Paper prepared for the Second Symposium on the History of Aerosol Science, Portland, OR, October, 2001
The Emergence of the Bimodal Distribution Concept
Rudolf B. Husar
CAPITA, Box 1124,
Washington University, St. Louis, MO, 63130. rhusar@me.wustl.edu
Introduction
In a nutshell, the bimodal distribution concept states that the atmospheric aerosol mass is
distributed in two distinct size ranges, fine and coarse; each aerosol mode having characteristic
size distribution, chemical composition and optical properties. Furthermore, implicit in the
bimodal model is that the scientific and regulatory treatment of these modes can be dealt with
separately since each mode is governed by its own sources, transformation, transport, and
removal processes and is associated with different effects. With the introduction in 1997 of the
PM2.5 national air quality standard, the bimodal distribution concept has gained full acceptance
in air quality management as well.
According to (Friedlander, 2000), the concept of multimodal mass (or volume) distributions has
dominated our understanding of atmospheric aerosol dynamics for the last thirty years. Being a
story of successful science-policy interaction, the history of the bimodal distribution seems
suitable for chronicling, particularly since indeed it has a long history: It took about thirty years
to move from the scientific conception to the air quality management implementation!
The emergence and resilience of the bimodal distribution concept was evidently facilitated by the
confluence of three parallel developments: 1) development of complete and continuous size
spectrum measurement technologies; 2) the introduction of aerosol dynamics as the scientific
principle for the explanation of atmospheric aerosol pattern; 3) unprecedented synergistic
collaboration by key researchers in the field.
This summary is structured to describe the above three developments. Following a brief review
of the ‘Junge’ distribution, the paper roughly mirrors the content of the three papers by the
University of Minnesota group on the ‘1969 Pasadena Smog Caper’ describing the Minnesota
Aerosol Analyzing System (MAAS), (Whitby et al. 1972a); the size distribution of the Los
Angeles smog (Whitby et al., 1972b) and the physical mechanisms governing the dynamics of
the Los Angeles aerosol (Husar et al., 1972, Husar and Whitby, 1973) and related papers 1. Some
aspects of the bimodal distribution history were previously reported during in the first History of
Aerosol Science volume (Preining and Davis, 2000) by several authors (Friedlander, 2000; Hidy,
2000; Sem and Whitby, 2000).
1
Several of the figures are reproduced here in original form, as I have plotted during the 1969 Pasadena
experiments. In fact, I have painted myself into this chronicle with shameless regularity. In order to reduce the
damage, from this pretentious, self-centered reporting, I have placed most of the self-referencing into the footnotes.
1
Scientific Background: The Junge Distribution
The modern science of atmospheric aerosols began with the pioneering work of Christian Junge
who performed the first comprehensive measurements of the size distribution and chemical
composition of atmospheric aerosols. The nature of the particulate matter arising from these
measurements is reviewed and summarized in a series of papers, Junge (1953; 1955; 1957; 1958;
1961) as well as in his book on “Air Chemistry and Radioactivity” (Junge, 1963). Based on
tedious and careful size distribution measurements performed over many different parts of the
world, Junge and co-workers have observed that there is a remarkable similarity in the gathered
size distributions: they follow a power law function over a wide range of particle size from 0.1 to
over 20 m in particle radius. The inverse power law exponent of the number distribution
function ranged between 3 and 5 with a typical value of 4. This power-law form of the size
distribution became known as the Junge distribution atmospheric aerosols. When plotted as a
volume distribution the Junge distribution is a horizontal line between about 0.1 and 20 m
(Figure 1). The data indicated that below and above this size range the volume distribution
function dropped off.
In the 1960s the physical mechanisms that were responsible for producing these similarities in
the atmospheric aerosol size spectra were not known, although it was clear that homogeneous
and heterogeneous nucleation, coagulation, sedimentation and other removal processes were all
influential mechanisms. In particular, it was unclear which combination of these mechanisms is
responsible for maintaining the observed similarity of the size spectra.
Sheldon Friedlander tackled the science i.e. the explanation of atmospheric aerosols along two
fronts. In a series of articles based on dimensional analysis and similarity theory, Friedlander
(1960a, 1960b, 1961) has shown that IF coagulation and sedimentation were the dominant
physical mechanisms, then a significant part the aerosol spectrum could follow the power law
shape. In essence, the implication of this theory was that the observed quasi-stationary size
distribution could be the result of balancing aerosol production (nucleation) and removal
(sedimentation) with coagulation transferring particles from the nuclei to the sedimentation size
ranges.
