Figure 18. It is evident that for these sites the log-normal distribution adequately represents the
data between the 10th and 90th percentiles. However, both the median values and the logarithmic
standard deviations vary substantially with location and season, and probably in time.
Figure 18. Cumulative probability distribution functions for Charlotte, NC, and Albany, NY
(Husar et al., 1979).
As part of an automated data correction scheme, the minimum value of b ext can be determined
and the least-square fit to the cumulative distribution obtained for each station and month. Once
the monthly log-normal parameters are available, higher moments of the distribution function,
for example, the mean, can be calculated. Special attention is directed to those stations that have
less than 50% of the readings above the threshold. In those cases, the entire station record is
displayed as shown in Figure 19 for a United States and a Canadian station. It is apparent that
for some stations, such as Kindersley, Sask., there is a need to assume a standard deviation since
only the worst 5% or 10% of the bext values are above the threshold.
Figure 19. Strip charts of monthly 25th and 90th percentile extinction coefficient Ely, NV, and
Kindersley, SASK.
The daytime visual range reduction is due to the solar radiation scattered into the visual path
by the aerosol as well as by light extinction. At night the visual deterioration is due to the light
extinction only. Thus, daytime visual range depends primarily on aerosol extinction coefficient
whereas at night the visual range depends on the source intensity as well. The diurnal pattern of
light extinction coefficient calculated for fixed relative humidity increments is shown in Figure
20 for Dayton, OH. It is apparent that there is about a factor of two increase of daytime
extinction coefficient compared with the night values. This increase is attributed primarily to the
incompatibility of visual range determinations during day and night. When visual range is used
for the estimation of the aerosol extinction coefficient, only the daytime values should be
analyzed.
Figure 20. Visibility data, classified according to time of day and relative humidity ranges,
Dayton, OH, 1970 to 1974 (Husar et al., 1979).
The turbidity data obtained by the sun photometer network is also subjected to systematic
errors. The characteristics, calibration, and use of the photometers has been discussed by Volz
(1960), McCormick and Baulch (1962), and Flowers et al. (1969), among others. Beyond the
problems of maintenance (for example, drift and calibration), the question of temporal and
spatial representation must be examined.
Spatial Pattern of Extinction Coefficient and Optical Depth
The goal of this analysis of extensive visibility data is to establish the climatic pattern of
visual range over a continent and subsequently over the world. This requires proper averaging
over 5 to 10 years of data for each station. However, since the bext distribution function is
extremely broad and variable, a single arithmetic mean is inadequate for its characterization.
Therefore, the data are first examined in terms of percentiles before arithmetic or logarithmic
averaging is performed. The maps of bext percentiles 10, 25, 50, 75, 90, and 95 are obtained for
each year and season as well as for three different values of extinction coefficient: the unedited
bext values, bext values ignoring the data when precipitation events occurred, and bext values
further corrected for relative humidity effects by normalizing to 60% RH. An illustration of
quarterly local noon haze maps for North America is shown in Figure 21. The first column
shows the 50th percentile bext contours taking all observations into account. The visual range
due to all causes is worst around the Great Lakes in the first quarter. The second column shows
the noon 50th percentile contours, taking only visual range data that were in absence of fog and
precipitation (bext-FP). The third quarter shows the highest extinction for the precipitation-free
observations. The last column, RH-bext, was obtained by taking the individual precipitation-free
observations and correcting (or normalizing) the bext to 60% relative humidity. It is remarkable
that the RH correction for the noon observations has an insignificant effect on the 50 th percentile.
The RH correction factor applied is shown in Figure 22.
Figure 21. Quarterly average North American extinction coefficient (BEXT), excluding fog and
precipitation events (BEXT-FP) and excluding fog and precipitation events, corrected for
relative humidity.
Figure 22. Functional form used to normalize the extinction coefficient to 60% relative
humidity.
The 30-year trend of quarterly eastern United States extinction coefficient is shown in Figure
23. During the first quarter, the noon mean extinction coefficient, taking all observations, was
about 0.55 km-1 and it has been roughly constant since 1948. Once the observations coinciding
with fog or precipitation were ignored, the mean bext dropped to about 0.3 km-1, constant over
the years. The RH correction yielded a further drop to about 0.25 km-1. The trends in the fourth
quarter were similar to those in the first quarter. The most interesting trends are shown in the
third quarter. The exclusion of precipitation events decreased bext only about 10% to 20%. The
RH correction to the mean bext was insignificant. The trend of the summer haziness shows that
in the early 1950s, the RH corrected bext over the eastern United States was about 0.2 km-1, rose
rapidly to 0.3 in the 1960s, and dropped somewhat below 0.3 in the 1980s.
Figure 23. Quarterly trends of mean extinction coefficient over the eastern United States. The
top dashed line reflects all values, the middle dotted line only those values exclusive of fog or
precipitation events, and the bottom solid line those values exclusive of fog or precipitation
events corrected for relative humidity.
