Hi David,
I am returning to the conversation we had last week at Lake Arrowhead regarding possible access to the raw
ASOS data through NCDC. As you know, in the standard weather database, the ASOS visibility data are
truncated to a few quantized values. Also the max visibility reported is 10 miles. (Imagine the Weather Service
starts reporting the temperature in 5 deg increments and anything over 25C would be lumped to 25+). Some
of the issues are listed here:
http://capita.wustl.edu/CAPITA/CapitaReports/USVisiTrend/ASOSDataTruncation/ASOSDataTruncation.htm
As a result of these changes, since 1996 we cannot continue using the visibility data (as aerosol surrogate)
for trend analysis nor for the effective analysis of haze transport/dynamics. For this reason, we are now
seeking routine access to the full-resolution ASOS data, so that we can continue harnessing the visibility data
as we have done for 25 years (partly supported by NCDC).
The on-line visibility/haze-aerosol data would be useful for many applications. For example, Dr. Stefan Falke
at EPA, Office of Environmental Information has a modest project "WebVis - Web-based Visibility Information
System" to deliver visibility data to the public. His draft white paper is here.
http://capita.wustl.edu/webvis/WebVIS.htm. The project includes several key federal/state and industrial
partners interested in visibility. For the next two years, we at CAPITA are the beneficiaries of some EPA
support form his project to filter/QA-QC and process the data into aerosol extinction coefficient. Jim Meagher
at NOAA Aeronomy Laboratory is also interested in using visibility as an aerosol surrogate. The State of
Texas and other states need the data for aerosol tracking. There are number of aerosol-related field projects
all over the country that could benefit from current haze/aerosol maps.
On-line access to the full-resolution ASOS data gathered at NCDC would probably solve our problem and
also provide the advantages of the more robust and frequent instrumental data compared to the human
observer data. Could the ASOS data be stored, say for a day on an FTP site for pickup? Somewhat like the
METAR data and many satellite data?
You may also consider 'partnering' in the WebVis project since the cleaned visibility/aerosol extinction data
may be of use for climate change research. I for one would be delighted to work with you on examining the
weather/climate-aerosol relationship over N America or at other interesting aerosol locations like India and
China.
Drs. Falke, Meagher and others could possibly drum up some resources to compensate for your extra
expenses.
Looking forward hearing from you on this topic and when would be a good time to talk on the phone.
Best regards,
Rudy
Rudolf B. Husar,
Professor of Mechanical Engineering and Director,
Center for Air Pollution Impact and Trend Analysis (CAPITA), Campus Box 1124,
Washington University, St. Louis , MO 63130-4899.
Phone: (314) 935-6099 Fax: (314) 935-6145, e-mail: rhusar@me.wustl.edu
http://capita.wustl.edu/CAPITA/People/RHusar/rhusar.html
In order to derive the aerosol extinction, the raw visibility data from the instrument (and/or observer) needs to
filtered and corrected for a number of factors. That part of the processing and extra QA/QC we intend to do
here, at least until it becomes routine. The processed current and historical visibility data would pe posted on
the CAPITA website. The results of our processing is
- total extinction coefficient due to weather and aerosol
- aerosol extinction coefficient at ambient humidity
- aerosol extinction coefficient normalized to 60% RH
- spatially interpolated daily (possibly X-hourly) maps of the above 3 extinction parameters
2
3
ASOS Visibility Data Evaluation and Analysis
Background
Over the past decades visibility data have been used to document the spatial and temporal pattern of
atmospheric haziness over the US and also as a surrogate for the pattern and trends of fine particle
mass. Starting in 1994-6, NOAA National Weather Service (NWS) in cooperation with FAA has
been replacing human observers of visual range with automated light scattering instruments that can
automatically monitor the haziness with high dynamic range, accuracy, and precision. The
instrumental visibility data are part of the Automated Surface Observing System, ASOS. In 2001,
the ASOS system consists of over 860 sites distributed rather uniformly over the country as seen in
the station location map (Fig. 1). The network is operated jointly by FAA (535 sites), NWS (315)
with participation from DOD.
4
Fig 1. Location of ASOS monitoring stations over the US. In 2000, over 800 stations were
distributed uniformly over the US.
The ASOS sensor measures the extinction coefficient as one-minute averages. The visibility sensors
in the new Automated Surface Observing Systems (ASOS),
(http://www.nws.noaa.gov/asos/vsby.htm) is a Belfort Model 6220 forward scatter instrument that
illuminates the ambient air by a Xenon flash lamp and detects the forward scattering from dust,
smoke, haze, as well as from rain, fog, and snow particles. The instrument is an accurate and
reliable way of monitoring light scattering due to the variety of particles. The forward scattering
signal can be meaningfully converted into visibility, i.e. the distance to which objects are
discernable. The ASOS package also monitors, temperature, dew point, precipitation, and etc.,
which allows the separation of fog, rain, and snow from dust, smoke, and haze. Hence, it is also
possible to relate the Sensor Equivalent Visibility (SEV) reported by ASOS to the in-situ
concentrations of fine particle dust, smoke, and haze.
5
Figure 2. Automated Surface Observing Systems (ASOS), and the Belfort Model 6220 forward
scatter instrument
According to the manufacturer, Belfort Instruments (http://www.belfort-inst.com) the specifications
of the instrument include a visibility range from 17 ft to 30 miles, with and accuracy of +/- 10% of
the light scattering signal. This range and sensitivity makes the instrument a suitable augmentation
of the filter-based 24-hr average fine particle monitoring system now being implemented by EPA in
response to the new PM2.5 standard. A unique contribution of the ASOS system is that it monitors
the ambient aerosol concentration in-situ, i.e. in the state as particles are inhaled rather than on filters
following equilibration to standard laboratory conditions. The other unique feature is the fast, oneminute time response.
At ASOS-equipped airports pilots can examine the visibility pattern using the full resolution of the
Belfort scattering instrument (up to 30 miles visual range in 1 to 10-minute resolution. The oneminute resolution, ASOS forward light scattering data are stored on the on-site computer for 12
hours.
Unfortunately, the ASOS visibility data are not archived at this high resolution. Rather, the visibility
values are quantized. The 18 distinct visual range bins are: <1/4, 1/4, 1/2, 3/4, 1 1/4, 1 1/2, 1 3/4, 2,
2 1/2, 2, 4, 5, 6, 7, 8, 9,10+. The number of visibility bins is marginally sufficient for most air
quality applications. However, the main limitation of the archived data arises from the fact that the
ASOS visibility values are truncated at 10 miles by declaring that the last bin contains all the
occurrences above the 10 mile visual range. The instrument can provide meaningful data to 20-30
miles visual range. The average daytime visibility over the Eastern US is well above >10 miles.
Hence, the archived ASOS data can only reveal the spatial and temporal pattern during the hazy
episodes. This limits the detailed pattern and trend analysis of regional haze.
6
ASOS Data Usages and Data Evaluation.
The primary purpose of the ASOS data is to provide the necessary visual range information aviation
safety. For that purpose, the truncated visibility data are evidently appropriate. However, the ASOS
light scattering data many other benefits:
 It can document hazardous road conditions during smoke, dust and intense haze
 It can be used to evaluate aerosol forecast models, including data assimilation
 ASOS data can support research on PM sources and transport
 The ASOS system is already in place.

The performance and the characteristics of the ASOS sensors were tested by the operational
Agencies (FAA, NWS) but the results of such test are not available to us. Richards et al., (1996)
have demonstrated that the full resolution ASOS visibility sensor along with the other monitored
meteorological parameters can be used to derive fine particle mass concentrations during nonprecipitating periods and relative humidity less than 80%.
The data of Richards et al., 1996 indicate that the ASOS sensor can provide meaningful data to
visibility of 20-30 miles. If the full resolution data were available the ASOS data could be used to
document the fine particle pattern during both clean and hazy periods over the eastern US. The
ASOS sensor does not seem suitable for visibility/fine particle monitoring over the pristine
Southwest with average visibility well in excess of 30 miles.
Bradley and Lewis (1998) and Ramsey (2000) have performed a limited comparisons of the ASOS
sensor to the previously used human observer approach. The data indicate general consistency of the
two methods but also show significant deviations particularly at very low visibilities. This suggests
combining the old human observer data with that ASOS data for long-term trend analysis requires a
detailed calibration study.
Proposed Approach
The proposed work consists of two major tasks:
1. Evaluation of ASOS Visibility Data as a PM Surrogate
2. ASOS Data Analysis over the Southeastern US: Summer 2000 Huston Study
Task 1: Evaluation of ASOS Visibility Data as a PM Surrogate
Weather Filters and Humidity Correction
The ASOS data undergo extensive automated quality control by the Weather Service. However,
many interferences, largely due to weather makes the raw ASOS data unsuitable as a surrogate for
PM2.5. For this reason, additional filters need to be applied to the ASOS data.
The weather filter eliminated visibility records when the obstruction to vision could be attributed
to weather, i.e. hydrometeors associated meteorological phenomena. Records that contain flags for
rain, fog, or precipitation need to be eliminated. Furthermore, the daily record when the difference
between temperature and dew point was <2.2 C were also eliminated. This temperature spread
corresponds to about 90% relative humidity. Finally, an “ice fog” and “blowing snow” filter need to
be applied that eliminates extreme cold and windy conditions (temperature <-29 C and wind speed
>16 km/hr).
