Progress Report
EPA grant No. CR 827981
Evaluation of the Models-3C/CMAQ System
Relationship between Airport Visibility and PM2.5 Concentrations
in the Eastern US
Sponsoring Agency
US Environmental Protection Agency
Office of Research and Development
Research Triangle Park, NC 27711
Submitted by:
Bret A. Schichtel
Principal Investigator: Rudolf B. Husar
Center for Air Pollution Impact and Trend Analysis
Washington University
St. Louis, MO 63130-4899
Abstract
The use of the visibility data and IMPROVE light scatter data for estimating PM2.5
concentration is assessed. Based upon a literature review and a regression analysis it was found
that the bsp could be related to the fine particle matter via a scattering efficiency of 4 m2/g. This
relationship was as good as a model based on haze, fine soil and coarse mass. Using the simple
model, the bsp can explain 73% of the spatial and daily variation in PM2.5 concentrations. There
was a poorer correspondence between the visibility derived dry aerosol bext and reconstructed bsp
from fine mass. Comparisons of daily data at individual sites had r2 values from 0.23 - 0.7 and
an average of 0.4. In addition, the aerosol bext to fine mass ratio was found to vary substantially
with space. These result imply that the light scattering data is a good surrogate for the Eastern
US fine mass and can be used to quantitatively estimate daily fine mass concentrations.
However, the use of the visibility data as a surrogate for PM at high time resolutions should be
restricted to aiding the spatial and temporal interpolation of fine particle data.
Bret Schichtel, D. Sc
May 10. 1999
1.0 Introduction
MODELS-3/CMAQ is a sophisticated modeling system designed to simulate and
investigate fine particle mass, its species and its impact on light extinction. It is a regional scale
model capable of simulating the air quality over the Eastern US at high spatial and temporal
resolutions. Due to the large computational demands and data inputs required to operate the
model over the regional scale, MODELS-3 is restricted to the simulation of episodes on the order
of several weeks. Annual or longer term averages are estimated by simulating a number of
different episode types and aggregating them together. Therefore, to simulate the long term
averages it is necessary to determine the different classes of episodes and their frequency of
occurrence from air quality data. In addition, MODELS-3 is a relatively new system that needs
to be thoroughly evaluated by comparing model results to measured data. In the evaluation, it is
desirable to have high air quality data that matches the spatial and temporal domains and high
resolution of the model results.
Prior to the establishment of the National Fine Particle Network in 1999, fine particle
data were generally collected for research purposes at only a few monitoring sites over short
periods of time, months to several years, and infrequently, 1-3 times per week. Currently the
most extensive aerosol monitoring network is IMPROVE (Malm et al., 1994), which collects
speciated fine mass data at 81 sites in National Parks and Wilderness areas in the US twice a
week with some data extending back to 1988. Twenty two of these sites are located in the
Eastern US. This data set is inadequate to fully evaluate the spatial and temporal dynamics of
the MODELS-3 results and to determine the number of different types of air quality episodes and
their frequency of occurrence. Therefore, other datasets and particulate matter surrogates are
needed.
A good surrogate for particulate matter is measured light scattering and extinction
coefficient. Light extinction is due to the scattering and absorption of light by gases and
particles and is therefore dependent on the aerosol concentrations. The theory of light extinction
and the contributions of particle light scattering and absorption is well understood today, and a
number of researches have developed models that accurately reproduce measured light scattering
from size segregated speciated aerosol data (NAPAP, 1990, White et al., 1994, Malm et al.,
1994). The primary difficulty in using light extinction data as a fine particle mass surrogate is
that aerosol light extinction depends on a complex function of the ambient aerosol chemistry,
shape, density, size distribution, and index of refraction, as well as the optical properties of the
instruments used to measure the light extinction (White 1986, Molenar 2000). However, it has
been found that good relationships between fine mass and particle scattering data do exist
(NAPAP, 1991). For example, using Lorenz-Mie scattering theory and estimates of the variation
of ambient aerosol properties over the US, and the optical characteristics of nephelometers,
Molenar (2000) estimated an error of 30-40% when using nephelometer scattering data to
estimate PM2.5 concentrations.
Several Eastern US light scattering and extinction monitoring networks are in operation.
The IMPROVE network monitors hourly light scattering via nephelometers at a number of its
monitoring sites. These data are potentially useful for filling in the IMRPOVE fine mass time
series. The most extensive network is hourly light extinction derived from human observed
visibility data at the National Weather Service's surface monitoring network (Husar et al., 1979).
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The visibility data are collected at over 300 locations, primarily at airports, throughout the US
every hour with some site's data records extending back to 1948. This long term datasets unique
characteristic of high spatial and temporal resolution has made it the center of a number of
investigations to understand the visibility patterns (Husar et al., 1979, Husar et al., 1981, Husar
and Holloway, 1984 Husar and Wilson, 1993) its relationship to light scattering and particulate
mass (Griffing, 1980, Dzubay et al., 1982, Ozkaynak et al., 1985, Schichtel et al., 1992, Husar,
et al., 1997, Molenar, 2000,) and as a surrogate for fine particle mass (Falke et al., 2000).
This analysis evaluates the use of the visibility data and IMPROVE light scatter data for
estimating fine particle mass. This includes developing a model relating visibility and light
scattering data to speciated aerosol data, and assessing the contribution of the various aerosol
types to the light scattering. In addition, error associated with relating the scattering and
extinction estimate to fine particle alone will be assessed.
Relating visibility to aerosol concentrations involves establish several intermediate
relationships which are schematically presented in Figure 1. As shown, the process can be
broken into four primary relationships, the relationship between visibility and ambient light
extinction, ambient light extinction and wet particle scattering, wet particle scattering and dry
particle scattering and dry particle scattering and measured aerosol concentrations. In this
analysis, all four relationships will be established. This will be done by drawing upon the
extensive results from past studies and validating these results using the aerosol and
nephelometer data from the IMPROVE monitoring network and airport visibility data.
The quality of the airport visibility data for estimating aerosol concentrations in the
Eastern US will be evaluated by first estimating the dry aerosol scatting and absorption from the
visibility data and reconstruct the dry particle scattering from the aerosol data. These two
estimates of light scattering will then be reconciled. The ability of the visibility data to be used
as a surrogate for the aerosol mass will be established from this reconciliation.
2.0 Data
2.1 Hourly Surface Airways
The Hourly Surface Airways dataset (Steurer and Bodosky, 1999) contains hourly or 3hourly surface weather observations measured primarily at major airports and military bases.
The digital record of the surface observation data is available from 1948 to the present. In this
time period a number of stations have come on and gone off line. Today there are approximately
370 operational stations in the U.S. with about 150 of these stations operational since the late
1940's (Figure 2). Each station records a complete range of meteorological parameters
including: visibility, cloud, wind, temperature, sky cover, relative humidity, pressure, and present
weather data. The stations are operated by the National Weather Service (NWS), U.S. Air Force
(Air Weather Service), U.S. Navy (Navy Weather Detachment) and the Federal Aviation
Administration (FAA).
Prior to 1992, instantaneous observations were collected by human observers during the
ten minutes prior to the hour. Cloud data, visibility, present weather, and freezing rain were
estimated and noted at the time of observation. Temperature and dew points were read from a
dial or analog chart record. Pressure was read from a dial or scale on the mercurial barometer and
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precipitation was manually measured at the gauge each hour. Wind data were estimated by
viewing a dial for one minute and estimating the average speed and direction during that time.