Friedlander (1965) and Friedlander and Wang (19) also pursued another path by introducing the
concept of self-preserving size distributions for dynamic aerosol systems. According to the selfpreserving distribution concept, in an aging aerosol system, the size distribution asymptotically
approaches a self-similar shape. In other words, when aging size spectra are properly normalized
by total number and volume, the consecutive size distributions follow a universal, self-preserving
shape that is characteristic for that aerosol system. In a numerical solution of the coagulation
equation, Hidy (1965) has indeed found a confirmation of the self-preserving size distribution
shape for coagulating aerosols.
The lack of complete, dynamic size distribution data covering the entire 0.01-10 m size range
of atmospheric aerosols in the 1960s have precluded a detailed verification of these theories.
That changed when Clark and Whitby (1967) reported an extensive set of complete size
distribution measurements in Minneapolis using the newly developed Minnesota Aerosol
Analyzing System (MAAS) (see next section). The new aerosol size distribution data were
welcomed by the atmospheric aerosol research community and sparked a vigorous debate
2
regarding the applicability and the causality of the Junge distribution. Clark and Whitby (1967)
themselves spoke in favor Friedlander’s ideas but Junge (1969) swiftly responded that the ‘selfpreserving’ distritibution of a coagulating aerosol can be ruled out as an explanation of the
atmospheric power-law data with negative slope4: since the slope of self-preserving
coagulating aerosol size distribution was much steeper. Also, Junge argued, that since thermal
coagulation is a weak mechanism for changing the size range above 0.1 m, there must be other
stronger mechanisms that govern the atmospheric aerosol dynamics. He named homogeneous
and heterogeneous gas-particle conversion, and cloud scavenging as candidate processes.
However, he has declined to speculate on the relative roles and magnitudes of these other
processes.
Figure 1. Volume distribution functions of several atmospheric aerosol types (Junge, 1969)
In a further significant statement, Junge also re-emphasized his earlier findings, that the slope of
the power law distributions can be rather different, depending on the aerosol type as reproduced
here in Figure 1 from (Junge, 1969). For example, the volume spectra of upper tropospheric
aerosols has a volume peak at around 0.1 m radius, while marine aerosols show a broad peak at
1-10 m. Only the aerosols observed in the continental boundary layer adhere to the flat Junge
distribution with 4 over the 0.1-100 m radius range. Finally, Junge offered an alternative
explanation for the flat Junge spectrum: Continental aerosol arise from many different aerosol
sources, each having a narrow size range but of different mean size in the range 0.1-100 m.
When these heterogeneous aerosols are mixed in the atmosphere, they form a broad size
spectrum that can be represented by a broad log-normal distribution which resembles the typical
flat Junge distribution2.
This was roughly the state of understanding in 1969 as the research groups were preparing the
Los Angeles smog aerosol study in Pasadena, California.
2
It is remarkable how close Junge came to a full explanation of the of the atmospheric aerosol dynamics in 1969,
yet how far he was from actually completing the story.
3
Instrumentation Developments: Minnesota Aerosol Analyzing
System (MAAS)
Until the late 1960s aerosol size distribution was obtained using impactors and filter
measurements, electron microscopy and other time consuming techniques that precluded
continuous monitoring of atmospheric aerosol dynamics. The total number of measured size
spectra that covered the 0.1-10 m range was very limited, probably in the range of 50-100
spectra. That has changed rapidly when in the mid 1960s Kenneth Whitby and Benjamin Liu at
the University of Minnesota Particle Technology Laboratory developed or adapted a suite of
instruments for near continuous in situ monitoring of atmospheric aerosol size spectra, Whitby et
al., (1972a). It consisted of an electrical mobility analyzer, an optical counter and a condensation
nuclei counter.
The heart of the Minnesota instrument package was the electrical mobility analyzer covering the
size range 0.008–0.5 m. Atmospheric particles were passed through a diffusion charger and
subsequently separated by the electric mobility. The current resulting from the charged particles
in a given mobility range was used as a measure of aerosol concentration in that range. The
instrument relied on the fact that the electrical mobility of charged particles was monotonically
decreasing with increasing particle size as shown in Figure 2. This allowed particle size
segregation in the size range 0.008-0.5 m. A history of the electrical mobility analyzer is given
in Sem and Whitby (2000).