7
Our past experience is with the weather-filters developed for the human-gathered weather data. As
part of this work, the weather filters will be re-examined and adopted for the automated ASOS data.
Aggregation into Hourly and Daily Averages
The ASOS monitoring data are transmitted at one-minute resolution. For some applications,
including fast changing weather the one-minute resolution is highly desirable for the interpretation
of the phenomenon involved. Example applications include the identification of dust fronts; smoke
plumes near major fires or pollution plumes near urban-industrial centers. The fine-scale temporal
resolution of ASOS should allow separation of aerosol plumes from the background aerosol based
on temporal structure of the 1-minute data. Since the spatial and temporal pattern of pollutant
concentrations are related through the wind speed, the ASOS data should allow estimating the size of
aerosol puffs passing over the sensor.
As part of the proposed analysis the fine time resolution of the ASOS data will be evaluated with the
goal of characterizing the aerosol fine structure.
For many applications the “noisy” one-minute resolution ASOS data are not appropriate. This is the
case for regional scale analysis as well as for seasonal and long-term pattern analysis. The
aggregation of the ASOS light scattering data into hourly and daily aggregates is made difficult by
the strong non-linearity associated with the humidity effects. As part of the proposed work
alternative aggregation schemes will be evaluated along with recommendations for routine
processing.
ASOS Scattering- PM2.5 Relationship
One of the key purposes of this work is to evaluate the suitability of ASOS light scattering data as a
surrogate for PM2.5 concentrations. Research over the 30 years has demonstrated that light
scattering measurements using integrating nephelometers correlate well with fine particle mass,
except under humid and dusty conditions. It is expected that the forward scattering ASOS sensor
will also correlate well with fine mass. In fact, in a pilot study conducted by Richards et al., 1996, it
was found that hourly TEOM PM 2.5 data at south Boston, MA correlated well with ASOS visibility
sensor light scattering coefficient from Logan Airport. The 24 hr average of TEOM PM2.5 and
ASOS surrogate PM2.5 had a correlation R2 = 0.9. (Fig)
8
Fig. 3A. Comparison of the hourly ASOS scattering data with TEMO PM2.5 measurement in
Boston. B). Comparison of daily average scattering and PM2.5. (Richards et. al, 1996)
As part of this work, the ASOS scattering data will be compared to the existing PM2.5 data from
FRM and hourly TEOM/Beta gauge sensors. Currently there are about 600 PM2.5 FRM filter
samplers that report data every 3 days. (Fig. 4). Unfortunately, there are only a limited number (<50)
of hourly PM2.5 samplers through out the country.
Fig 4. Monitors reporting in the AIRS database at least 6 months of PM2.5 data sampled every 3rd
day from 1999 – 2000 (FRM).
The visibility and PM samplers are generally not collocated and a detailed data comparison is not
possible. However, the available data will allow the rough evaluation of the PM2.5 – ASOS
relationship over different regions, seasons, weather and aerosol conditions. Such analysis will be
conducted for the locations that are most suitable for intercomparison.
Systematic and Random Measurement Errors
The ASOS data will be evaluated for systematic and random errors. Stations that consistently deviate
from the surrounding stating values will be examined for possible systematic errors. Also, the
temporal pattern at the available sites will be examined for possibly spurious behavior. The
systematic and random measurement error analysis will be focused on the sites with duplicate
sensors.
Task 2. Analysis of the ASOS Data over the Southeastern US: Summer
2000 Huston Study
Spatial Pattern of Hourly ASOS Data during the Houston Aerosol Study
The ASOS data will be analyzed in detail to support 2000 Texas Air Quality Study
(http://www.utexas.edu/research/ceer/texaqs/visitors/study.html). The study involved measurements
at approximately 20 sites in the Houston area. In Figure Fig 5 squares represent the main ground
9
chemistry sites and the base of aircraft operations. Circles are particulate matter sites. Triangles are
additional meteorological and chemical sites.
Fig. 5. PM Sampler locations during the 2000 Houston Aerosol Study.
The ASOS data will be mapped for the entire duration of the Texas study, August-October, 2000.
The ASOS scattering data will be presented as
 Raw light scattering coefficient, Bscat
 Scattering coefficient with data during rain, fog and (90%+) humidity removed,
FBext
 Relative humidity-corrected scattering coefficient, RHBExt.
The ASOS data values, along with the available regional-scale PM2.5 values will provide a regional
context for the intensively monitored Houston data.
Comparison of ASOS Pattern with Routine Aerosol Data
The spatial and temporal pattern of the ASOS data in the Houston region will be compared to the
monitoring data during the Houston Aerosol Study. Given the intensive aerosol sampling network, it
is hoped that data from several monitoring sites will be suitable for comparison with the ASOS data.
Comparison with Special measurements During the Huston Aerosol Study
The Houston Aerosol Study included aircraft and other special sampling. The aircraft data provide
the vertical aerosol structure, while the ASOS and other surface data cover the spatial and temporal
pattern at the surface.
As part of this work, we will collaborate with the participants of the Houston study in jointly
analyzing the special aerosol measurement and the ASOS data. We will also contribute to the
analysis of the high-resolution satellite data from the 8 wavelength SeaWiFS sensor. The daily color
satellite data will provide estimates of the vertical aerosol optical depth over the region.
10
Deliverables
Report: Evaluation of ASOS Visibility Data as a PM Surrogate
Report: Analysis of the ASOS Data over the Southeastern US during the 2000 Huston Study
Data: ASOS data filtered for weather, humidity and spurious data.
Project Personnel, Period and Budget
The project will be conducted by Rudolf B. Husar, Professor of Mechanical Engineering and
Director of the Center for Air Pollution Impact and Trend Analysis (CAPITA), Washington,
University, St. Louis. He will be assisted by Graduate Research Assistants, Sean Raffuse and Jeremy
Colson.
The project will be conducted between September 2001- May 2002. Throughout the project, there
will close interaction with the Project Officer, James F. Meagher.
The Budget of the 9 month long project is $50,000. Tasks 1 and 2 are budgeted equally at $25,000
each.
References
ASOS: http://www.faa.gov/asos/asos.htm
Richards L.W., Dye T.S., Arthur M., Byars M.S. (1996) Analysis of ASOS Data for Visibility
Purposes, Final Report STI-996231-1610-FR, EPA Work Assignment 2-4. Contract Number
63D30064, prepared for Systems Applications International, Inc., San Rafael, CA.
Bradley, J.B., and R. Lewis, 1998: Comparability of ASOS and Human Observations, 14th
International Conference on Interactive Information Processing Systems (IIPS), American
Meteorological Society, Phoenix, AZ, Paper 10.1
Ramsay, A. C., 2000: Comparison of ASOS and Observer Ceiling Height and Visibility Values.
Report to then National Weather Service ASOS Program Office under Contract Number 50-DGNW6-90001.
Report
The Relationship Between Aerosol Light Scattering and
Fine Mass
Rudolf B. Husar and Stefan R. Falke
11
Center for Air Pollution Impact and Trend Analysis (CAPITA)
Washington University
St. Louis, MO 63130-4899
Submitted to:
Neil Frank, Project Officer
Cooperative Agreement # CX 824179-01
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC.
February 19, 1996
12
Table of Contents
TABLE OF CONTENTS
1
1. INTRODUCTION
2
2. POSSIBLE EVALUATION CRITERIA FOR PM2.5 MASS AND LIGHT SCATTERING
AS FINE PARTICLE STANDARDS
2
2.1. Relevance to the Aerosol Effects on Health and Environment
2
2.2. The Relationship to Aerosol Sources
3
2.3. Suitability for Enforcement
3
2.4. Suitability for Monitoring
3
3. LIGHT SCATTERING AND PM2.5 DATA SETS USED IN THE ANALYSIS
3
4. STATISTICAL RELATIONSHIP BETWEEN LIGHT SCATTERING AND PM2.5
4
4.2 PM2.5 and Light Scattering Correlation
4
4.2 Temporal Pattern
5
4.3 Diurnal Cycle of Light Scattering
6
5. BSCAT / PM2.5 EVENT ANALYSIS
6
1. INTRODUCTION
The ambient air quality standard for particulate matter is currently under review. In particular,
consideration is given to the introduction of an additional standard for fine particles. This is in
recognition of the fact that the sources, effects, as well as the atmospheric behavior of fine particles
are different from PM10.
The potential monitoring techniques for PM10 and fine particles are also different. High volume
filter samples for PM10 followed by gravimetric weighing of filters before and after exposure is a
well established technique derived from high volume TSP monitoring technology. Monitoring the
fine particle concentration, on the other hand, is more elaborate than for PM10 or TSP because
inertial size separation reduces the available air flow.
The aerosol population is a mixture of different particle sizes and each size class is composed of an
internal and/or external mixture of chemically diverse particles. Hence, it is not possible to express
the aerosol concentration as a single number, as is the case for gaseous pollutants. On the other
hand, practical considerations dictate that the number of aerosol parameters to be monitored has to
be limited. Thus, routine monitoring of aerosol chemical composition in many size classes does not
appear to be practical for enforcement purposes. Rather, the aerosol size - chemical composition
distribution function needs to be monitored using integral measures such as PM2.5 and/or total (or
size segregated) light scattering coefficient. PM2.5 is the integral of the aerosol mass - size
distribution up to about 2.5 µm. The total light scattering is also an integral of the aerosol mass size
distribution but also weighed by the size dependent scattering efficiency factor.