In September 1992, the Automated Surface Observing System (ASOS) was gradually
phased in at the monitoring sites replacing the human observers (Appendix A). ASOS was
designed specifically to support aviation operations and forecast activities. As a result,
significant changes have occurred in this data set for many previously observed weather
parameters, and possible data biases may have been introduced in the historical record. Due to
the potential for bias no ASOS data will be used in the analysis.
2.2 Aerosol Data
The aerosol concentration data were obtained from the IMPROVE (Interagency
Monitoring of Protected Visual Environments) (Malm et al., 1994). The IMPROVE fine particle
network collects PM2.5 and PM10 samples over a twenty four hour period (midnight to
midnight) every Monday and Friday using IMPROVE samplers. The network consists of 81
monitoring sites, located in rural areas, operating between 3/88 to present (Figure 3). Twenty
one of the IMPROVE samplers are located in the Eastern US. The PM samples are analyzed for
PM2.5 mass and its elemental constituents, organics, ions, light absorption and PM10 mass.
2.3 Nephelometer data
Starting in 1993, the IMPROVE monitoring network started to installed nephelometers at
selected monitoring sites measuring the hourly light scattering coefficient. Today, 27 monitoring
sites are instrumented with these nephelometers, 11 of which are located in the Eastern US
(Figure 4). In addition to light scattering, relative humidity and temperature are measured.
3.0 Derivation of Dry Aerosol Light Extinction from Airport Visibility Data
3.1 Relationship of Visibility to Ambient Light Extinction
The visual range is a subjective concept, being the maximum distance at which an
observer can discern the outline of an object. The obvious limitations in actually making a
judgment of visual range includes the observers' visual acuity, the number, configuration, and
physical and optical properties of the visible targets. Observer's subjectivity imposes a random
component on the observed signal. The lower contrast of real targets compared to black objects,
imposes a systematic underestimate of visual range. In addition, visibility is reported in
quantized units, depending on the availability of visible targets. Thus, an observation of 10 miles
means that the visual range is greater than 10 miles. The reported visual range is always an
underestimate of the actual visual range and the calculated extinction coefficients are always
overestimates.
The visual range is reduced by atmospheric gases and aerosols absorbing and scattering
light out of and into the line of sight, i.e. light extinction, bext. Visibility can be related to bext via
the Koshmieder equation, bext = K/Visibility , where K is the Koshmieder coefficient, the log of
the contrast threshold of the human eye. For the typical contrast threshold of 0.02, K = 3.92.
The Koshmieder equation is based upon the following assumptions: 1) the illumination from the
sun is the same at the target and the observer, 2) the aerosols are homogenously distributed in the
atmosphere, 3) the observer has a horizontal view at the target, 4) the targets are large ideal black
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objects. These assumptions are most likely to be met during the afternoon hours when the sun is
over head and there is good vertical mixing. However, the assumption of black targets is never
truly met. Calibration of noon human visibility data with measured light scattering data have
shown that K typically ranges between 1.4 and 2.2 (Griffing, 1980, Dzubay et al., 1982 and
Ozkaynak et al., 1985, Schichtel et al., 1992). In this analysis K = 1.9 was used.
The extinction coefficient is in units of 1/km and is proportional to the concentration of
light scattering and absorbing aerosols and gases. The visual range is influenced by both haze
and natural obstructions to vision such as rain and fog. The role of these natural obstructions
were eliminated by discarding data that occurred during periods of rain, fog and when the
relative humidity was above 90%.
Visibility Data Quality Control. A problem with the visibility data is that a distance
limit usually exists beyond which the visual range is not resolved. This is due to either a lack of
markers, or to observations rules that do not require reporting visibility beyond this limit. These
limits are clearly identifiable by a hard edge in the data time series (Figure 2) and truncation of
the visibility distribution function. Note that the threshold is not fixed but changes as visibility
markers are added or removed from a station. These threshold value can be very restrictive in
that at some location 90% of visibility observation are beyond the threshold value. In addition,
some location use only a few, 3-5, visibility targets reducing there ability to identify small
changes in bext. Sites with low visibility thresholds or use only a few visibility markers are
applicable to climatological studies only and in these cases care must be taken to insure they do
not bias the results. Appendix A, list all of the surface observation sites including their visibility
threshold from 1990 - 1995.
3.2 Relationship of Ambient Light Extinction to Wet Aerosol Extinction
The light extinction coefficient is the sum of the light scattering and absorption of
particles and gases:
bext = bsp + bap + bRay + bag
where
bext
bsp
bap
bRay
bag
=
=
=
=
=
(1)
light extinction coefficient
light scattering coefficient due to particles
light absorption coefficient due to particles
Rayleigh light scattering coefficient due to gases
light absorption coefficient due to gases
Rayleigh scattering can be theoretically computed and varies with altitude from 9-12 Mm-1 with
the lower values at mountain tops. This work uses a constant bRay = 10 Mm-1. Absorption due to
gases is primarily due to NO2. The data used in this analysis is primary in rural and suburban
areas where the NO2 concentrations are low. In addition, Ozkaynak et al., (1985) found that in
Eastern US cities the NO2 contribution to bext was less than 1%. Therefore, bag is assumed to be
negligible. The particle light absorption is primarily due to elemental carbon or soot and soil
particles. Estimates of light absorption in the Eastern US range from ~10% in rural areas to 30%
in urban areas (Malm et al., 1994).
Light scattering is generally the largest contribution to light extinction. All particles in the
atmosphere scatter light, but the degree to which the particles scatter light is primarily dependent on
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the particle size, index of refraction and density. If the aerosol is externally mixed or if in an
internally mixed aerosol the index of refraction is not a function of composition or size, and the
aerosol density is independent of volume, then (Ouimette and Flagan, 1982):
bsp =   i ci
(2)
i
where ci is the concentrations of species i and αi is the scattering efficiency [m2/g].
An aerosol light extinction coefficient was calculated from the visibility derived bext by
subtracting off the Rayleigh scatter and assuming gaseous absorption was negligible. The
remaining contributors to the light extinction are particle scattering and absorption. The bap
cannot be subtracted out since it varies with space and time and no estimate of bap is available in
the surface observation data set.
3.3 Relationship of Wet Aerosol Light Extinction to Dry Aerosol Light Extinction
Relative humidity can have a power effect upon light scattering. As the humidity
increases the hygroscopic fraction of fine particles, i.e. sulfate, nitrate, and some organics, grow
in size increasing their light scattering (Malm et al., 1994, Sloan 1984, 1986; Tang et al., 1981).
IMPROVE fine particle samples are analyzed in a controlled laboratory with RH ~ 40%.
Therefore, any reconciliation between ambient visibility measurements and aerosol
concentrations need to account for increased ambient light scattering due to high relative
humidity.
The light scattering-humidity relationship depends on the particle composition,
microstructure (i.e. internally or externally mixed aerosol) as well as the history of relative
humidity values previously experienced by the particles (Sloan 1984, 1986, Malm and
Kreidenweis, 1997). These particle properties and history are not known for the visibility data.