Figure 2. Electrical mobility as function particle diameter. Note the distribution of mobility due to nonuniform charging.
4
Ken Whitby and Ben Liu have also made significant improvements in the size resolution of
commercial optical particle counters. In the commercial counters a wide jet of atmospheric
aerosols was passed through the entire illuminated volume and the scattered light pulse from the
individual particles was classified by a multi-channel pulse height analyzer. Whitby and Liu
have introduced the “sheath” air inlet such that the aerosol particles were passed through a
uniformly illuminated center of the light beam. The result was a significant improvement in the
size resolution. The availability of monodisperse polystyrene latex (PSL) beads in the size range
0.3-2.0 m allowed accurate size calibration of the optical counters. The performance of the
optical counter was also evaluated by theoretical values arising from Mie calculations.
The MAAS was not without problems. The most uncertain part of the measured size
distributions was in the size range 0.3-0.5 m, which was at the “edge” of the size ranges for
both electrical mobility analyzer and the optical counter. In fact, at about 0.4 m neither
instrument could be trusted to provide high quality data. In case of the electrical mobility
analyzer the particle electrical mobility curve becomes virtually size independent, as discussed
earlier Figure 2. Hence, particle misclassification in that size range was rather likely. On the
other hand, the optical counter was at the lower limit of its size detection. The light pulses
generated by 0.4 m particles were barely above the photomultiplier noise and it was plausible
that some of the noise was registered as particle signal in the 0.4 m range. Repeated field
calibrations of the optical counter with polystyrene latex (PSL) beads provided some comfort
that the OPC data are real. Another source of uncertainty was that the Royco model 220 had a
light scattering sensor at 90 degree from the light source. It was known from Mie calculations
and experimental data that at that angle the amount of light scattering is highly dependent on
particle shape. Hence, if a significant fraction of the smog aerosol was composed of irregularly
shaped soot particles, then the PSL calibration would be inappropriate.
Figure 3. The Minnesota Aerosol Analyzing System (MAAS) prior to shipment to the 1969 Pasadena Study.
5
The Minnesota Aerosol Analyzing System, MAAS, as prepared for the 1969 Pasadena
experiment is shown in Figure 3. The white cabinet houses one of the first TSI commercial
mobility analyzers (Whitby Aerosol Analyzer). The Royco optical counter, equipped with the
sheath air inlet along with the multi-channel pulse height analyzer is house in a separate cabinet.
The suite of MAAS instruments was completed by the General Electric condensation nuclei
counter for continuous monitoring of the total nuclei count, i.e. particles greater than 0.01 m.
The analog and digital data from these MAAS and other meteorological instruments were
gathered by a modern data acquisition system in 20 minute intervals. 3 As a result a total of 350
complete size distributions were recorded during the three-week Pasadena study, September 320, 1969. The digital data recorder was a teletype machine equipped with the punch tape unit that
recorded the digital data as holes in a continuous paper tape4.
This data acquisition and recording system allowed swift computer processing of the size
distributions and other monitoring data. In fact, the prompt availability of the size distribution
data to the other collaborating research groups allowed synergistic collaboration between the
Minnesota and other groups5.
The 1969 Pasadena Study: A Stimulating Environment
The 1969 Pasadena Smog Caper was conceived and conducted by three pioneers of atmospheric
aerosol science K. T. Whitby, S. K. Friedlander and P. K Mueller6. Whitby was a professor of
mechanical engineering at University of Minnesota, with keen interest in developing aerosol
instrumentation, and measuring the atmospheric aerosol size distributions. Friedlander a
professor of chemical and environmental engineering at the California Institute of Technology
was the ‘theoretician’ working on the explanations of the measured size distribution data using
principles of aerosol dynamics. Peter Mueller, head of the Air Industrial Hygiene Laboratory in
Berkley was the chemical expert, for both aerosols and gases with deep experience in chemical
measurements7. The measurements were conducted in September 1969 at the Keck Laboratory
of Caltech, Pasadena, California.
3
Ken Whitby was justifiably very proud of the modern data acquisition system of MAAS. He talked about it more
frequently than about the aerosol instrumentation.