Recently, an ‘in situ’ fine particle monitoring standard has been suggested using integrating
nephelometers. Such devices are being considered either as a substitute or in conjunction with size
segregated filter and/or impactor samplers.
The purpose of this report is to present a comparative study of the aerosol light scattering - PM2.5
relationship using existing monitoring data. Light scattering - PM2.5 comparisons were conducted
using standard correlation statistics, as well as temporal pattern analysis on yearly, monthly, and
daily scales. This report also contains a brief discussion of the criteria by which the alternative
monitoring techniques (PM2.5 mass concentration and fine particle light scattering) are to be
evaluated as a suitable standard.
2. Possible evaluation criteria for PM2.5 mass and light
scattering as fine particle standards
The different aerosol monitoring methods as a potential fine particle standard can be evaluated from
at least four different points of view: relevance to the aerosol effects on health and environment, the
relationship to aerosol sources, suitability for enforcement, and suitability for monitoring. It is
certain that a full evaluation needs to incorporate additional criteria not listed here.
2.1. Relevance to the Aerosol Effects on Health and Environment
The aerosol parameter to be monitored has to be a suitable causal measure of health effects as well
as the effects on visibility, acid rain, climate, etc. It can be presumed that, for health effects, the
penetration into the lung and the health potency of the aerosol chemical species are relevant. On the
other hand, visibility effects are determined by the light extinction under atmospheric (humidity)
conditions. Acid deposition is primarily influenced by the aerosol acidity and it is not directly
effected by particle size. The direct aerosol effect on climate is due to scattering and absorption of
sunlight while the indirect aerosol effect on climate is due to the aerosol interaction with cloud
processes.
The main point of the above discussion is that each of the aerosol effects is associated with a specific
size and/or chemical composition. Therefore, it is not likely that the single monitoring variable
2
would be equally suitable as a surrogate for all of the effects. Thus, a choice in the measurement
technique would require a value judgment as to which effects exposure should to be matched most
closely.
2.2. The Relationship to Aerosol Sources
Ideally, the monitoring technique would allow the clear identification and apportionment of the
primary and secondary aerosol sources of each monitoring site. This criteria is relevant for the
introduction of effective control measures. The most common method of aerosol source type
attribution is the receptor oriented chemical source apportionment which requires reasonably
detailed aerosol chemical composition data. Apportionment of light scattering data among the
potential source types is also accomplished using the aerosol chemical composition data in
conjunction with chemically dependent mass extinction efficiencies. In case of aerosol light
scattering, special attention needs to be focused on the influence of ambient water vapor through
hydroscopicity. In general, the mass extinction efficiencies are inferred from statistical
considerations rather than from direct measurements.
The need for source identification for ambient aerosols dictates that aerosol chemical composition be
monitored at least at areas of high exposure, in the vicinity of unique sources, and other strategically
important locations.
2.3. Suitability for Enforcement
The monitored aerosol parameter needs to be suitable for documenting unambiguously man-induced
influences that are responsible for exceedances. For instance, uncontrollable high humidity events
may influence the readings without influencing the ambient concentrations or effects.
We are not fully aware of all the legal and technical issues associated with enforcement but it stands
to reason that the monitoring technique has to withstand legal scrutiny.
2.4. Suitability for Monitoring
The monitoring of the aerosol parameter should be accurate, precise, nearly continuous, and
inexpensive. Sampling of PM2.5 mass (as is usual for PM10) is intermittent (e.g. sixth day)
averaged over 24 hours and it is manual. Monitoring of the light scattering can be done
continuously, with high precision with automatic, self calibrating instruments. However, the
accuracy needs to be valued in the context of criteria 1) and 2); is the light scattering measurement
the relevant parameter?
From the point of view of monitoring ease and data coverage, light scattering instruments appear to
have advantages. On the other hand, from the point of view of data validation and source
apportionment, mass measurement is better because it permits compositional analysis.
3. Light scattering and pm2.5 DaTA Sets used in the analysis
In the current analysis, data from three different sources were utilized. The AIRS data base contains
light scattering data for about 100 of sites in the time period 1985-1995. PM2.5 data are also
available from AIRS for about 70 locations. Available light scattering and combined PM2.5 data
were examined to find locations that monitored for both fine mass and light scattering. In some
cases, both parameters were not measured at the same location but were measured at two separate
sites very near one another, i.e. PM2.5 observations from Rubidoux, CA were compared with light
scattering data from San Bernadino, CA. An overlap of the light scattering and PM2.5 AIRS
variables was limited to less than ten locations and short time periods. For this reason, the AIRS
PM2.5 data sets were augmented by fine particle monitoring data supplied by the state of California
3
Air Resources Board, ARB. The additional ARB data provided PM2.5 observations in California
for about 25 sites during 1989 - 1995. Available light scattering and combined PM2.5 data were
examined to find locations that monitored for both fine mass and light scattering. In some cases,
both parameters were not measured at the same location but were measured at two separate sites
very near one another, i.e. PM2.5 observations from Rubidoux, CA were compared with light
scattering data from San Bernadino, CA.
Light scattering and fine particle mass data were also available from the SCENES data base. These
were samples for seven sites surrounding the Grand Canyon National Park, operated between 1984 1989.
For all the sites, the hourly light scattering data were aggregated into daily averages so that they
were temporally compatible with the filter samples.
4. Statistical relationship between light scattering and PM2.5
It is well established that the fine mass concentration (PM2.5) measured by size segregated filter
sampling has a strong statistical correlation with total aerosol scattering. The main reason for this
relationship is that both the fine particle mass as well as the light scattering efficiency factor have a
peak in the size range 0.3 - 0.6 µm (Figures 1a, b). Exception to this relationship occur when the
characteristic aerosol size is either smaller (e.g. primary automobile exhaust) or larger (wind blown
dust) than the above size range. The most detailed discussion of the topic is contained in the
NAPAP State of Science Report 24. (Acid Deposition: State of Science and Technology, Volume
III, National Acid Precipitation Assessment Program, Washington, D.C., 1991)
4.2 PM2.5 and Light Scattering Correlation
A comparison of the light scattering coefficient and PM2.5 is shown for fourteen different sites in
Figure 2. The location of these sites is given in Figure 3, mostly west of the Mississippi. Suitable
light scattering-PM2.5 sites for the eastern U.S. were not available beyond St. Louis. The scatter
charts in Figure 2 is a cross plot of daily data for each site for the periods that both PM2.5 and
scattering data were available. The scatter charts also include the slope (m2/g) of the relationship as
well as the correlation, R2. The data for the fourteen sites indicate a good correlation, with half of
the sites exhibiting R2 above 0.8. Notable exception is Azusa, CA, R2 = 0.61. The slope, i.e. the
light scattering PM2.5 ratio, ranges between 4.1 and 11.9 with an average of 7.4 m2/g.
The goodness of the correlation for these data is comparable to numerous other studies conducted
over the past twenty years. However, it is remarkable that the light scattering to PM2.5 ratios are
about a factor of two higher than corresponding literature values. A recent review of the existing
data by White (NAPAP Tables 24-13 and 24-15) shows that this ratio is between 2.0 and 5.0 m2/g
for about 30 eastern and western, urban and rural sites. White also recorded two of the eastern sites
having a mass extinction efficiencies of about 10 m2/g.
A good example of light scattering data with low mass extinction efficiency is obtained during the
SCENES project, Figure 4. The slope for the entire data set is 2.78 m2/g and it ranges seasonally
between 2.5 m2/g in the summer and 3.4 m2/g in the winter. The R2 for the entire SCENES data set
is 0.42, which is substantially lower than the correlations found in this study.
The compilation of light scattering efficiencies by White is reproduced in Table 1 which also
includes the results of this study. A summary of fine particle mass ratios from different data sets is
also shown in Figure 5. The open rectangles show White’s compilation while the dark triangles
show the results of this analysis. It is clear that the results of this cursory analysis tend to indicate
substantially higher extinction efficiencies than the bulk of the literature values. There is also a
significant variation of the light scattering ratio between the monitoring sites. It was beyond the
scope of this examination to identify the causes of these discrepancies. Hence it is not clear what
4
specific light scattering instruments (nephelometers) were used, what were the sampling (humidity)
conditions, and what is the accuracy of these light scattering data sets.
4.2 Temporal Pattern
A difference between fine mass and light scattering data sets is the temporal coverage. The hourly
light scattering measurements reveal the aerosol fine structure in time that is not available from filter
samples collected every sixth day.
The nature of the high time resolution aerosol signal is illustrated through time series for six selected
stations, Canby (Portland), OR, Eugene, OR, Medford, OR, Stockton, CA, Azusa, CA, and Clayton
(St. Louis), MO (Figure 6). Each time series has three signals super imposed, hourly light scattering
coefficient (solid), daily average light scattering coefficient (dotted with crosses), and fine particle
mass (rectangles). The temporal variation of the aerosol signal is illustrated through a yearly plot
and two monthly time charts, one January and another July.