Therefore, an average light scattering-humidity relationship was derived from six years of
scattering coefficients measured by nephelometers and visibility data over the Eastern US (see
Appendix B) that was applied to all data. Figure 5, presents the average RH correction factor
used to correct the ambient aerosol light extinction coefficient to a dry extinction coefficient at a
relative humidity of 40%.
3.4 Summary
The derivation of the dry aerosol light extinction from the airport visibility data involved
a number of steps. First, the airport visibility data were converted to light extinction using the
Koshmieder equation and a Koshmieder constant of 1.9. The assumptions of the Koshmieder
equation are most likely met during the afternoon, so only noon data were used. The visual
range is influenced by both haze and natural obstructions to vision, such as rain and fog. The role
of these natural obstructions were eliminated by discarding data that occurred during periods of
rain, fog and when the relative humidity was above 90%. The Rayleigh light scattering was
subtracted from the noon weather filtered bext values and absorption due to gases was assumed to
be negligible. Therefore, the remaining light extinction is due to the scattering and absorption by
particles, i.e. the aerosol light extinction. Last, the data were corrected to a 40% relative
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humidity by multiplying the aerosol bext values by an RH correction factor derived from the
measured light scattering and visibility data.
4.0 Reconstructing Dry Light Scattering from Measured Aerosol Data
The relationship of dry light scattering and aerosol data has been an extensively studied
subject (Ouimette and Flagan, 1982; Hasan and Dzubay, 1983; White, 1986; Malm et al., 1994;
White et al., 1994; McMurry et al., 1997). In this section, results from these studies are drawn
upon to examine this relationship in the Eastern US during the 1990's, and reconstruct the dry
light scattering from the IMPROVE aerosol monitoring network. The primary aerosol
contributions to the light scattering will be evaluated. In addition, the quality of the
reconstructed light scattering is assessed by comparing the results to measured light scattering
from the IMPROVE Nephelometer network.
4.1 Relationship between Dry Light Scattering and Measured Aerosol Data
Light scattering is linearly dependent on the contribution from each aerosol type
assuming externally mixed aerosol types (Equation 2). However, for multi-component particles
Equation 2 is unable to properly apportion light extinction to the aerosol components (White
1986). Fine particles are dominated by the products of condensation and atmospheric reaction
resulting in multi-component particles, thus Equation 2 is strictly not valid.
It has been shown that the derivation of bulk aerosol scattering properties is rather
insensitive to the extent of internal and external mixing (Ouimette and Flagan, 1982, Hasan and
Dzubay, 1983, White, 1986, McMurry et al., 1997, Malm and Kreidenweis, 1997). Therefore,
Equation 2 can be used to compute extinction coefficients for subsets of the fine particles
grouping the internally mixed species together. Following White et al., (1994) and McMurry et
al., (1997) the fine mode is considered to be composed of two externally mixed fractions of haze
and soil, where the haze consists of the sulfate, organics, nitrate, etc. Coarse aerosol mass is
treated as a third externally mixed aerosol type. While coarse mass is composed primarily of soil
particles it is separated from the fine soil due to its larger particle size and poor correlation with
the fine soil. Using all Eastern US data from the IMPROVE network, the correlation between
fine soil and coarse mass is only 0.37.
Using the above assumptions, light scatting can be related to aerosol concentrations via:
bsp = haze * chaze + f. soil * cf. soil + c. mass * cc. mass
(3)
Where chaze = PM2.5 - Fine soil
cf. soil = 2.2*[Al] + 2.49*[Si] + 1.63*[Ca] + 2.42*[Fe] + 1.94*[Ti] (Malm et al., 1994)
cc. mass = PM10 - PM2.5
The light extinction efficiencies, i, for these aerosol types in the Eastern US, compiled from a
number of sources, is presented in Table 1 and 2. In addition, the extinction efficiencies derived
in this study from a multiple linear regression analysis of haze, fine soil and coarse mass from all
Eastern US IMPROVE aerosol and nephelometer data is presented. Note, this regression
analysis did not use data from Shining Rock NC, due its high elevation, and corrected the light
scattering data to a RH of 40% using the RH correction factor in Figure 5.
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Based upon all North American IMPROVE data and a model varying the particle size,
density and index of refraction Molenar (2000) derived haze = 3.8 m2/g with a geometric
standard deviation of g = 1.2 (Table 2). In the San Joaquin Valley Richards et al., (1999) found
haze = 3.7 m2/g (Table 1). Compilation of extinction efficiencies for haze aerosol types in
NAPAP, 1991 are approximately 3 m2/g. Extinction efficiencies of 3 m2/g for the haze aerosol
types were also used by Malm et al., (1994) to reconstruct dry light scattering using the
IMPROVE aerosol data. A number of studies derived extinction efficiencies for fine mass in the
Eastern US, many of which have been compiled in the NAPAP (1991) document. These fine
mass extinction efficiencies range from 3 - 6 m2/g with an average value of 4 m2/g (Table 1).
Few studies have looked at the fine mass and soil extinction efficiencies. Of those studies
compiled in Table 1 and 2 f. soil = 1 - 2 m2/g and c. mass = 0.3 - 0.6 m2/g.
The regression analysis conducted in this study produced result similar to the literature
values for haze and fine soil, haze = 4.2 m2/g, f. soil = 1.27 m2/g (Table 1). However, the coarse
mass extinction efficiency was c. mass = -0.1 m2/g. The small negative value for c. mass may be
due to the fact that coarse mass contributes little the Eastern US light scattering (see below), the
IMPROVE coarse mass suffers from larger errors than the haze and fine soil, and bsp measured
by nephelometers underestimate coarse mode scattering by approximately a factor of 2 (White et
al., 1994).
Based upon the literature review and regression analysis the extinction efficiencies used
to reconstructed the dry particle scattering were:
haze = 4 m2/g
f. soil = 1.25 m2/g
f. soil = 0.6 m2/g
4.2 Evaluation of the Aerosol to Dry Light Scattering Relationship
The ability of the above model to reproduce the dry light scattering data was evaluated by
comparing the reconstructed bsp to the measured bsp. Figure 6, presents scatter plots comparing
the measured and reconstructed bsp for all sites segregated by season. The spatial dependence of
the quality of the aerosol to dry bsp relationship was assessed by examining the regression
statistics for each station (Table 3). Over all, the reconstructed bsp compares favorably with the
measured data with an r2 = 0.75 and root mean square error (RMS) error of 40% (Table 3). This
relationship is seasonally dependent with poorer correspondence during the winter and spring (r2
= 0.57) compared to the summer and fall (r2 = 0.77). The correspondence is deteriorated by a
series of data points where the reconstructed values underestimate the measured data by more
than a factor of 2. These outliers correlate well with r2 = 0.74. However, no pattern pointing to a
cause for their systematic underestimation was found. The outlying data points occur across all
seasons (Figure 6) and location. Also, these data points have typical haze, fine soil, and coarse
mass compositions.
It is interesting that the intercept of the regression lines are ~17 Mm-1 for all stations and
seasons (Figure 6, Table 3). Therefore, when the measured bsp is 0, the reconstructed bsp is
almost 2 times Rayleigh scattering on average. The cause of the intercept is not known.