4
In 1969, data editing was rather similar to the currently used cut-and-paste graphic user interfaces. The difference
was that in 1969 cut meant cutting the paper punch tape with scissors, replacing the bad data with a new punched
tape section and pasting the new tape segment with the appropriately perforated sticky tape.
5
For example, Dave Ensor and Bob Charlson could immediately work on calculating aerosol light scattering from
the size distribution data and compare those to their four wavelength nephelometer data.
6
I had the good fortune of being the PhD student of Ken Whitby and postdoctoral associate of Sheldon Friedlander
during the time when the bimodal distribution concept has emerged and matured. Obviously, in the right place at the
right time in the right company.
7
Prima donnas , collaboration Whitby mused about this as well.
6
Significant factors in the success of the 1969 Pasadena study were the high ‘visibility’ and the
stimulating intellectual environment. The leaders of the project Whitby, Friedlander Mueller
provided clear guidance. The Pasadena Aerosol project has also attracted several (then) junior
scientists e.g. Bob Charlson, George Hidy who eagerly contributed their instruments and rich set
of ideas while the monitoring was in progress (e.g. Figure 4)8.
Figure 4. Scene from the 1969 Pasadena Study: Bob Charlson of University of Washington (center) leading
an animated discussion next to the new four-wavelength nephelometer, the ‘coffin’.
Throughout the experimental period, Sheldon Friedlander as the host, has invited many visiting
dignitaries, including Arie Haagen-Smit, the “discoverer” of the Los Angeles smog and chairman
of the California Air Resources Board. This high visibility and stimulating company has
provided ample impetus to analyze and “show-off” the collected data and to discuss the features
of the size distribution with the participants and visitors – virtually as it happened.
The collective frame of mind of the Minnesota group was particularly upbeat 9. (Figure 5). There
was no question in minds of the Minnesota contingent, that something really interesting will be
discovered about the Los Angeles smog aerosol, but what will that be?
8
For us graduate students this was an intense learning experience on how “science” is conducted, how scientist
operate. I never forget a scene with Bob Charlson, probably the one pictured in Fig. 5. Bob arrives from the airport,
enters the lab and immediately looks at the strip charts of his four-wavelength nephelometer. First, it showed a large
spread of the scattering and then abruptly the scattering in the four wavelengths overlapped. Eureka! Bob turns to
me and says: ‘Lets write a paper together comparing his nephelometer data with our sizes distributions’. It took full
5 minutes between his entrance and the plan to publish!
9
In fact, I talked Ken Whitby and Ben Liu into posing with the fake headline “U of Minn. Solves L.A. Air
Pollution”. Little did we suspect that 30 years later ‘U of Minn.’ helped solving the national air pollution problem.
7
Figure 5. Ken Whitby (left) and Ben Liu of University of Minnesota pose in front to the Minnesota Aerosol
Analyzing System (MAAS) in Pasadena.
The LA Smog Data: The Bimodal Distribution
Since the MAAS data acquisition and recording were completely automatic it freed much of our
time for data displays, special experiments, and stimulating discussions with the participants and
visitors. One of the size distribution displays posted on the bulletin board (Figure 6) early in the
measurement period was the sequence of “Typical daily changes in spectra.”10 It showed a strong
increase of 0.01-0.02 m size range during the day compared to the late night hours. So, we were
looking for aerosol dynamics, we have observed aerosol dynamics from the first day on.
10
If Ken Whitby was there when I posted this chart, he would have given me a real lecture on statistics. Two or
three days of data are NOT enough to derive as “typical” spectra.
8
Figure 6. Posting of early smog size distribution data, plotted as number, surface and volume spectra. Note
the evidence of increased daytime concentrations.
Once the size distributions were plotted routinely on daily basis, a persistent feature of the LA
smog aerosol was appearing: a sharp peak in the volume distribution at about 0.3 m. It was
even evident in log-log plots that we have used at that time (Figure 6). The sharpness of the
volume peak was rather puzzling since it deviated rather significantly from our expectation of a
broad Junge type distribution. But was the persistent volume peak at 0.3 m ‘real’ or
‘instrumental’?