Canby, OR is a near urban site adjacent to Portland, OR. The light scattering signal indicates that
the highest light scattering average and the high excursions occur during the winter season,
December - March. The monthly chart for January shows the occurrence of short term, daily aerosol
events as well as aerosol accumulation for about a week (January 23-29). Throughout July, the
aerosol signal is low and uneventful.
Eugene, OR displays temporal trends similar to those of Canby, OR. The greatest light scattering
occurs during the winter months. The month of January shows high peak values of light scattering
and an order of magnitude variation from one day to another. July is less variable with low values.
Medford, OR is a valley site in southern Oregon that showed frequent fine mass concentrations well
in excess of 100 µm/m3. The site is characterized by a strong winter peak when both the absolute
concentrations and the variability is high. The monthly chart for January clearly shows the existence
of long term episodes that may last for a week or more (the squares that represent PM2.5 in the plot
are difficult to discern due to their overlap with the light scattering data). The summer
concentrations at Medford are low and uneventful.
Stockton, CA also exhibits a strong winter peak. The fluctuations between aerosol events exceed a
factor of ten. The time chart for January shows the occurrence of a long episode at about seventy
µm/m3 that lasts for about two weeks. Subsequently, the concentrations drop to below 5 µm/m3.
The summer concentrations at Stockton are below 10 µm/m3 and relatively constant.
Azusa, CA is in the Los Angeles basin and it is exposed to the Los Angeles smog. (Evidently the
light scattering coefficient data at Azusa are quantized) The seasonal pattern at Azusa is rather
uniform high concentration aerosol peaks occur throughout the year. The monthly charts for January
and July also indicate that aerosol events lasting two to four days occur during both winter and
summer.
Clayton, MO is a suburb of St. Louis and it is exposed to the regional haze which is characteristic for
much of the eastern U.S. The highest aerosol concentration and light scattering occur during the
summer season between June and September, distinctly different from the Western sites. The winter
concentrations are relatively low between 5 and 15 µm/m3. The concentrations for July show the
occurrence of week long haze episodes that alternate with cleaner air.
The high time resolution light scattering data clearly indicate that the aerosol variation is significant
in both season and monthly time scales. Western monitoring sites (except in Los Angeles) show a
winter peak while the eastern U.S. exhibits a summer peak in fine aerosol concentration and light
scattering. Qualitative inspection of the time charts also indicates that the western sites, e.g. Canby,
Medford, Stockton, exhibit somewhat more temporal texture in the winter than the peak summer
episodes in St. Louis. However in this analysis, we have not pursued a quantification of the
temporal signal variation. The only additional temporal analysis pertains to the diurnal cycle of light
scattering discussed next.
5
4.3 Diurnal Cycle of Light Scattering
Data on the diurnal cycle can only be obtained from high time resolution (hourly) continuous
monitoring, e.g. light scattering. The diurnal cycle charts (Figure 7) were obtained by averaging all
of the available light scattering data for a specific hour of the day and season. The examination of
the light scattering data for the above six “characteristic” sites reveals a somewhat varied diurnal
pattern that differs from site to site as well as seasonally for each site.
Canby, OR shows virtually no diurnal cycle for any of the seasons. The average diurnal modulation
is about 10% or less for all seasons.
Eugene, OR exhibits little diurnal variation during the spring and summer while during the fall and
winter it, unlike Canby, OR, displays high diurnal modulation. The light scattering peaks between 6
and 10PM with the lowest values occurring around 4PM.
Medford, OR shows one of the highest diurnal modulations of about 50% of the daily mean.
Furthermore, the diurnal cycle at Medford changes shape from one season to another. In January,
the peak concentration is at midnight while the lowest light scattering occurs at 6 AM. In April and
July, there is a strong peak at about 8 AM which shifts to a 9 AM peak in October. The lowest light
scattering in the fall occurs in the afternoon. The diurnal cycle of light scattering in Medford is a
clear indication of local sources in the air basin where the sampler is located. The shape and
magnitude of the diurnal cycle is influenced by the emission pattern, the atmospheric ventilation, and
possibly by the aerosol formation rate during fog conditions.
Stockton, CA also exhibits a moderate diurnal pattern during the cold season, January and October.
However, the peaks are shifted from 10 PM in January to 6 AM in October. During July, the light
scattering is constant throughout the day.
Azusa, CA also shows a diurnal cycle; a mid day peak 8-12 AM during April - October and an early
night peak at 10 PM during January. Again, this diurnal cycle of light scattering at Azusa is
determined by the smog emission, transport within the L.A. basin, as well as by the diurnal cycle of
the smog formation.
Clayton, MO shows minimal diurnal variation of light scattering. The values are somewhat elevated
during the night and early morning and less during the midday hours. It is quite remarkable that
during the summer months when the light scattering is the highest, the average Clayton values are
constant throughout the day. This tends to suggest that local sources and aerosol accumulation
during peak hours is not significant in determining the local light scattering coefficient.
The above analysis indicates that there is a measurable diurnal modulation of up to 50% of the daily
average of Western Valley sites where primary particle emissions are significant. However, at other
sites, particularly over the east (St. Louis) the diurnal fine particle variation is remarkably low,
consequently at those locations, the information content of hourly data is similar to the daily
average.
5. BSCAT / PM2.5 Event Analysis
The relationship between fine particle mass and light scattering can be obscured by many physical
factors and sampling errors. This cursory analysis prevents us from identifying the causal factors for
outliers. Therefore, in the brief discussion below, we merely highlight and expand on the conditions
when outliers in the scattergram occurred. For this reason, the statements below are to be taken with
a great deal of caution.
A closer analysis of the correlation plots in Figure 2 revealed the presence of days with unusually
high light scattering values or PM2.5 concentrations. Evaluating these “outlying” points may
provide better insight into the behavior of the BSCAT-PM2.5 relationship. In the following event
analysis, hourly and daily light scattering, PM2.5, PM10 and TSP data are examined. Figure 8
shows the plots for three locations: Eugene, OR, Azusa, CA, and Rubidoux/San Bernadino, CA.
Two aerosol events with extreme values are plotted for each location.
6
Eugene, OR has a low Bscat to PM2.5 ratio on February 18, 1985. While the aerosol concentrations
(PM2.5, TSP) have their highest values on the 18th, the daily average light scattering peaked on the
17th. A continuous increase in TSP from the 12th-18th does not reflect the very dynamic pattern of
the light scattering data over the same time period whereas from the 18th -24th the TSP and light
scattering behave similarly. The 26th of January, 1986 shows a peak in aerosol concentrations and
light scattering. The Bscat/PM2.5 ratio is consistently near 10 during the period.
The Azusa, CA correlation chart in Figure 8b shows two days with a light scattering of
approximately 600 (Mm)-1 with corresponding PM2.5 concentrations of about 25 and 90 µg / m3.
The PM2.5 concentration of 25 µg / m3 occurred on July 14, 1995. The figure shows a light
scattering peak on the fourteenth. PM2.5 and PM10 concentrations remained relatively constant
over the two week period while the TSP concentration exhibited a peak on the fourteenth. The
ozone data did not show any unusually high concentrations on or around the 14th. The other high
light scattering day in Azusa occurred on January 20, 1994. Daily average light scattering, PM2.5
and PM10 all peaked on this day and resulted in the highest PM2.5 concentration for the site as seen
in the scatter plot.
Rubidoux/San Bernadino, CA shows high Bscat to PM2.5 ratios on March 17, 1995 and October 29,
1994. The rise in daily average light scattering on the March date has a corresponding increase in
aerosol concentrations (PM2.5, PM10, TSP). The actual peak of the light scattering event occurs on
the 19th on a day with aerosol concentration data. The October plot displays a steady decrease in all
aerosol concentrations. The daily average light scattering shows a relatively constant value until the
29th, when it peaks slightly and then decreases.
7
Figure 1 (a) Light scattering per unit volume of aerosol material as a function of particle size, integrated over all wavelengths for a refractive index, m=1.5.
(Friedlander, S. K., Smoke, Dust and Haze, John Wiley & Sons, New York 1977) (b) Typical aerosol mass distribution for automobile primary emission, fine
particle haze, and coarse particle dust.
8
9
10
Figure 2. Relationship between daily average light scattering and PM2.5 for 13 sites. The Figure also contains the slope (m2/g), as well as the correlation, R2. Note, the
high ratio (average 7.4 m2/g) and good correlation (R2>0.8) at most sites.
11
Figure 3. Location of the sites where light scattering and fine mass data were available.
12
Figure 4a. Relationship between the daily light scattering coefficient and fine particle mass at the SCENES
monitoring sites near the Grand Canyon. Note, the low ratio (2.78 m2/g) and poor correlation.
Figure 4b. Relationship between the daily light scattering coefficient and fine particle mass at the SCENES
monitoring sites for four seasons. Note, that the ratio is somewhat higher in winter (3.41 m2/g), than
in the summer (2.55 m2/g).
14
Figure 5. The light scattering-fine mass ratio from the literature (rectangles) and from this study (solid
triangles).