The reconstructed bsp fit the measured data slightly better in the south than the north. At
the three northern sites, Boundary Waters and Acadia the RMS error is 53 - 67% while in the
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south the RMS error varies between 31 and 45% of the measured data. This pattern is not
evident in r2 values which vary between 0.68-0.76 in the north and 0.63-0.85 elsewhere.
The seasonal relative contributions of the haze, fine soil, and coarse mass to the
reconstructed dry bsp for all Eastern US IMPROVE sites is presented in Table 4. As shown, haze
is the dominate contributor accounting for 80 - 97% of the dry bsp and 93% on average. The fine
soil typically contributed less than 2% during all seasons except in Florida where the summer
where fine soil accounted for ~7% of the dry bsp at Everglades National Park. The coarse mass
contributes from 3-14% of the dry bsp and 6% on average. The largest values also occurring
during the summer in Florida.
The large contribution of haze to the total dry bsp and the fact that fine soil accounts for
only ~5% of the fine mass indicates that the aerosol to dry bsp can be constructed from fine mass
alone. A regression analysis of fine mass and measured bsp estimated a fine mass light scattering
efficiency of 4 m2/g which is inline with literature values (Table 1). The reconstructed dry bsp
using fine mass alone is compared to the measured data in Table 5. The regression statistics and
RMS errors are nearly identical to those in Table 3 which reconstructed the dry bsp using haze,
fine soil, and coarse mass. The largest increase in error occurred at Upper Buffalo, AK where
the RMS error increased by 1.5 Mm-1 and r2 decreased from 0.7 to 0.65. However, at Acadia,
ME, the RMS error decreased 4 Mm-1 using only fine mass to reconstruct the dry bsp.
4.3 Summary
The relationship between aerosol and dry bsp was constructed by assuming that the bsp
was due to three classes of aerosols, haze, fine soil, and coarse mass, and their contribution could
be estimated by multiplying their concentration by their light scattering efficiency. The light
scatter efficiencies used were haze = 4 m2/g, f. soil = 1.25 m2/g, f. soil = 0.6 m2/g which were
derived from literature values and a regression analysis. Using this model, the reconstructed and
measured bsp compared well with r2 = 0.75 and an RMS error of 40%. The dry bsp was
dominated by haze which contributed 80-97% of the total bsp. Due to the dominance of haze and
the fact that haze accounts for 95% of the Eastern US fine mass, the dry bsp was reconstructed
using fine mass only and f. mass = 4 m2/g. The resulting reconstructed bsp fit the measured data
equally as well as the reconstructed bsp using haze, fine soil, and coarse mass.
5.0 Reconciliation of Reconstructed bsp and visibility derived Aerosol bext
The reconciliation of the reconstructed dry bsp from measured aerosol data and the dry
aerosol bext derived from visibility data was conducted by comparing daily estimates of these
values at several locations. The reconstructed dry bsp was calculated by using fine mass and an
extinction efficiency of 4 m2/g. In comparing the daily value it is necessary to have visibility and
aerosol monitors near each other. Only the Washington DC region has both an IMPROVE and
airport monitoring sites in the vicinity of each other. Therefore, PM2.5 from the AIRS network
for St. Louis, MO and from two specialty studies conduced in Philadelphia, PA were drawn
upon. In addition, results from previous studies comparing airport visibility data to PM2.5 were
examined.
The correspondence between the bsp and aerosol bext varied depending on location and
sampling period. During the Saturation Study in Philadelphia, PA from September - October
1994 there was good agreement between the bsp and aerosol bext with r2 = 0.71 (Figure 7).
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However, over a longer time period, 1992 - 95, the bsp to bext correspondence decreased to r2 =
0.32. Similar results were also seen at St. Louis, MO and Washington DC (Figure 7). Ozkaynak
et al., (1985) compared four years of PM2.5 data to visibility derived dry aerosol bext at 12 urban
sites in the US from 1978 - 82 and found r2 values between 0.23 and 0.65 with an average of
0.43 (Table 6). These values are considerably lower than that found when comparing
reconstructed bsp to nephelometer measured bsp which had an average r2 = 0.73 (Table 5).
The poor correspondence for the visibility derived aerosol bext can be expected due to the
fact that the sites are not co-located and the large errors associated with human visual range
estimates (see section 3.1). These errors can be reduced by examining longer term aggregates of
the reconstructed bsp and aerosol bext. Figure 8, presents results from Schichtel et al., 1992 who
compared quarterly averaged aerosol bext to reconstructed bsp over the US. As shown, there is
excellent correspondence with r2 = 0.94 indicating that the aerosol bext can explain 94% of the
spatial and quarterly variance of the reconstructed bsp.
On average, the aerosol bext at St. Louis, MO and Washington DC is nearly twice as large
as the reconstructed bsp. In addition, in all four scatter plots in Figure 7, the intercept of the best
fit line is nearly half the aerosol bext. The slope of the regression lines fitted through 0 range
from 1 to 1.9. These result point to the reconstructed bsp systematically underestimating the
aerosol bext. To remove this bias, the fine mass scalar in the reconstructed bsp would need to
range between 4 and 7.6 m2/g. Ozkaynak et al., (1985) found that the best fit between fine mass
and aerosol bext occurred with a fine mass scalar between 3.3 - 6.1 m2/g and 4.8 m2/g on average
(Table 6). A fine mass scalar greater than 4 m2/g is expected, since the reconstructed bsp does
not account for particle light absorption but the aerosol bext does. Light absorption has been
found to account for 10 - 30% of the light extinction in the Eastern US (Malm et al., 1994).
Accounting for this light absorption, the fine mass scalar would need to be 4.4 - 5.2 m2/g.
As shown in Table 6, the regression coefficients relating aerosol bext to fine mass vary by
a factor of 2. Figure 9 presents the ratio of the average aerosol bext to average fine mass at all
available fine mass monitoring sites with summer data from 1992-95. In this figure, the aerosol
bext to fine mass ratio varies over the Eastern US by a factor of three. There is no clear spatial
pattern to this variability, and this wide range of variation was not found when comparing fine
mass to measured bsp at individual sites. This variability may be a random component imposed
on the ratio by the uncertainties associated with human visibility measurements. The variability
of this relationship with season was not investigated. However, Ozkaynak et al., (1985) examine
variations in the relationship between a cold season (October - March) and a warm season
(April-September) and found "no generalizable, systematic seasonal effect."
5.1 Summary
Reconstructed dry bsp from fine mass and dry aerosol bext derived from visibility data
were reconciled by comparing daily estimates of light extinction at several sites. On average the
daily reconstructed bsp had r2 = 0.43 at a given site. However, comparison of quarterly values
over North America had r2 = 0.94. The reconstructed bsp systematically underestimated the
aerosol bext and regression lines between the bsp and aerosol bext resulted in an intercept equal to
almost half the aerosol bext. The bsp underestimation can be reduced by scaling the fine mass by
5 m2/g as opposed to 4 m2/g. The increase in the scaling factor can be justified on the basis that
it accounts for the 10-30 % of light extinction due to particle absorption. However, the ratio of
aerosol bext to fine mass was found to vary by a factor of three with space.