Throughout the measurement period the Minnesota group and others have discussed the
possibility that the sharp peak was due to instrumental artifacts. This was rather plausible since
the volume peak occurred at the “edge” of the size ranges for both electrical mobility analyzer
and the optical counter as discussed in the previous section. A certain level of comfort arose
from the fact that in most days the concentration of 0.1-0.2 m size particles co-varied with 0.60.8 m size range. Since these ranges were well within the sensitive range of the respective
instruments, it indicated that there was indeed a single aerosol mode in that size range.
Nevertheless, this measurement uncertainty in the 0.3-0.5 m size range was the single most
troublesome aspect of the Pasadena measurements11.
Other aspects of the dynamic size distribution data and the special experiments on aerosol
formation and decay are discussed in the next section on aerosol dynamics.
11
In fact, during my thesis work in 1970 I have spent over half of the time on re-calibrating the electrical mobility
analyzer with emphasis on the 0.1-0.5 m size range. In doing so, the experience and the guiding wisdom of Ken
Whitby was invaluable. In fact, as still vividly remember his baseball analogy “In baseball it is perfectly OK to miss
two third of the balls, as long as one hits one third. In science, he said, there is a ten-to-one rule. If you miss once, it
takes ten good hits to fix the missed hit.” This was his way of telling that there is no way we should miss the
existence of this fine mode peak. The mobility distribution chart in Figure 2 was a key result of that work.
9
Stretching the plot. Around 1970, while searching the literature for my thesis, a ran across a
paper by Berry (1967) who made the strong case that for coalescing (and coagulating) aerosol
systems, one should plot the volume distribution on semi-log paper: dV/dlogD. In such a plot,
the area under the volume distribution curve is proportional to the volume concentration. For
depicting dynamic aerosol processes this is advantageous, since it shows, in a quantitative way,
the shifts in particle size as well as gain or loss in mass. Equally important to us at that time, the
linear volume scale accentuated the peaks of the volume distribution which made the bimodal
distribution much more apparent than in the more flat log-log plot. Ken Whitby has immediately
accepted the use of the semi-log plot and from there on all the volume spectra were published in
the semi log form as shown in Figure 7 . (Whitby et al., 1972b, Husar et al., 1972)12.
Figure 7. Normalized ‘grand average’ size distribution of the LA smog with clear indication of the bimodal
volume distribution. The influential processes included nucleation, coagulation, condensation and
sedimentation.
The Grand Average Los Angeles and Minneapolis Spectra. Probably the most succinct
depiction of the bimodal distribution concept is the normalized ‘grand average’ size distribution
plot shown in Figure 2 taken from the summary paper Whitby et al., 1972b)13. Figure 7 shows
the grand averages for LA, including the number, surface and volume weighing of the smog
aerosol spectra, the different subranges, as well as the dominant mechanisms that govern the
12
Throughout the years, I was rather proud of this ‘unique contribution’ to the discovery of the bimodal distribution.
It was not until Friedlander (2000) has brought to our attention the fact that Junge (1963) has used that type of a plot,
probably for the same reasons as we did! It was just one of the many re-discoveries.
13
Whitby was always keen on statistics and such ‘grand averages’ were key part of his analytical toolbox.
10
aerosol dynamics in each subrange. The semi-log plot clearly conveys the bimodal volume
distribution with peak in the 0.1-1.0 m size range. It also indicates the rise of the mass spectra
above 2m but the data abruptly end at 6 m since in the Royco optical counter reached only up
to that size. The grand averages for Minneapolis, when presented in the same way, showed a
similar pattern including the volume peak in the 0.1-1.0 m size range. However, the story of the
bimodal distribution was not quite complete. The lingering questions were: Was there an equally
sharp coarse particle mode and at what size? How do the two modes compare? Is the bimodality
specific to LA and Minneapolis?
Completing the Bimodal Distribution Story: The Coarse Particle Mode. Around 1970-71,
Whitby has completed the bimodal distribution story by collecting and analyzing several size
distribution data sets arising from different locations, times, and sampling methods. Whitby
presented the summarized data in Figure 14 in Whitby et al. (1972b). The broad range of data
provided strong evidence that the bimodal distribution is ‘real’ i.e. it occurs as a ubiquitous
feature of atmospheric aerosols in general. The lower, fine particle mass mode was consistently
between 0.1-1.0 m but it was shifting considerably between 0.1 and 0.8 m depending on
location and time. All the data have indicated a saddle point between the modes in the 1-3 m
size range. The assembled data showed a distinct coarse particle peak but the characteristic size
of the coarse particle mode tended to increase with increasing coarse mass.