15
16
CANBY
17
MEDFORD
18
19
EUGENE
20
STOCKTON
21
AZUSA
22
23
CLAYTON
Figure 6. The temporal pattern of hourly light scattering (solid lines), daily average light scattering (dashed
lines) and PM2.5 mass concentration for five typical sites. The top Figure is for an entire year. The
middle is for January, and the bottom is for July. The daily average light scattering value (midnight
to midnight) is plotted at 0 hour.
24
25
26
27
28
Figure 7. The diurnal pattern of hourly light scattering for five typical sites for January, April, July and
October.
29
30
31
32
33
34
Figure 8. Aerosol time series (a) for observations that are outliers on the scatter chart (b). The time series
includes hourly and daily light scattering as well as PM2.5, PM10, and TSP (PMT).
Progress Report
EPA grant No. CR 827981
35
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
36
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).
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
37
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 3-hourly
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
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
38
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
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
39
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
(1)
where
bext = light extinction coefficient
bsp = light scattering coefficient due to particles
bap = light absorption coefficient due to particles
bRay = Rayleigh light scattering coefficient due to gases
bag = 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
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):
b sp =   i ci
(2)
i
where ci is the concentrations of species i and αi is the scattering efficiency [m2/g].
40
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
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
41
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.
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.
42
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 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
43
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). 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
44
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.
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
45
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.
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48
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
49
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
50
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
51
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 b ext 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
53
Figure 1.Schematic diagram showing the flow of information and intermediate relationship necessary to relate airport visibility data to aerosol mass.
54
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.
55
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.
56
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.
57
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.
58
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 b ext 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 b ext = 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.
59
Figure 9. The ratio of the mean visibility derived dry aerosol b ext 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).
Appendix A. Hourly Surface Airways Location Table.
61
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.
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
62
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 B4). 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 B-6). It is not
known if this lack of dependence is due to the dew point temperature being a poor 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
63
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-Stokes-Robinson 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.
64
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
65
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.
66
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
67
Figure B-4. Back trajectories and associated dew point temperature for the Eastern US.
68
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.
69
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.
An Exploration of the NRL Global Surface Observation Database
70
Rudolf B. Husar
CAPITA
Washington University, ST. Louis, MO 63130
July 15, 2000
Background
Surface meteorological observations provide valuable data for the testing and verification of
atmospheric chemical transport models. In particular, the surface visual range provides an estimate
of the horizontal extinction coefficient along the line of sight, which in turn is related to the
concentration of aerosols. Additional parameters in the surface meteorological record include a
coded description of the current weather. There are specific codes for dust, smoke, and haze, as well
as for a large variety of weather-related obstructions to vision. The combination of the visual range
data and the weather code allows the estimation of the surface aerosol extinction coefficient as well
as its causes.
Given the successful operation of the NRL-NAPS global aerosol model since 1998, it became
desirable to verify and/or augment the NAPS model using the available meteorological data. Such
verification is particularly important over land, since the current operational satellite-based aerosol
retrievals operate well over the oceans but do not provide information over land.
A major problem with the visibility observations is that the obstructions to vision include rain, fog,
clouds, and other forms of hydrometeors. In order to detect the aerosol contribution, the weather
related phenomena need to be filtered so that the aerosol extinction signal is not obscured by
hydrometeors.
In 1999, NRL has contracted with R. Husar to adopt the weather filters for the calculation of the
aerosol extinction coefficient from the NRL data, and to implement the IDL code so that the NAAPS
system that would perform the appropriate filtering and plotting of th extinction coefficient. In
summer of 1999, these algorithms were developed and tested using a global surface data set for one
six hour period.
Since September 1999, the Bext filtering algorithm was incorporated in the production of six hourly
surface weather maps at NRL. The aerosol extinction coefficient was drawn as a circle, with radius
proportional to the extinction coefficient and color-coded according to dust, smoke, and haze.
The experience of the past 9-month suggested that both the weather filter and the display rendering
should be re-examined to better represent the surface aerosol pattern. This report summarizes this
second effort which consisted of:
1. 1. Assessment of the NRL surface observation over a longer period.
2. 2. Evaluation of surface extinction coefficient data over Africa , March-June 2000.
3. 3. Recommendation for additional activities that would improve the utility of the surface
visibility data.
Assessment of the NRL surface observation over a longer period
The NRL global surface observations were collected over the period March 28-June8, 2000. The
NRL data were plotted on the global map (Figure 1). On any particular day (e.g. May 1, 2000), the
station data are coded according to four different categories. Red circles, represent stations for
which valid Bext data were available. Blue circles represent stations that are eliminated because of
the weather flag, i.e. when the relative humidity was (>90%). The yellow circles indicate stations
71
that were filtered due to other weather flags (ww code>13). The small black circles show the
stations for which visibility data were not available at that particular hour.
Since the weather filters are highly diurnal, the pattern of these stations is shown in Figure 1 through
AVI animation representing the 24-hours May 1, 2000.
Fig.1. Global pattern of stations accepted (red), rejected due to RH>90% (blue), regejcted due to
weather (yellow) or not available (small circle). See AVI animation of the 24 hourly data.
This evaluation indicates that most of the global network provides data every 3 hours. Eastern
Europe, Japan and Australia have data for every hour. The majority of the U.S. has data are in 6hour increments at 00:00, 06:00, 12:00, 18:00.
The spatial pattern of the available data show that globally about 30-40% of the stations provides
valid aerosol Bext values. Stations in Europe and Southeast Asia have low fraction of valid Bext
stations due to high relative humidity, particularly in the early morning hours.
The fraction of the valid stations is very low in Africa, between Sahara and South Africa. Only 2030% (??) of the available stations report valid values. The cause low Bext coverage in the midsection of Africa is almost exclusively ‘missing data’. The spatial pattern of the stations in North
Africa for the 24-hour period is illustrated in Figure 2.
72
Fig. 2. Spatial distribution of different stations over N. Africa and Europe. Legend same as Fig. 1.
See AVI animation for all 24 hours.
Evaluation of surface extinction coefficient data
The resulting bext values for Africa, north of the Equator, Europe, and Middle East are shown in
Figure 3. The yellow circles are proportional to the surface extinction coefficient, 1/km. The
animation represents daily pattern at noon (12:00 GMT)
Figure 3. Noon aerosol extinction coefficient on 00/03/28. For the noon Bext maps for the entire
March-June period see the AVI animation.
The daily data over Africa indicate a rather consistent pattern in that high extinction coefficients tend
to occur over a cluster of stations. This is consistent with the regional dust clouds that occur over
500-1000 km areas. The main problem in Equatorial Africa is the large number of missing values.
73
Recommendation for additional activities
Based on the above discussion it is concluded that the weather filter incorporated in the current
operational algorithm is not too stringent. The station data are eliminated due to high humidity
(RH>90%) and precipitation. A major problem, particularly in Africa is lack of data.
In order to improve the station filters and derive meaningful Bext values for model comparison it
would also be necessary to evaluate each station whether it is suitable based on additional statistical
criteria. Many stations in Africa and elsewhere report constant visibilities throughout the record, as
illustrated in Fig. 4.
Figure 4. Constant visibility (Bext) indicating a bad visibility station
Since, it is impossible to have a constant extinction coefficient in the atmosphere the invariant
visibility data suggest that the observers did not bother to report the actual visual range. This is
troublesome for model comparison since the existence of dust clouds do not show up in all of the
data and the model comparison would be meaningless.
Given sufficient length of a data record, say 3-month, it would be possible to identify and reject
those stations that report constant visibility. In fact, this latter approach was applied in deriving the
global continental haze climatology, published recently by Husar et al., 2000.
Application for funding
Scoping Study for Regional Haze over the Upper Midwest
Principal Investigator
Rudolf B Husar, rhusar@me.wustl.edu
Center for Air Pollution Impact and Trend Analysis
74
Washington University
St. Louis, MO 63130-4899
Project Officer
Michael Koerber
Technical Director
Lake Michigan Air Directors Consortium
2250 East Devon Avenue, Suite 216
Des Plaines, IL 60018
August 18, 2000
Regulatory and Scientific Background
Regulatory Background
The federal haze regulations require the “prevention of any future, and the remedying of any
existing, impairment of visibility in Class I areas which impairment results from manmade air
pollution.” As part of the process to develop State Implementation Plans (SIPs) for regional haze, the
haze rule encourages the participation of States in Regional Planning Organizations (RPOs). The
states Illinois, Indiana, Michigan, Ohio, and Wisconsin have formed such an RPO administered by
the Lake Michigan Air Directors Consortium (LADCO). LADCO is responsible for preventing
future and remedying an existing visibility impairment at two Class I areas: Seney National
Wilderness Area and Isle Royale National Park, both located in northern Michigan. Also, the haze
regulations require reduction of haze impacts on Class I areas external to the LADCO region. There
are over 15 Class I areas to the south and east of the LADCO region that may be influenced by
LADCO emissions. There are also haze reciprocity commitments in the US Canada AQ Agreement
which will require consideration of Canadian impacts on US and vice versa.
The Regional Haze Scoping Study
From both technical and regulatory points of view, the regional haze issue is a major challenge to
LADCO. Over the time frame 2000-2004, technical information will be needed to support SIP
development by the States and to aid decisions as part of possible multi-jurisdiction agreements.