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6.0 Discussion
The primary questions to be addressed by this analysis are what is the relationship
between measures of light extinction and particulate matter, and can these measure of light
extinction be used to estimate the fine mass concentrations in the Eastern US. Using the
IMPROVE aerosol and light scattering data it was shown that the bsp could be related to the fine
particle matter via a scattering efficiency of 4 m2/g. This relationship was as good as a model
based on haze, fine soil and coarse mass. Using the simple model, the bsp can explain 73% of the
spatial and daily variation in fine particulate mass concentrations. Therefore, the bsp is a good
surrogate for the Eastern US fine mass and can be used to quantitatively estimate daily fine mass
concentrations.
There was a poorer correspondence between the visibility derived dry aerosol bext and
reconstructed bsp from fine mass. Comparisons of daily data at individual sites had r2 values
from 0.23 - 0.7 and an average of 0.4. In addition, the aerosol bext to fine mass ratio was found to
vary substantially with space. Therefore, the aerosol bext is inadequate to quantitatively
reproduce the high spatial and temporal variability of daily fine mass. The use of the visibility
data as a surrogate for PM at high time resolutions should be restricted to aiding the spatial and
temporal interpolation of fine particle data. However, comparison of quarterly average aerosol
bext and reconstructed bsp had r2 = 0.94, so that the visibility is well suited to estimating the
seasonal spatial and temporal variability in fine mass.
References
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13
Table 1. Literature review of dry scattering efficiencies coefficients derived from ambient monitoring data for the Eastern US and
North America.
Measured Data
Eastern North America
Location
Type
Fine
Mass
Haze
4
4.2
Eastern US
IMPROVE
Egbert, Ontario
Lenox, MA
Lewes, DE
Lewes, DE
Luray, VA
Abbeville, LA
rural
rural
rural
rural
rural
rural
3.2
5.75
4.76
3.67
5.00
3.93
East Coast
New York
Detroit, MI
Raleigh, NC
Houston, TX
Houston, TX
urban
urban
urban
urban
urban
3.3
3.69
3.0-6.0
3.14
4.1
rural
Extinction Efficiency (m2/g)
Sulfate Organic Elemental
Carbon
Nitrate Fine Soil Coarse Reference
Mass
Regression analysis
1.27
-0.1
performed in this study
Hoff et al., 1996
NAPAP, 1991 page 24-90
NAPAP, 1991 page 24-90
NAPAP, 1991 page 24-90
NAPAP, 1991 page 24-90
2.2-3.2
North America
North America
San Joaquin Valley
IMPROVE Recon Light
Extinction Efficiencies.
Hegg et al., 1995
Trier and Horvath, 1993
NAPAP, 1991 page 24-90
Conner et al., 1991
NAPAP, 1991 page 24-90
Trier and Horvath, 1993
2.5
3.75
10.5
2.5
1.25
0.6
3
3
10
3
1
0.6
3.7
NAPAP, 1991 page 24-90
Richards et al.,1999
Malm et al., 1994
14
Table 2. Literature review of dry scattering efficiencies coefficients derived from light scattering models and assumed or measured
composition of aerosol for the Eastern US and North America.
Modeled Data
Extinction Efficiency (m2/g)
Eastern North America
Location
Type
Eastern US
Eastern US
urban
rural
North America
North America
IMPROVE
rural
Fine
Mass
3.8
3.4
3.7
Sg=1.2
Haze
Sulfate
Organic Elemental
Carbon
Nitrate Fine Soil Coarse Reference
Mass
0.32 Ozkaynak et al., 1985
0.32 Ozkaynak et al., 1985
3.8
Sg=1.2
2
Molenar, 2000
Table 3. Comparison of the reconstructed to measured dry light scattering for each station. The dry light scattering was reconstructed
using haze, fine soil, and coarse mass. All available data from 1993-98 was used.
Location
Boundary Waters, MN
Acadia, ME
Great Gulf Wilderness,
NH (Summer data only)
Dolly Sods, WV
Shenandoah, VA
Mammoth Cave, KT
Upper Buffalo
Wilderness, AK
Great Smoky Mnts, TN
Okefenokee, FL
All Locations
Slope
r2
RMS
Error
0.50
0.74
0.68
0.76
19.8
15.9
66
54
29.8
29.6
29.7
36.7
7.1
1.18
0.85
15.1
49
31.0
43.9
17.1
13.7
16.4
0.63
0.79
0.78
0.63
0.89
0.69
22.0
13.0
17.4
43
31
38
50.6
42.0
45.6
49.2
46.9
51.9
19.3
0.58
0.70
21.5
45
48.0
47.1
16.7
21.3
17.0
0.76
0.64
0.69
0.84
0.75
0.75
17.7
15.9
18.3
33
36
41
54.2
44.4
44.6
58.1
49.8
48.0
Intercept
Mm-1
14.9
14.8
Normalized
Avg.
Avg.
RMS Error % Meas. bsp Recon. bsp
15
Table 4. Contribution of haze, fine soil and coarse mass to the dry light scattering contribution for all IMPROVE monitoring sites in
the Eastern US using data from 1988 - 1998.
Annual
Winter
Spring
Summer
Fall
Location
Upper Buffalo, AK
Voyageurs, MN
Boundary Waters, MN
Isle Royale, MI
Sipsy Wilderness, AL
Mammoth Cave, KT
Great Smoky Mnts, TN
Chassahowitzka, FL
Okefenokee, FL
Everglades, FL
Cape Romain, SC
Jefferson, VA
Dolly Sods, WV
Shenandoah, VA
Washington DC
Brigantine, NJ
Lye Brook, VT
Great Gulf, NH
Acadia, ME
Moosehorn, ME
Haze F Soil C Mass Haze F Soil C Mass Haze F Soil C Mass
0.93 0.02
0.05
0.94 0.01
0.05
0.93 0.02
0.06
0.90 0.01
0.09
0.94 0.01
0.05
0.92 0.02
0.06
0.93 0.01
0.06
0.94 0.01
0.05
0.92 0.02
0.06
0.92 0.01
0.07
0.92 0.01
0.07
0.90 0.01
0.08
0.95 0.01
0.04
0.96 0.01
0.04
0.95 0.01
0.04
0.96 0.01
0.03
0.96 0.01
0.03
0.96 0.01
0.03
0.95 0.01
0.04
0.94 0.01
0.05
0.94 0.01
0.04
0.90 0.02
0.08
0.92 0.01
0.07
0.91 0.01
0.08
0.92 0.02
0.06
0.93 0.01
0.06
0.93 0.01
0.06
0.87 0.03
0.10
0.89 0.01
0.10
0.90 0.02
0.08
0.92 0.01
0.07
0.92 0.01
0.07
0.92 0.01
0.07
0.96 0.01
0.03
0.97 0.01
0.03
0.95 0.01
0.04
0.96 0.01
0.03
0.96 0.01
0.04
0.95 0.01
0.04
0.95 0.01
0.04
0.95 0.01
0.05
0.94 0.01
0.04
0.95 0.01
0.04
0.95 0.01
0.04
0.95 0.01
0.04
0.90 0.01
0.09
0.91 0.01
0.08
0.87 0.01
0.12
0.95 0.01
0.04
0.95 0.01
0.04
0.94 0.01
0.04
0.93 0.01
0.06
0.89 0.02
0.09
0.94 0.01
0.06
0.94 0.01
0.06
0.92 0.01
0.07
0.93 0.01
0.06
0.93 0.01
0.06
0.91 0.01
0.08
Haze F Soil C Mass Haze F Soil C Mass
0.91 0.04
0.05
0.93 0.01 0.05
0.88 0.01
0.11
0.88 0.01 0.11
0.94 0.01
0.05
0.92 0.01 0.07
0.92 0.01
0.07
0.90 0.01 0.09
0.94 0.02
0.04
0.96 0.01 0.03
0.95 0.01
0.03
0.96 0.01 0.03
0.95 0.01
0.04
0.95 0.01 0.04
0.86 0.06
0.08
0.91 0.01 0.08
0.88 0.04
0.07
0.93 0.01 0.06
0.79 0.07
0.14
0.89 0.01 0.09
0.91 0.02
0.07
0.92 0.01 0.07
0.97 0.01
0.02
0.96 0.01 0.03
0.97 0.01
0.02
0.96 0.01 0.03
0.97 0.01
0.03
0.95 0.01 0.04
0.96 0.01
0.03
0.95 0.01 0.04
0.92 0.01
0.07
0.91 0.01 0.08
0.97 0.01
0.03
0.95 0.01 0.04
0.94 0.01
0.05
0.91 0.01 0.08
0.95 0.01
0.05
0.93 0.01 0.07
0.95 0.01
0.04
0.93 0.01 0.07
Average
Min
Max
0.93
0.87
0.96
0.93
0.79
0.97
0.01
0.01
0.03
0.06
0.03
0.10
0.94
0.89
0.97
0.01
0.01
0.01
0.06
0.03
0.10
0.93
0.87
0.96
0.01
0.01
0.02
0.06
0.03
0.12
0.02
0.01
0.07
0.05
0.02
0.14
0.93
0.88
0.96
0.01
0.01
0.01
0.06
0.03
0.11
16
Table 5. Comparison of the reconstructed to measured dry light scattering for each station. The
dry light scattering was reconstructed using PM2.5 only. All available data from 1993-98 was
used.