With this, the bimodal distribution as a new aerosol model was established to Whitby’s
satisfaction and the summary paper on the bimodal distribution (Whitby et al.,1972) was ready
for publication. The rest, as they say, is history.
11
The Explanation of the Bimodal Distribution: Aerosol
Dynamics
The Whitby et. al., (1972b) paper described the bimodal distribution but the causal processes and
mechanisms were not elaborated. Key scientific issues have not been tackled: What is the
explanation for the bimodality? What are the key driving mechanisms each of the modes?
The introduction of the bimodal concept has allowed swift progress in establishing the governing
mechanisms throughout the size spectrum, including the nuclei and accumulation modes. It was
no longer necessary to lump the entire size range (0.1-20 m) into a single aerosol dynamic
system.
Dynamics of the Coarse Mode
In the summer of 1970 Whitby and Husar have participated in another aerosol study in Ft.
Collins, Colorado. While continuously monitoring the ambient aerosol, they observed the
changing size distributions during the passage of a dust event with a strong increase of coarse
particles and simultaneous decrease of fine particle mass.
Figure 8. Measured size distribution of dust in Colorado showing a strong coarse particle peak. It is
compared to the average LA smog aerosol.
The dusty aerosol spectra for Fort Collins is compared to the grand average LA aerosol in Figure
8. Such observations have confirmed that the dynamics of the fine and coarse mode aerosols
tended to vary independently. This is a key consequence of the bimodal model, since the modes
were governed by different sources, formation, transport, and removal mechanisms.
12
By the 1970s, the scientific literature has well established that coarse sea salt particles were the
result of mechanical breakup and dispersion and that sedimentation was the key removal
mechanism (e.g. Blanchard, 1955). It was also generally accepted that wind blown dust mass in
the coarse particle size range, well above 1.0 m (e.g. Gilette, 1972). With the physical
processes and the dynamics of the coarse mode understood, in principle, no further effort was
invested in the coarse mode. Besides, suitable instrumentation for the measurement of large
particles (>10 m) was not available.
Dynamics of the Fine Particle Spectra
The remaining scientific issues pertained to the dynamics of sub-micron size particles. As seen in
the grand average Figure 7, the fine particle mode is at very different location depending on the
weighing of the size distribution. The mode of the fine particle number concentration was about
0.01 m, the surface area peaked at 0.2 m, while the volume at 0.3 m. It became evident
early on during the observations that the dynamics of the number concentration was virtually
decoupled from the dynamics of the fine particle volume spectra. For this reason, the fine
particle mode could be sub-divided into a ’nucleation’ range (0.01-0.1 m) and an
‘accumulation’ range. The level of physico-chemical understanding in these subranges was
considerably weaker since the sources, transformation and removal processes were only partially
established. Furthermore, the nature of the interaction between the nucleation and accumulation
mode was unclear.
Ever since the landmark work of Haagen-Smit (1952) on the chemistry and physiology of the
Los Angeles smog, it was clear that visible smog particles are largely due to photochemical
reactions and gas-particle conversion processes. The subsequent aerosol studies (e.g. Leighton,
1961; Renzetti and Doyle, 1960; Stevenson et al., 1965; Goetz et al., 1961; Goetz and Pueschel
1965; Goetz and Pueschel, 1967) have revealed that the formation of light scattering aerosols
was driven by the concentration of gaseous precursors. Goetz and Pueschel (1967) have also
shown that in the aerosol-rich Los Angeles basin, the gas to particle conversion process occurs
mostly by heterogeneous nucleation on existing particles rather than homogeneous nucleation.
These studies have revealed a very complex interaction between the precursor gases (SO2, NOx,
hydrocarbons, and water vapor). As Goetz and Pueschel (1967) point out, “the available results
did not permit even a preliminary interpretation of the very complex [positive and negative]
reaction pattern involved in smog aerosol formation”. In particular, the evolving size
distribution of aging photochemical aerosol was not on hand. This was the topic of the third
paper in the Minnesota trilogy on the physical mechanisms governing the dynamics of Los
Angeles aerosol (Husar et al., 1972) 14.