Some key details of haze regulations are yet to be filled in by EPA guidance and LADCO needs to
be prepared to comment on the development of these key details.
For these and other reasons, the actual regional haze study (LADCO Regional Haze Project, LRHP)
is preceded by this Scoping Study which has the purpose of planning an array of technical activities,
such as the collection and analysis of air quality data, the preparation of a regional emissions
inventory, and the application of air quality models. The remainder of this proposal provides a brief
scientific background on regional haze and describes the proposed approach for each of the
requested tasks.
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Regional Haze-PM-Ozone Relationship
Regional haze, i.e. the reduction of visual range and discoloration of objects, is caused by light
scattering and absorption of suspended liquid and solid particles, PM. The relationship between the
concentration of particulate matter and haze is determined by at least three major physical factors:
particle size, the quantity of absorbed water, and whether the particles are mixed externally or
internally. The relationship between the concentration of dry particle matter and light extinction is
expressed through the mass extinction efficiency, which is about 4 m2/g for anthropogenic haze,
about 3.5 m2/g for biomass smoke, and 0.7 m2/g for aged windblown dust.
The humidity dependence of light scattering is caused by the absorption of water vapor at higher
humidity with a resulting non-linear increase of scattering at humidities > 80% for hygroscopic
particles. The particle hygroscopicity is responsible for the strong diurnal cycle of regional haze
with the peak in the high humidity early morning hours.
Ultra-fine particles between 0.01-0.1 m in diameter with a given mass have virtually no effect on
light scattering. However, if the same mass of particles is internally mixed (attached to) particles in
the optical sub-range, 0.2-1.0 m, their scattering efficiency increases dramatically. Repeated incloud scavenging and subsequent evaporation is responsible for the internal mixing of particles
originating from multiple sources.
Figure 1a shows that during the summer, the highest Eastern US PM2.5 concentration is over the
Ohio River Valley. The unique feature of the LADCO region is the strong north-south gradient from
< 5 g/m3 in the north to over 20 g/m3 over the Ohio River Valley.
Ambient particles are of either primary or secondary origin. Within urban industrial areas primary
particulate sources such as automobile and diesel exhaust, road dust and fly ash are important
particularly during poor dispersion conditions in the winter. However, at remote sites such as the
two wilderness areas in upstate Michigan, virtually all (>95%) of the anthropogenic regional haze is
of secondary origin, i.e it is formed within the atmosphere from their precursor gases: SO2, NOx,
hydrocarbons and ammonia. Estimating the actual PM contribution from different sources requires
the knowledge of the cumulative transformation and removal processes between the source and
receptor.
In addition to the strong causal link between PM and haze, there is also a chemical link with ozone.
The transformation of sulfur oxides and volatile organics to particulate sulfates and organics is
strongly enhanced by oxidizing compounds such as ozone. On the other hand, aerosols can enhance
photochemical smog production (Dickerson et al. 1997, Science 278: 827-830.) due to redistribution
of UV light. As a consequence, the ozone-fine particle-haze complex constitute an inseparable
physico-chemical system.
EPA’s FACA Subcommittee on Ozone, Particulate Matter, and Regional Haze stressed the
interrelationships of these three specific regional air pollutants. The work associated with the
regional haze program can strengthen the programmatic link between the three pollutants. In fact,
several existing activities related to PM2.5 and ozone could directly benefit the regional haze
program: (1) The new PM2.5 monitoring network will provide an extensive data set.; (2) The virtual,
living, community-produced PM Data Analysis Workbook contains many data analysis procedures
that are directly applicable to regional haze (http://capita.wustl.edu/PMFine); (3) PM Supersites
Program will provide detailed aerosol data at several characteristic US locations; (4)The OTAG
Process for ozone has demonstrated the necessity of dynamic, multi-component, national-scale
modeling for secondary pollutants and for multi-regional collaboration.
The work on regional haze should also provide benefits to other regional pollutant programs, for
example: (1) the current PM Data Analysis Workbook could be expanded to include haze; (2) the
Regional Haze Program may support the application of satellite and WebCam monitoring that could
help the interpretation of the PM2.5 data; (3) the Regional Haze Program may also co-sponsor the
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operation of continuous, coupled on-line regional models for O3, PM2.5 and regional haze; (4) it may
co-support the development of better PM-related emission inventories.
Spatial Scales and Inter-Regional Transport of Haze
The residence time of light scattering fine particles in the lower troposphere is 3-5 days which is
about twice as long as for ozone. At an average wind speed of 5 m/s, this corresponds to 1500-2500
km of transport distance as illustrated in Figure 1b. The atmospheric lifetime of anthropogenic haze
is determined primarily by cloud scavenging and precipitation.
Given the relatively long and highly variable atmospheric residence time of haze particles, it is
beneficial to consider the characteristics of atmospheric haze at multiple spatial scales:
global/continental (~10,000 km), regional (~1,000 km) and local (~100 km). Global/continental
aerosol plumes have been found throughout the world based on global scale satellite observations.
Wind–blown dust plumes, biomass smoke and anthropogenic haze plumes have been observed as
5,000-10,000 km long continental plumes. Typical examples of such events are the Sahara dust
transport to the eastern US, the Asian dust transport to the US West Coast, and the Central American
smoke transport to the eastern US.
Figure 1. a. Map of summertime PM2.5 concentration based on IMPROVE, surface visibility and AIRS PM 2.5 data; b. Location of the
IMPROVE network stations and illustration of 1500-2500 km spatial scale; c. Satellite image of Idaho wildland forest fires smoke
plume approaching the LADCO region; d. Movement of hazy airmasses over the eastern US during a major haze episode in 1975.
Most of the haze dynamics occurs on 1000 km scale regions, such as the four quadrants of the
Eastern US. Such regions are influenced by their own emissions as well as by anthropogenic and
natural emissions in surrounding regional and international jurisdictions. Extra-regional transport
events represent extra-jurisdictional and largely uncontrollable background on top of which the
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regional and local contributions are superimposed. Figures 1c and d illustrate a smoke episode and
an anthropogenic regional haze episode with continental-scale transport over multiple regions.
Depending on the region, the ambient concentrations may be dominated (>75%) by emissions within
the region (e.g. Southeastern US). In other regions (e.g. the Northeast) the concentrations may be
dominated by extra jurisdictional contributions from the neighboring regions. Over the Upper
Midwest the intra-regional and inter-regional contributions may be comparable.
Although the long-range transport of aerosols is fully recognized and accepted by the scientific
community, the regulatory mechanisms to deal with the trans-regional and trans-boundary issues are
not yet fully established. At some point, it may be appropriate to add a national or North American
component to the regional haze RPOs.
Approach to the LADCO Regional Haze Scoping Project
AQ Data Analysis Needs
The haze regulations are based on aerosol monitoring data. The data provide the baseline visibility
conditions, allow evaluating model performance, and allow identifying specific sources that
contribute to visibility impairment. Long-term data also allow tracking of progress in reducing haze.
Adequacy of visibility related monitoring
The regional haze rules are expressed as reconstructed extinction coefficient, which in turn is
determined by the concentration of key fine particle species obtained from IMPROVE-like speciated
data. IMPROVE aerosol monitoring sites are being installed at the Isle Royale and Seney Class I
areas within the LADCO region, and are expected to start running this Fall on every 3rd day. There
will also be 32 additional IMPROVE protocol sites, as shown in Figure 1b. There will also be
several EPA speciation sites elsewhere in the LADCO region, starting about 2001. A major DOE
Ohio River Valley PM2.5 study also maintains several speciated PM monitoring sites. Additional
speciated aerosol data are available through the Canadian GAViM monitoring network nearby.
Since 1998 the number of FRM PM2.5 mass monitoring sites has been expanding rapidly and will
continue to increase.
Human visual range observations and more recently automated forward scattering instruments
(ASOS) at synoptic meteorological stations provide hourly data at a relatively dense network that
has been in operation for the past 50 years. Unfortunately, the new ASOS visibility data are
truncated at ten miles or less, but un-truncated data are potentially available. The full resolution
ASOS data would also help extending the past visibility trend analysis work based on human
observations. Nephelometers and transmissometers at IMPROVE monitoring sites provide higher
quality light scattering and extinction data coincidentally with speciated fine particle data.
Semi-quantitative satellite observations provide daily vertically integrated haze pattern globally at 1km resolution. SeaWiFS, Terra, Aqua, Earthprobe and other satellites will provide more quantitative
haze data over land. A recent new development is the use of WebCams, i.e. inexpensive digital
cameras monitoring a specific scene and sharing the images through the web. Both satellite and
WebCam data are useful for the documentation of haze episodes to the research and regulatory
communities as well as to the general public.
In summary, it is evident that adequate monitoring data are available from existing and near-future
monitoring systems to characterize the haze in the Upper Midwest. The exception is the western
boundary of the LADCO region which is sparsely monitored, leaving the incoming air from the west
poorly characterized.
The Scoping Study should identify possible additional gaps and redundancies in the monitoring
network (e.g. sites west of LADCO) which should be forwarded to the monitoring agencies. It
should recommend the acquisition of un-truncated ASOS data. The Scoping Study should assess the
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existing WebCams in LADCO region and should recommend possible augmentation. It should also
recommend procedures for archiving relevant satellite and WebCam data.