Location
Boundary Waters, MN
Acadia, ME
Great Gulf Wilderness,
NH (Summer data only)
Dolly Sods, WV
Shenandoah, VA
Mammoth Cave, KT
Upper Buffalo
Wilderness, AK
Great Smoky Mnts, TN
Okefenokee, FL
All Locations
Intercept
Mm-1
14.7
12.4
Slope
r2
0.47
0.73
0.65
0.76
RMS
Error
20.6
14.7
Normalized
Avg.
Avg.
RMS Error % Meas. bsp Recon. bsp
69
29.8
29.7
50
29.6
36.7
6.4
1.11
0.85
12.3
40
31.0
43.9
15.3
12.4
15.1
0.64
0.79
0.78
0.64
0.89
0.69
22.0
12.6
17.1
43
30
37
50.6
42.0
45.6
49.2
46.9
51.9
18.2
0.58
0.65
22.9
48
48.0
47.1
14.6
18.9
15.2
0.77
0.64
0.69
0.84
0.69
0.73
17.2
16.5
18.4
32
37
41
54.2
44.4
44.6
58.1
49.8
48.0
Table 6. Fine mass extinction efficiencies derived from regressing visibility derived dry aerosol
bext and PM2.5 mass at twelve cities with data from 1978-82. This table came from Ozkaynak et
al., 1985. The visibility data came from the US Hourly Surface Airways and the PM2.5 data
were from. The reported R2 values are for models with an intercept.
 f. Mass
New York, NY
Buffalo, NY
Washington, DC
Baltimore, MD
Philadelphia, PA
Pittsburgh, PA
Minneapolis/St. Paul, MN
St. Louis, MO/IL
Kansas City, KS/MO
Dallas/Ft. Worth, TZX
San Francisco, CA
Los Angeles, CA
Average
(m2/g)
Error
r2
6.1
3.6
3.3
4.0
4.2
4.7
5.1
5.8
5.7
5.2
5.4
4.8
4.8
0.24
0.15
0.15
0.19
0.10
0.19
0.29
0.34
0.29
0.19
0.29
0.19
0.24
0.65
0.23
0.50
0.32
0.55
0.53
0.34
0.49
0.31
0.38
0.41
0.47
0.42
17
Figure 1.Schematic diagram showing the flow of information and intermediate relationship necessary to relate airport visibility data to
aerosol mass.
18
Figure 2. Monitoring sites, time period and variables in the Hourly Surface Airways database.
Figure 3. Monitoring sites, time period and variables in the IMPROVE aerosol database.
19
Figure 4. Monitoring sites, time period and variables in the IMPROVE Nephelometer database.
Bxt(RH) / Bxt (RH=40%)
4.0
Aerosol bext Relative Humidity
Correction Factor
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
20
30
40
50
60
70
80
90
100
Relative Humidity, %
Figure 5. The relative humidity correction factor used to correct the aerosol b ext to a standard relative humidity of
40%. Appendix B discusses the derivation of this RH correction curve.
20
Reconstructed bsp
Reconstructed bsp
y = 0.70x + 17
R2 = 0.75
250
Mean X = 45
Mean Y = 48
200
Winter
300
150
100
50
0
y = 0.52x + 17
R2 = 0.54
250
Mean X = 34
Mean Y = 34
200
150
100
50
0
0
100
200
300
0
200
Mean X = 65
Mean Y = 70
300
Reconstructed bsp
250
y = 0.70x + 24
R2 = 0.81
100
200
300
Measured bsp
Summer
300
150
100
50
0
250
250
Spring
y = 0.63x + 20
R2 = 0.61
Mean X = 39
Mean Y = 45
200
150
100
50
0
Measured bsp
Reconstructed bsp
300
Reconstructed bsp
Annual
300
0
100
200
300
Measured bsp
Fall
y = 0.70x + 16
R2 = 0.74
Mean X = 39
Mean Y = 44
200
150
100
50
0
0
100
200
Measured bsp
300
0
100
200
300
Measured bsp
Figure 6. Comparison of the reconstructed dry light scattering to measure data at all IMPROVE nephelometer monitoring sites from
1993-98. The bsp was reconstructed from haze, fine soil, and coarse mass aerosol concentration.
21
y = 1.5x
R2 = -0.01
0.4
y = 0.81x + 0.08
R2 = 0.71
0.3
Mean X = 0.08
Mean Y = 0.15
0.2
0.1
Philadelphia, PA 5/92 - 4/95
0.5
Aerosol bext (km-1)
Aerosol bext (km-1)
0.5
Philadelphia Saturation Study NonIndustrial Sites Sept. - Oct. 1994
0
y = 1.34x
R2 = -0.12
0.4
0.3
y = 0.66x + 0.06
R2 = 0.32
0.2
Mean X = 0.07
Mean Y = 0.11
0.1
0
0.0
0.1
0.2
0.3
0.4
0.5
0
Reconstructed bsp (km-1)
0.2
0.3
0.4
0.5
Washington DC 9/85 - 6/96
St. Louis, MO 5/85 - 6/96
0.5
0.4
y = 1.9x
R2 = 0.1
0.3
y = 1.07x + 0.06
R2 = 0.37
0.2
Mean X = 0.06
Mean Y = 0.12
0.1
Aerosol bext (km-1)
0.5
Aerosol bext (km-1)
0.1
Reconstructed bsp(km-1)
y = 1.06x
R2 = 0.24
0.4
y = 0.70x + 0.03
R2 = 0.35
0.3
Mean X = 0.07
Mean Y = 0.08
0.2
0.1
0
0
0
0.1
0.2
0.3
0.4
Reconstructed bsp (km-1)
0.5
0
0.1
0.2
0.3
0.4
0.5
Reconstructed bsp (km-1)
Figure 7. Comparison of reconstructed bsp to visibility derived aerosol bext. The reconstructed
bsp is equal to four time the fine mass concentrations. The St. Louis, MO PM2.5 data came from
EPA's AIRS database, the Philadelphia data came from a specialty study which collected daily
data at Presbyterian Home from 5/92 - 4/95. The Philadelphia Aerosol Saturation Monitoring
Study collected data from September-October, 1994 at 15 sites throughout Philadelphia. Eleven
non industrial sites were used in the analysis (Husar et al., 1997). The Washington DC PM2.5
data came from the IMPROVE monitoring network.