Given the extensive aerosol dynamics data set collected during the 1969 Pasadena Study it was
possible to infer the governing mechanism based on the observations rather than from purely
14
In preparing for the Pasadena aerosol study, Whitby has frequently expressed his anticipation that the project will
reveal the dynamic size changes of aging photochemical aerosols. Hence, obtaining and interpreting such data was
definitely not an accident. I was fortunate that by the time of the Pasadena experiments, I had learned enough about
Whitby’s size distribution ideas and Friedlander’s aerosol dynamics approach, so that I could tie the two together –
during the in Pasadena study and later in finishing my PhD theses work.
13
theoretical considerations. The key issues at hand were the specific roles of nucleation,
coagulation and heterogeneous gas particle conversion.
Dynamics of the Nuclei Subrange: Coagulation
The role of coagulation was the easiest to evaluate and quantify based on the ambient data,
laboratory experiments, and numerical simulation15. Coagulation was found to be a significant
mechanism for the removal of particles in the size range below 0.1 m. Night after night, the
smog particle concentration in 0.01-0.1 m size range declined by an order of magnitude or more
with the corresponding reductions in the total number concentration. The diurnal pattern of this
size range was simulated numerically by solving the coagulation equation with initial conditions
corresponding to the daytime spectra. The results of such simulation have shown that the decline
of nighttime concentrations in 0.01-0.1 m size range could be explained by coagulational mass
transfer to the “accumulation” mode in the 0.1-1.0 m size range.
?????Hence, it was established that the total number concentration of particles was dominated by
primary emissions (presumably from automobile), some photochemical nucleation during the
midday hours followed by decay due to coagulation over the night hours.
Condensation
Establishing the dynamics of the accumulation mode aerosols focused on three factors: 1)
primary emissions into that subrange; 2) coagulation transfer from the nucleation mode; 3)
condensational growth by heterogeneous condensation of photochemically produced vapors.
Coagulation mass transfer from 0.01-0.1 m size range was quickly eliminated as a significant
factor for the accumulation mode. Based on Monte Carlo coagulation simulations the amount of
mass transferred by coagulation into the accumulation mode range was found to be insignificant.
The role of primary emissions for the accumulation mode was also assessed to be of weak
significance for the Pasadena aerosol. This was based on the observation that the mass in the
accumulation mode had a peak during the midday hours, while the various tracers of primary
emissions (NO, condensation nuclei) showed a morning peak and an evening peak associated
with rush hour traffic. Thus, the remaining explanation for the midday growth focused on the
heterogeneous conversion of condensable species.
The sharp mid-day accumulation of the aerosol volume is illustrated in Figure 9a. I shows a
virtually constant-volume mean size as the fine particle volume increased in the morning and
decreased in the afternoon. This was a puzzling pattern since condensational growth of particles
tend to increase the mean particle size. This paradox was resolved by a set of condensational
growth calculations reproduced in Figure 9b. In this case, the initial size distribution was taken to
be the measured spectra at 10:00 AM, and subsequently grown by diffusion limited
condensation. An important constraint in the condensational growth equation was the assumption
15
Besides, this was the topic of mine Ph.D. dissertation “Coagulation of Knudsen Aerosols”, so I was most
comfortable with the topic, Husar, 1971.
14
of a critical particle size of 0.09 m. Particles below this size range were taken to be below the
“Kelvin cutoff” and therefore not subject to condensational growth.
Figure 9. Measured (left) and simulated (right) growth of photochemical smog aerosols. The simulation
assumes condensation of precursor gases onto existing particles.
A comparison of the observed and simulated condensational growth pattern revealed that in both
cases the volume mean diameter has remained constant but the magnitude of the peak has
increased. In other words, the mass has accumulated without much change in the mean size –
hence the term ‘accumulation’ mode. The explanation of the paradox was that for a broad initial
size distribution, the small particle range growth faster than the large ones. In other words, the
abundance of 0.1 m particles more than replenished the particles growing out of the 0.3 m
sub-range.
Special Studies on Aerosol Dynamics
During the 1969 Pasadena Study several aerosol dynamics experiments were carried out to
further explore the dynamics of the Los Angeles aerosol. In the “big bag” experiment a large
plastic balloon of 50 m3??? was rapidly filled with ambient aerosols. Subsequently, the aging
size distribution in the bag was monitored to measure the aging aerosol. It was confirmed that
coagulation rapidly reduced the concentration of total number of particles. The fact that even in
the presence of sunlight the number concentration has decayed by coagulation indicated that in
the presence of smog aerosol, homogeneous nucleation was not taking place.