The Scoping Study should recommended that the visibility related data from multiple sources are
integrated into a coherent data set that would be suitable for assessing the nature of regional haze
and for the evaluation of dynamic haze models. An example of such a data integration effort is the
CAPITA integrated fine particle data set. The application of the CAPITA integrated fine particle and
visibility data set is shown in Figure 1a using a visibility surrogate-aided extrapolation procedure.
Procedures to characterize regional haze
The purpose of the haze characterization is to summarize and analyze the haze-related monitoring
data, establishing baseline and natural conditions, measuring trends, and identifying emission
sources contributing to regional haze. For the criteria pollutants, the key aspect of characterization is
whether the ambient concentration exceeds the NAAQS, which is set in terms of daily and yearly
average concentrations. For regional haze such absolute standards for extinction coefficient (or
deciviews) can not be set because of the highly variable (50-100 km in the West; 20-40 km in the
East) and largely uncontrollable background haze. The haze regulations are expressed in relative
terms, i.e. “prevention of any future, and the remedying of any existing, impairment of ...” This
formulation releases the burden of setting an absolute standard but imposes the requirement that the
existing conditions at the Class I areas are established.
The regional haze rules are expressed as reconstructed extinction coefficient, ReconEx. The value of
ReconEx is determined from traditional IMPROVE species groups (sulfates, nitrates, organics, EC,
fine soil and coarse), as well as Rayleigh scattering. Each of the species will be multiplied by their
respective dry extinction efficiencies. Sulfates and nitrates will be further multiplied by additional
relative humidity correction factors, f(RH).
The Scoping Study should evaluate the open issues related to this definition of regional haze metric.
A particular issue relevant to LADCO is how to deal with the relative humidity at the Seney
Wilderness Area which is near the water.
The Scoping Study should recommend additional statistical measures that characterize the haze at
the Class I sites and throughout the LADCO region. Recommended analyses may include: (1) spatial
pattern (e.g. contour maps of 80th and 20th percentile extinction coefficient); (2) seasonal pattern of
80th and 20th percentile of specific chemical species; (3) extinction budgets by source types under
high-low conditions (e.g. power plants, transportation); (4) jurisdiction budgets. The analytical
procedures 1-3 are standard methods of haze analysis that are well documented in the literature. The
concept of jurisdiction budget proposed here seeks to apportion the existing haze (extinction
coefficient) by jurisdiction. The three suggested jurisdiction categories are: L - Local anthropogenic
from within the region (e.g. LADCO); N – Non-local anthropogenic from other regions (including
Canada, Mexico, East Asia); U – Uncontrollable haze from emissions such as biomass burning and
wind-blown dust.
The Scoping Study should recommend specific analysis for establishing the haze (extinction) budget
at the Class 1 areas according to the above jurisdictional categories, with focus on quantifying the
local contributions. Alternative jurisdictional categories should also be explored to best satisfy the
need of the haze management process (SIPs, multi-region agreements). The above categorization
should also be applied for impact analysis, e.g: L:L - Local impacts from Local sources; L:N - Local
impacts from Non-Local sources (including Canada and East Asia); N:L - Non-Local impacts from
Local sources.
In the Scoping Study the jurisdictional haze budgeting and impact analysis should be specified in
more detail, including the appropriate receptor and source oriented analysis and modeling techniques
described below. The Scoping Study should recommend specific analyses that incorporate the close
link between the ozone, PM, haze systems. For example, it would be beneficial to examine the
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patterns of ozone during the hazy days and identify the relationship between the hazy days and high
ozone days.
The procedures to characterize the regional haze have many commonalities between the RPOs. The
Scoping Study should recommend ways to share knowledge and experience among the RPOs. One
possible approach would be to expand the virtual PM Analysis Workbook by including specific
regional haze sections. Additional sources of haze-related analysis procedures can be found in the
archives of the MOHAVE and Grand Canyon Visibility studies in the Southwest and SAMI in the
Southeast.
Procedures to establish baseline and natural conditions
The prevention of any future, and the remedying of any existing, impairment of visibility due to
manmade sources requires the establishment of baseline and natural conditions. The baseline
conditions will be established based on conditions between 2000-2004. The level of haze will be
calculated by the ReconEx procedures. The upper and lower 20th percentile of ReconEx are to be
determined for the baseline period. The lower 20% is used to prevent loss of clean days. Upper 20th
percentile must be gradually lowered toward natural background over the next 60 years. The two
major natural haze sources arise from highly episodic forest fires and wind-blown dust events.
A further complication in defining the natural conditions arises from the fact that these ‘natural’
conditions have changed dramatically throughout this century. For example, during the dust-bowl
era of the 1930s major dust clouds were reported over Minnesota and Michigan a dozen times a year.
The frequency of dust storms have declined significantly since then. Similarly, the frequency of
major forest fires has declined markedly since the early 20th century with the onset of forest fire
management practices. According to anecdotal evidence, forest fire smoke was a major cause of
regional haze over the eastern US. In fact, in the late 1890s, the term ‘Indian summer’ described the
dry smoky days in September and October.
The Scoping Study should recommend establishing the frequency and magnitude of wind-blown
dust events to the Upper Midwest, based on the chemical trace constituents of the soil dust. The
frequency and magnitude of forest fire smoke should also be determined. Since the chemical tracers
of forest fire smoke are variable from one fire to another and the composition and size of the smoke
tends to change with age, the Scoping Study should recommend procedures to quantify the
uncontrollable biomass smoke. The recommended techniques should also include satellite remote
sensing of fire and smoke events. An example smoke transport event from Idaho fires is illustrated in
Figure 1c.
The Scoping Study should devote considerable attention to the issue of ‘natural conditions’. For
example, the haze regulations are expressed as long-term averages. One of the challenges for
LADCO, the other haze RPOs and the federal EPA will be to establish a proper aggregation metric
that expresses the highly episodic natural events as long-term averages. The Scoping Study should
recommend procedures for establishing the causes of haze in jurisdictional context, e.g. Local
anthropogenic, Non-local anthropogenic, as well as the Uncontrollable contributions from smoke
and dust.
Many of the key details related to baseline and natural conditions are challenging from scientific and
regulatory viewpoints. It is likely that the definition of baseline and the determination of natural
conditions over the LADCO region will evolve over the next few years. Both the federal EPA and
the other RPOs will participate in this process. The Scoping Study should help LADCO with
technical information that will be needed to actively participate in this collective conceptual
development process.
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Emissions Inventory Needs
Existing emission inventories
Emission inventories are required for establishing a regional haze control program, tracking progress
in emission reductions as well as for operating regulatory models. The secondary origin of remote
regional haze implies that the most relevant emission inventories are those of precursor gases, SO2,
NOx, hydrocarbons and ammonia. In fact, the main purpose of speciated PM source characterization
is to derive chemical fingerprints for different source types: e.g. selenium for coal-fired power
plants, nickel/vanadium for oil-fired power plants. Since over 50% of the Eastern US regional haze
is attributed to sulfates, the quality of the SO2 emission inventories will require particular scrutiny.
Fortunately, the SO2 emission inventories have been scrutinized as part of the acid rain programs in
the 1980s.
The Scoping Study needs to examine the existing emission inventories, relevant to the regional haze
program, including the USEPA’s NET inventory, USEPA’s final SIP Call inventory, and LADCO’s
Grid M inventory. The primary emissions of fine particles and crustal materials will also need to be
reviewed.
The Scoping Study could recommend the use of EPA National Emissions Trends (NET) emissions
repository as the initial emission database. The Scoping Study should also recommend procedures
for evaluating the long-term consistency of the emissions data for tracking emission reduction
progress.
Emission inventory improvements and recommended activities
The Scoping Study should recommend comparing the EPA NET to the LADCO Grid M inventory
for consistency. Reasons for any apparent discrepancies for SO2 in the NET and LADCO should be
identified. Visibility trends should be also compared to SOx emission trends for consistency.
Deviations in the visibility/SOx emission trends may include inconsistent long-term emissions data,
changing the mix of key aerosol species or the relative roles of transported haze.
The Scoping Study should recommend to LADCO the establishment of a comprehensive, dynamic
emissions model for SO2, NH3, NOx, and reactive hydrocarbons. Such a model could be driven by
economic indicators and materials flow data such as fuel combustion. Such an emissions model
would allow a more responsive tracking of the seasonal and yearly emissions as well as their
underlying causes.
The Scoping Study should recommend procedures on gathering information on Canadian emission
inventories. The Canadian industrial emissions may contribute to regional haze and the “cleanest”
days in the LADCO region are associated with air masses arriving out of Canada. Therefore
Canadian sources may have a relatively greater influence on the baseline 20%. The Canadian and US
inventories should be merged. One such effort is conducted under the U.S./Canada Air Quality
Agreement in which the Subcommittee on Scientific Cooperation has established a transboundary
modeling work group for particulate matter. This workgroup is developing an emission inventory
for its modeling efforts that will be ready sometime in the spring of 2000.
The Scoping Study should evaluate the role of inland marine emissions and recommend procedures
for their incorporation. For example, NESCAUM has developed an emissions inventory for Boston
Harbor (NOx, PM, HC, CO, SOx).