22
Haze scattering efficiencies from Schichtel et al.,
1992. The original analysis used a Koshmieder
coefficient of 3. These values were scaled to a
Koshmieder coefficient of 1.9.
Northeast
Southeast
Northwest
Southwest
Cold
6.3
10.7
9.2
3.8
Warm
5.4
7.3
8.2
2.5
Figure 8. Comparison between seasonal visibility derived aerosol bext and reconstructed aerosol bext over the US from Schichtel et al.,
(1992). The aerosol bext was calculated using a similar relationship as Equation 3, with aerosol bext = haze * chaze + dust * cdust, where
dust is the sum of fine soil and coarse mass. dust was set equal to 0.7 m2/g and haze was determined via a fitting process where it
varied with season and space. Aerosol data from the National Park Service - National Fine Particle Network (NPS - NFPN) Stacked
Filter Unit Network (Eldred et al., 1986) from 1982-86, and data from the NESCAUM network from 1988-90 were used in the
analysis.
23
Figure 9. The ratio of the mean visibility derived dry aerosol bext to fine mass at all IMPROVE,
AIRS and CASTNet aerosol monitoring sites in the Eastern US during the Summer (June, July,
August) 1992-95 (Falke et al., 2000).
24
Appendix A. Hourly Surface Airways Location Table.
25
Appendix B Derivation of Visibility Data RH Correction Factor
Sulfates, nitrates and some organics are hygroscopic. As these particles are humidified
they uptake water increasing their particle size which results in more effective light scattering
(bsp) per unit mass aerosol. The particle growth and changes in humidity of pure hygroscopic
species are well understood and have been modeled theoretically (Hanel, 1976, Tang et al., 1981,
Shettle and Fenn, 1979. The humidification of dry aerosols, such as sulfate, results in no particle
growth until the deliquesce point is reach, ~ 80% RH, at which point the particle size and bsp
increase exponentially. This results in a sharp discontinuity in the particle growth curve.
However, when the wet aerosol is dried it can retain water past the deliquesce point, i.e. the
hysteresis effect (Winkler and Junge 1992, Tang et al., 1981).
In the atmosphere particles are composed of complex mixtures of species. The make up
of these particles can dramatically change the particle growth curve from that of a pure species.
Consequently, the associated bsp growth curves change as well. For example, mixtures of
ammonium nitrate and ammonium sulfate aerosol have been shown to be hygroscopic below
their respective deliquescence point for the pure species (Sloane, 1984, 1986). In addition,
Saxena et al., (1995) found that the presence of organics appeared to either increase or decrease
the hygroscopicity of inorganic species. Due to the difficulty in modeling the hygroscopicity of
mixed particles and not knowing whether the ascending or descending limb of the hysteresis
curve applies for a particular aerosol sample empirical and semi-empirical particle growth curves
are often used (Zhang et al., 1993; Sloane 1986; Malm and Kreidenweis, 1997; Husar and
Holloway, 1984).
In this analysis, the relationship between bsp and relative humidity is empirically derived
using summertime (June, July, August) measured light scattering from the IMPROVE
nephelometers network (Figure B-1) and light extinction data derived from airport visibility data
(Table B-1). In addition, the dependence of the relationship on increasing and decrease relative
humidity and different airmasses is also investigated.
Method
Relative humidity (RH) follows a pronounced diurnal cycle with summertime Eastern US
RH ~90% in the morning and evenings and ~60% in the mid afternoon (Figure B-2). A natural
experiment is conducted over the course of each day with the aerosol being humidified from the
afternoon to the morning and dried out from the morning to afternoon. Assuming that the
aerosol composition and concentration do not change, then the change in the bsp over the course
of the day is due to the changes in RH. Therefore, it should be possible to derive the light
scattering to RH dependence from these daily bsp and RH fluctuations.
In this work, the bsp to RH dependence or humidograms are derived for each monitoring
site and day by normalizing the hourly bsp data by the bsp value that occurs at 70% RH at some
time during the day. To examine changes in the bsp to RH relationship for increasing and
decreasing RH curves, the analysis was conducted for both the ascending, hours 15 to 6, and
descending RH curve, hours 6 to 15. A different normalizing bsp value at 70% RH was used for
each limb of the RH curve.
26
Figure B-3 presents the normalized hourly bsp values for Mammoth Cave, KT for the
ascending RH curve. As shown, there is a tight scatter of the hourly values about the mean at
RH below 80% with a coefficient of variation ~20%. Above 80% RH there is increased scatter
with a coefficient of variation of 36% at 90% RH. The values where the hourly bsp ratios are
much greater than average, e.g. bsp (80%)/ bsp (70%) = 4 are often associated with drops in dew
point temperature of over 3 oc in the course of the day. This is most likely due to changing
airmasses, thus the assumption of a constant aerosol composition and concentration is violated.
Also, the low bsp ratios above 90% are often associated with precipitation. Again this violates
the assumption of constant aerosol composition and concentration. No attempts were made to
filter out these values. However, note that all light scattering and extinction data associated with
RH above 90% are generally filtered out from any analysis since these data are generally
affected by fog and precipitations.
The humidograms are dependent on the ambient aerosol composition. This was clearly
shown by Charlson et al., (1984) who identified differences in humidograms for airmasses
dominated by sulfate, urban/photochemical smog, marine and background continental aerosol
types. In order to account for changes in the aerosol composition the data and humidograms
were further segregated based upon the dew point temperature. Dew point temperature is a
slowly changing property of an airmass and is an effective tracer of an airmasses history. For
example, in the Eastern US, low dew point temperatures are typically associated with airmass
transport from Canada while high dew point temperatures are associated with stagnant airmass
transport and transport from the south (Figure B-4). The dependence of the humidogram on dew
point was derived by creating humidograms for low humidity, Dew Pt. < 15 oc, mid humidity 15
oc < Dew Pt. < 20 oc and high humidity, Dew Pt. > 20 oc. It is understood that changes in dew
point temperature are not necessarily related to changes in aerosol composition. However, this
was the best available airmass tracer that is routinely measured with the IMPROVE
nephelometer and airport visibility data.