Repeating the experiment but filling the bag with aerosol-filtered air yielded dramatically
different results. The total number concentration in 0.01-0.03 m sub-range rapidly increased
within 15 minutes from zero to over 105 particles/cm3. Clearly, the gaseous precursors in the “big
bag” illuminated by sunlight were self-nucleating. Repeating the self-nucleation experiments
after sunset yielded no nucleation. This confirmed that the nucleation of gaseous precursors was
driven by solar radiation. Conversely, this special experiment confirmed that in the presence of
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smog particles the self-nucleation was suppressed and that the gas–particle conversion must have
occurred onto the existing particles.
In another special experiment the smog aerosol was artificially humidified by passing air by a
wet surface which increased the relative humidity from 25% to 86%. The measured aerosol
volume concentration increased from 26 to 38 m3/cm3 as a result of humidification. However,
the mean particle size did not change. This experiment implied that a considerable fraction of the
smog aerosol in the sub-micron range was hygroscopic. Therefore, relative humidity was an
important meteorological parameter influencing the dynamics of the smog aerosol. At that time,
this was new information since previous reports by Cadle at al. (1950) and others indicated that
smog aerosol was ‘dark brown gummy, water-insoluble organic material’.
Based on the field observations, special experiments and numerical simulation, the following
conclusions were reached regarding the Los Angeles smog aerosol dynamics (Husar at al., 1972;
Husar and Whitby, 1973). The driving force for the photochemical gas-particle conversion in the
accumulation mode were gaseous photochemical reactions producing condensable species. In the
presence of foreign nuclei, the concentration of these species was relaxed by deposition on
existing particles (heterogeneous nucleation). The growth of the accumulation mode aerosol was
consistent with diffusional condensation. The amount of surface area required for heterogeneous
nucleation was found to increase with conversion rate. In the polluted Los Angeles atmosphere
the growth on foreign nuclei dominated the gas-particle conversion rate and this condensational
growth was largely responsible for the growth in the ‘accumulation’ mode. Coagulation tended
to limit the lifetime of particles below 0.1 m in size but it did not influence the concentrations
in the accumulation mode. The smog aerosol was found to be hygroscopic.
Summary
In the view of this undoubtedly biased chronicler, the most significant factor in the emergence of
the bimodal aerosol model was Whitby’s new aerosol measuring system for dynamic, in situ size
distribution measurement over three decades of particle size. It provided the observational
evidence of the ubiquity of the two mass modes. The aerosol chemistry data organized first by
Peter Mueller and subsequently analyzed by Friedlander and coworkers showed that the fine and
coarse mass modes were chemically distinctly different. In fact, the differences in the chemical
composition of different aerosol types is the basis for Friedlander’s chemical mass balance
method (Miller et al., 1972; Heisler et al., 1973). The aerosol optical data by Bob Charlson and
co-workers confirmed that the accumulation mode was responsible for the scattering of visible
light (Ensor et al., 1972).
In the context of the bimodal size distribution history, it is ironic how close Junge was not only
to observe but also to explain the remarkable regularities of atmospheric aerosols. He clearly
identified aerosol mass modes for specific aerosol types (e.g. upper tropospheric fine particle
mode and marine coarse mode). The deviation between the Junge and the Whitby models was in
the shape of the continental aerosol spectra, composed of a mixture of different aerosol types
with varying size distributions. Junge indicated a broad flat distribution in the range 0.1-200 m
16
for the mixture of the different continental aerosol types. On the other hand, the Whitby
distribution for the same continental aerosol mixture showed two distinct modes16.
The second factor was the use of aerosol dynamics as introduced and promoted by Sheldon
Friedlander. Applying these analytical tools helped Husar and others to explain the observed fine
particle dynamics and hence provided a robust scientific support for the bimodal concept based
on accepted physical mechanisms. These mechanisms are the basis of the current regional
dynamically coupled gas-aerosol models that are the key tools for the implementation of air
quality management programs.
Thirdly, a major factor in the discovery, promotion and the general acceptance of the bimodal
distribution concept was the unprecedented collaborative spirit of the visionary organizers of the
1969 Pasadena Aerosol Study and other participants. That collaborative and supportive spirit
created a most stimulating environment for the collective creation of the new atmospheric
aerosol paradigm.
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