The Scoping Study should recommend procedures to evaluate the existing ammonia emissions data
for the region, particularly in the agricultural areas where most of the ammonia emissions occur.
One approach to verify the ammonia inventories would be to use receptor data (e.g. ammonia
deposition) and models to reconstruct, validate or improve the inventories. There is a growing body
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of literature on these ‘source retrieval’ or inversion techniques and the regional haze program would
be a meaningful platform to explore these new techniques.
The Scoping Study should recommend the gathering of speciated particulate source profiles
throughout the LADCO region. This effort should include incorporation of recent source
characterization by DOE for the power plants in the Ohio River Valley region.
The Scoping Study should recommend procedures to make the emission data and methodologies
consistent across the Eastern US. Procedures for coordination, comparison and sharing of emission
methodologies should be recommended, including shared databases and websites. Procedures for
emission inventory updates and revisions should also be recommended.
Identification of Sources Subject to BART
The southern part of the LADCO region has among the highest sulfur emission densities in the
country. The NOx emissions are also high throughout the urban-industrial part of LADCO. Many of
the SOx/NOx sources are older power plants that are eligible for Best Available Retrofit Technology,
BART. The BART provision is applicable to any sources constructed between 1962 and 1977 that
emit pollutants ‘that are reasonably anticipated to contribute to visibility impairment in any Class I
area’. The BART provision can also take into account costs, remaining lifetime of the plant, energy
consequences, etc. BART could be the major regulatory action that will actually reduce emissions
related to the regional haze regulations. In this context, it is relevant that substantial recent sulfur
reductions have occurred in the LADCO region from the implementation Title IV, and more
reductions are under way. If the recent EPA NOx SIP calls survive the courts, it will also result in
significant NOx reductions from LADCO utility sources.
The Scoping Study should recommend approaches for the identification of specific sources that are
subject to BART. Such procedures will require plant-by plant inventories and examination of the
inventory database for BART-suitable plans. The procedures will largely depend on future EPA
guidance on this issue.
AQ Modeling Needs and Options
LADCO will have to rely on the AQ models for source identification, establishing specific sourcereceptor relationship and ultimately providing and quantifying the options for SIPs. Comprehensive
regional models will also be important to demonstrate and quantify the linkage between ozone, PM,
and regional haze and to derive the most cost-effective control strategies that involve all three
pollutants.
Over the past years, LADCO has been instrumental in the application of regional models for the
development of ozone management policies. The Regional Haze Project could provide opportunity
for LADCO to extend its regulatory modeling leadership to regional haze and PM. This will be a
challenging task, since PM and regional haze modeling encompasses many uncertainties in emission
inventories and transformation/removal processes.
Comprehensive and Reduced Form Dynamic Models for Regional Haze
The primary objective of this task is to determine and recommend the best available analytical tools
for regional haze planning in the LADCO region including their strengths and weaknesses. Modeling
the dynamic aerosol system can be accomplished by a complementary set of comprehensive and
reduced form models.
Predictive comprehensive models are based on sound physico-chemical principles that describe the
transport, chemical transformation, and removal processes in the atmosphere. They explicitly include
the interaction among all relevant chemical species including ozone and PM. Comprehensive models
require relatively high spatial and temporal resolution (~10 km) over several thousand kilometers in
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order to cover the size of airsheds of regional haze and to be suitable for exploring control options
and non-linearities of coupled physico-chemical systems. Such models are suitable tools for SIP
development. However, their complexity precludes their use in continuous mode over continental or
global scales.
The Scoping Study should recommend the evaluation of the available comprehensive models.
Currently, the leading comprehensive regional haze model is EPA’s Models3 system which has all
the features described above. In addition, it is a flexible community-based modeling system with
easily interchangeable modules for emission inventories, transport, physico-chemical processes and
output visualization. The Models3 system is currently being evaluated at CAPITA using the
integrated fine particle database for the July 1995 ozone and regional haze episode.
Reduced form dynamic models include linearized chemistry, which precludes their use for control
scenarios in SIP development. They have generally coarser spatial resolution (~100 km) and allow
continental scale or global spatial coverage and can be operated continuously and in the forecast
mode, or historically covering many years. In fact, the Navy global aerosol model for sulfates, dust
and smoke, NAAPS, has been operating continuously since 1998 and provides global aerosol
forecasts that are distributed through the web. Unlike the comprehensive PM modeling systems,
reduced form models are in abundance. They include grid-based models (REMSAT, NAAPS),
trajectory-puff models (ATAD; CALPUFF), as well as hybrid Monte Carlo simulations (CAPITA
regional model).
The Scoping Study should recommend conducting a literature search to catalogue the wealth of work
that has already been completed on reduced form models. The model evaluation/selection should
include factors such as quality of output (performance against the observations), required
meteorological inputs, spatial-temporal resolution, computational efficiency and computer platform,
documentation and track record of candidate models, experience of states and federal agencies with
specific models.
The Scoping Study should recommend ways to establish the consistency between the comprehensive
and the reduced-form models. Specific recommendations of collaboration on model
selection/evaluation of the comprehensive and reduced form models should be coordinated between
the NPOs, EPA, academic researchers and others. The establishment of interagency virtual
workgroups on model selection and evaluation would be one possibility.
Data sources for model evaluation
The available data sources for model evaluation should be drawn from the extensive PM, visibility
and satellite data described in the section “Adequacy of visibility related monitoring”. Integrated
data from multiple sources are most suitable for model evaluation. For example, the CAPITA group
is conducting a comprehensive evaluation of the Models3 modeling system using the CAPITA
integrated PM dataset for July 1996. CAPITA is also cooperating with the Navy in validating the
Navy global aerosol model using synoptic visibility data.
The Scoping Study should recommend the subsets of the monitoring data that are most suitable for
model validation. Also, the specific validation approach used in the CAPITA Models3 evaluation
should be extended to incorporate the more extensive recent chemical and satellite aerosol data.
Recommend receptor- and source-based models for PM2.5 and regional haze
Receptor-oriented models begin with observational data at the receptor and trace the pollutant
backwards to the source. The two complementary classes of receptor models are statistical and backtrajectory models. Statistical models help identify ambient aerosol type based on the chemical tracers
found in the ambient aerosols, e.g. smelter, power pant, automobile. There are three main categories
of statistical models, the chemical mass balance (CMB8, T. Coutler, EPA), positive matrix
factorization (PMF, P. Paatero, P. Hopke), and UNIMIX (R. Henry, USC). EPA is currently
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evaluating a suite of these receptor models for possible application to PM2.5, and they may prove to
be useful tools for evaluation of regional haze sources as well. Statistical models can not determine
the geographic location of sources. They also place heavy demand on air quality data including
ambient chemical composition data and source signature data. A major difficulty of statistical
models is that they can not adequately quantify secondary aerosol species since they do not have
unique source signatures.
The main purpose of trajectory models is to identify the source location by backward tracing the
airmass histories. Trajectory models can not apriori predict the chemical content of the arriving
airmass or evaluate the effectiveness of future emissions control strategies. However, since they
require minimal computational resources, they can be useful tools for evaluating long-term (multiyear) patterns in meteorological flow patterns and associated pollutant concentrations. Examples of
long-term, ensemble trajectory assessment techniques include: cluster analysis; residence time
analysis; potential source contribution function and quantitative transport bias analysis.
Over the past 30 years, statistical and trajectory receptor models have evolved to be practical and
useful for PM source identification. However, none of these methods are self-sufficient but best
viewed as complimentary. In fact, it is likely that the implementation of haze regulations will apply
several of these receptor methods to characterize regional haze.
The Scoping Study should review and evaluate the above suite of receptor models and recommend
tools that are most suitable for LADCO. The selection of models will also depend on factors outside
the jurisdiction of LADCO. EPA has recently issued a draft guidance document addressing modeling
of fine particles for the purposes of meeting air quality goals with respect to regional haze. The
selection of one or more air quality model(s) to be used for demonstrations of reasonable progress is
specifically addressed and will serve as a guide for the LADCO and other RPOs.
Capabilities
The proposed Scoping Study will be conducted at the Center for Air Pollution Impact and Trend
Analysis at Washington University in St. Louis. Over the past 30 years the CAPITA group has been
studying regional haze throughout the US and more recently throughout the world. CAPITA has
accumulated numerous PM and haze related databases and integrated those into coherent data sets
for spatial and seasonal pattern analysis, trend analysis as well as source identification. Throughout
the years, CAPITA has made these data sets available to the research community through its highly
visited web sites. The CAPITA research included the development of the CAPITA Monte Carlo
model for the simulation of regional pollutants including sulfates and regional haze. The model was
extensively tested since its development in 1980.
The regional haze Scoping Study will be under the direction of Professor Rudolf Husar, Director of
CAPITA. In 1976 he published one of the earliest papers on regional haze and its relationship on
regional scale ozone. Since the late 1970s he has continuously updated visibility and sulfur emission
trend data which were used extensively in NAS reports, the Visibility Report to Congress and
influenced the deliberations of the 1990 CAAA. The Scoping Study will also involve Drs Bret
Schichtel and Doug Fox as consultants. Both researchers have extensive experience in regional
modeling specifically on issues related to regional haze. In conducting this LADCO Scoping Study,
the proposing team will seek the comments and participation of leading haze researchers.
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