Once the percentiles for each station are established, other properties of the distribution
function can be calculated, for example, the total dosage, the episodicity (what fraction of the
dosage is contributed by the worst10% or 20% of the values), etc. For this purpose, the
interactive data processing capabilities of CAPITA were utilized extensively. For example, one
of our interactive routines allows calculating the difference of two maps, bext for 1976 to 1980,
and bext for 1950 to 1954 (Figure 24). The maps of the bext increase for quarters 1 and 3 are
shown in Figure s25. Evidently in the winter quarter, the haziness has increased in the
southeastern and northwestern states. The increase of summer haziness occurred mostly in the
southeast, east of the Mississippi, and south of the Ohio rivers. (Remote use of the CAPITA
database and processing software by other scientists is available.)
Figure 24. Spatial distribution of 90th percentile extinction coefficient for July, August, and
September 1976 to 1980, all values excluding fog or precipitation events.
Figure 25. Increase of extinction coefficient from 1950/1954 to 1976/1980; (a) January,
February, and March; (b) July, August, and September.
The WMO Turbidity Network reports values of turbidity, a measure of vertical atmospheric
transparency to short-wave radiation, at wavelength of 0.5 m to 0.38 m. The spatial pattern of
yearly average (1972 to 1975) haze optical depth at 0.5 m (2.3 x Turbidity) is depicted in
Figure 26. The highest optical depth occurred in the east-central section of the United States.
Figure 26. Contour map of 4-year average (1972 to 1975) have optical depth, , (turbidity
coefficient  2.3) at  = 0.5 m. Data are from the WMO Turbidity Network.
Relationship of Haze to Meteorological Parameters
Meteorological parameters, e.g. temperature, humidity, wind direction, wind speed, solar
radiation, etc., influence visibility through dispersion of aerosols, or by changing their properties
or formation and removal rate. Temperature and relative humidity may also influence the
emission rate of aerosols or the precursor gases, for example, for electric utility plants a thigh
summer temperature and relative humidity. The humidity classified extinction coefficients (Fig.
27) show that the average haziness increases with humidity. This result, however, does not
necessarily imply that relative humidity is the cause of high extinction coefficient. In order to
eliminate relative humidity as a variable, it is advantageous to perform the trend analysis by
using only data for fixed relative humidity windows.
Figure 27. Humidity classified extinction coefficients for Huntington, WV, Lexington, KY,
Chicago, IL, and San Antonio, TX (Husar et al., 1979).
The role of temperature as a factor influencing the haziness is difficult to perceive because of
the many routes by which temperature dependence may be exerted. Even coarse examination of
the existing historical database reveals that major qualitative changes have occurred in the
temperature dependence of haziness (Fig. 28).
Figure 28. In Dayton, OH, 1948 to 1952, the extinction coefficient was essentially temperature
independent whereas higher extinction coefficients were associated with higher RF. By 1970
to 1975, the temperature-RH dependence of haziness has unfolded dramatically: high
extinction coefficients are associated with high temperature and high relative humidity (Husar
et al., 1979).
Wind speed influences the dispersion of particles, and if haziness is due to local sources (Fig.
29, Washington, DC, winter 1945 to 1950) low wind speeds cause higher haze levels. For
regional haziness (Washington, DC, winter 1970 to 1975) the extinction coefficient is only
weakly dependent on wind speed. In arid areas, for example, Albuquerque, NM, the extinction
coefficient is the highest at high wind speeds because of the resuspension of dust (Fig. 30).
Figure 29. Wind speed dependence of extinction coefficient at Washington, DC, winter 1945 to
1950, 1951 to 1956, 1955 to 1960, and 1970 to 1975 (Husar et al., 1979).
Figure 30. Wind speed dependence of extinction coefficient at Albuquerque, NM.
“Wind direction of air pollutants has been used extensively in the past to construct “pollution
roses” as directional pointers to sources. A haziness rose for Lambert airport in St. Louis (Fig.
31) shows that in 1945 to 1950 the highest extinction levels were observed when the wind was
from the city direction; by 1970 to 1972 the haziness became essentially independent of the wind
direction. Here again the conclusion may be drawn that the haziness is currently from distant
rather than from local sources.
Figure 31. Haziness rose for St. Louis, MO, for 1945 to 1950 and 1970 to 1972 (Husar et al.,
1979).
These examples were intended to illustrate the vast reservoir of underutilized haze-related
information contained n the existing meteorological data base.
Visual range deterioration over the sea is also influenced by sea salt particles produced by
bubble bursting in white waves. In the literature (for example, Woodcock and Gifford, 1949),
relationships are given between the surface wind speed, sea salt concentration, extinction
coefficient, and relative humidity. These relationships were verified by using visual range
observations form ships at sea.
Over the industrialized continental regions of the world, eastern North America, Europe,
China-Japan and the adjacent marine areas, man-made aerosols are likely to dominate the haze
optics. Other regions such as east Africa, Saudi Arabia, and the Takla Makan desert (north of
Tibet) are most influenced by wind blown dust. West central Africa (Sudan), Central America,
and probably large portions of South America are exposed to smoke form vegetation burning.
There is also a contribution from the much referenced but chemically unidentified "natural" haze
from organics emitted by vegetation (Went, 1960).
The identification of specific aerosol sources of haze over the different parts of the world is
inherently a difficult task. It seems meaningful, however, to start a global source inventory for
aerosols influencing the optical climate of the world. Establishment of a continental and global
haze climatology seems to be the most reasonable beginning since optical data are much more
abundant than chemical composition, mass concentration, size distribution or aerosol deposition
data.
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