Results
The exploration of the humidograms and their dependence on the ascending and
descending RH cures and dew point temperature was conducted using the high resolution
IMPROVE nephelometer data. These humidograms averaged over all Eastern US sites are
presented in Figures B-5 and B-6. As shown, the humidograms are smooth with no
discontinuities over the entire RH range. In addition, there is virtually no difference between the
humidograms for the ascending and descending RH curves at any of the three dew point
temperatures (Figures B-5). The same results were found when the data were examined for each
station independently. Therefore, there is no indication of the aerosol suddenly going to solution
or re-crystallizing, as the aerosol is humidified or dried respectively. The lack of discontinuities
may be due to the aerosol never fully drying out. The morning and evening RH's typically
exceed 90% which is past the deliquescence point for most aerosol, however, the afternoon RH's
typically do not fall below 50% (Figure B-2 and B-3) which is not low enough to "dry out" the
aerosol (Tang et al., 1981). In addition, the light scattering RH growth factor for ambient
aerosols have previously been observed to be rather smooth (Wexler and Seinfeld, 1991).
The humidograms are also insensitive to changes in the dew point temperature (Figure B6). It is not known if this lack of dependence is due to the dew point temperature being a poor
27
indicator for changing airmass composition or the humidograms are insensitive to the variable
composition of the aerosol at each receptor.
The spatial variation of the humidograms are explored in Figure B-7 which plots the
average summertime bsp(RH) /bsp (70%) ratio for each location at RH = 50% and RH = 90%. As
shown, there is a weak spatial dependence of bsp on RH. At 90% RH, bsp increases by a factor of
1.8 in the north, compared to 1.6 in the south from Kentucky to Florida. However, at Upper
Buffalo Wilderness, AK the bsp increased by a factor of 2, similar to that in the north. The
spatial pattern of the bsp ratios at RH = 50% is similar to the pattern for RH =90%. In the
northern sites have ratios of ~0.85 while in the central region from Arkansas to Virginia the
ratios were ~ 0.7.
Due to the Eastern US humidogram's weak spatial dependence and lack of dependence on
dew point temperature and the ascending and descending RH curves, a single humidogram was
derived from all data in the Eastern US. A single humidogram was also derived from the four
airport visibility sites in Table B-1. These humidograms are presented in Figure B-8 and Table
B-2. As shown, below 70% relative humidity there is virtually no difference between these two
humidograms with the bsp ratios increasing from 0.8 at 40% RH to 1 at 70% RH. Above 70%,
the nephelometer humidogram estimates bsp ratios ~ 10% greater than the bext ratios with bsp
ratios at 90% RH of 1.9 and 1.7 respectively.
The Eastern US humidograms derived from the nephelometer and visibility data are
compared to results from other studies in Figure B-8 and Table B-2. These include the
humidogram Husar and Holloway (1984) derived from airport visibility data using a similar
technique used in this study, the humidogram from Malm et al., (1994) derived by smoothing the
sulfate aerosol humidograms from Tang et al., (1984) and the humidogram from Malm and
Kreidenweis (1997) which used an aerosol growth model based on the Zdanovskii-StokesRobinson assumptions and particles composed of the equal portions of sulfate and organics. The
humidograms derived in this study fall between those found in the other studies.
Conclusions
The dependence of light scattering on relative humidity was found to be independent of
the dew point temperature and the ascending and descending portions of the relative humidity
diurnal cycle. In addition, only a week spatial dependence was observed with the bsp(90%) /
bsp(70%) ratio approximately 10% larger in the north than the south. However, the bsp ratio in
Arkansas was similar to those in the North. The weak or lack of dependence of the light
scattering humidograms on any of these variables implies that a single humidogram can be
derived that is applicable to the entire Eastern US. Note, this analysis used only summertime
data only, so the applicability of this humidogram to other seasons has not been determined.
The final humidogram derived from all Eastern US data using the nephelometer showed
nearly linear increase of bsp with increasing RH with the bsp ratios increasing from 0.7 at 30%
RH to 1.1 at 75% RH. Above 75% RH the ratios increased rapidly to 1.9 at 90% RH. The
nephelometer derived humidogram was similar to that derived from the airport visibility data,
however, at high RH the airport visibility derived bxt ratios were about 10% lower.
28
Table B-1. The Eastern US surface meteorological observation sites used to derive the
bsp to RH relationship. These sites were selected for their large number of visibility markers,
>12, and distant visibility thresholds, > 30 km
Meteorological Surface Observation Sites
Monitoring Site
Time Period
Kalamazoo, MI
1994-97
Binghamton, NY
1994-95
Cincinnati, OH
1994-97
Crossville, TN
1994-97
Table B-2. The dependence of light scattering and extinction coefficient on relative humidity
derived from this study and others.
Bsp/Bsp70
RH
0
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
This Study
0.71
0.70
0.70
0.78
0.78
0.82
0.88
0.92
0.95
1.01
1.11
1.27
1.56
1.90
3.19
Malm et al.,
1994
0.52
0.52
0.52
0.52
0.52
0.52
0.52
0.58
0.61
0.64
0.68
0.75
0.86
1.00
1.14
1.43
1.76
2.44
5.12
Bxt/Bxt70
Malm &
Kreidenweis, This Study
1997
0.77
0.79
0.82
0.86
0.90
0.95
1.00
1.08
1.15
1.30
1.59
0.73
0.71
0.76
0.79
0.82
0.86
0.89
0.92
0.96
1.00
1.06
1.16
1.34
1.70
2.79
Husar &
Holloway,
1984
0.89
0.81
0.81
0.81
0.81
0.81
0.83
0.86
0.88
0.90
0.92
0.95
0.97
1.00
1.14
1.33
1.57
2.00
2.86
29
Figure B-1. Nephelometer light scattering data from the IMPROVE network during the summer
time, June, July and August, from 1993 – 98
Figure B-2. The average relative humidity diurnal cycle over the Eastern US during the summer,
June, July, August, from 1994-96.
30
Mammoth Cave, KT Humidogram
8
Bsp(RH) / Bsp(RH=70)
7
6
5
One Hour Values
4
3
Average
2
1
0
0
20
40
60
Relative Humidity, %
80
100
Figure B-3. Hourly summertime data from the ascending RH curve, hours 15 –6, at Mammoth
Cave, KT
31
Figure B-4. Back trajectories and associated dew point temperature for the Eastern US.
32
Figure B-5. The dependence of the summertime humidograms on dew point temperature. These
humidograms were averaged over all Eastern US IMPROVE monitoring sites.
Figure B-6. The dependence of the summertime humidograms on the ascending and descending
parts of the relative humidity curve. These humidograms were averaged over all Eastern US
IMPROVE monitoring sites.
33
Figure B-7. The average daily bsp(RH) / bsp(RH=70%) ratio at RH = 40% and RH = 90% for
each Eastern US IMPROVE monitoring sites.
Humidogram
4
Bsp(RH) / Bsp (RH=70)
Bxt(RH) / Bxt (RH=70)
3.5
3
2.5
2
1.5
1
0.5
0
20
30
40
50
60
70
80
90
100
Relative Humidity, %
Bsp/Bsp70 (This Study)
Malm et al., 1994
Malm & Kreidenweis, 1997
Bxt/Bxt70 (This Study)
Husar & Holloway, 1984
Figure B-8. The dependence of light scattering and extinction coefficient on relative humidity
derived from this study and others.
34