Fine Particle Maps Derived from Regional
PM2.5 and Visibility Data
by Rudolf Husar (rhusar@mecf.wustl.edu), Janja Husar(jhusar@mecf.wustl.edu) and Stefan
Falke (stefan@mecf.wustl.edu)
Center for Air Pollution Impact and Trend Analysis (CAPITA)
February, 1995
This report describes the methodology used in deriving quarterly and annual fine particle maps
for the US. The approach utilizes fine mass (<2.5 µm) concentration data from a 50 station
regional monitoring network (IMPROVE/NESCAUM) as the measured "anchor points" for the
derived maps. The spatial interpolation beyond the measured PM2.5 data is accomplished with
the aid of extinction coefficients (Bext) derived from a 280 station visibility data set. Annual fine
mass contour maps are given for 1987-89, 1990-92, and 1992 while quarterly maps are given for
1988-92. The approach uses novel data flow programming techniques for data processing and
fusion.
Contents:

Background and Rationale

Approach

Fine Mass Concentrations

Visibility as a Fine Particle Surrogate

Visibility Data Processing

Bext-Fine Mass Relationship

Data Flow for Fine Particle Mass Map Preparation

Resulting Fine Mass Concentration Maps

Limitations and Possible Improvements to Fine Particle Maps

Appendix I

References
Background and Rationale
Fine particles are known contributors of visibility degradation, acid deposition, and regional
climate change. Recent epidemiological studies also indicate fine particles as causal factors in
human health effects. For this and other reasons, the Environmental Protection Agency is
considering the promulgation of ambient concentration standards for fine particles. In this report,
the term fine particles refers to the mass concentration of particles below 2.5 µm (nominal). In
other contexts, the fine-coarse particle size cut may be anywhere between 1 and 2.5 µm.
The spatial and temporal pattern and trends of fine particles over the U.S. is not well established
since systematic fine particle monitoring has begun only in the 1980s and at limited number of
sites. On the other hand, estimating the fine particle effects on human health and other effects
requires higher resolution than what is provided by existing fine particle monitoring networks.
Higher spatial and time resolution particle concentrations can be estimated by several methods:
1. Integration and fusion of multiple fine particle mass concentration data sets.
2. Use of surrogate variables for spatial and temporal extrapolation.
3. Application of properly calibrated and verified regional and local atmospheric
transmission models as data extrapolators.
This report applies the surrogate method 2, by utilizing the existing higher resolution visibility
data for the interpolation of measured fine particle concentrations.
Back to Contents
Approach
Data Sets
The present approach for the construction of fine particle mass uses measured fine particle mass
concentrations by the IMPROVE/NESCAUM regional 50 station monitoring networks. These
fine particle mass data are used as "anchor points" for the derived maps. The geographic
extrapolation of the fine particle mass concentration is aided by the higher spatial resolution (280
stations) visibility data as surrogates for the fine particle concentrations. This method relies on
the strong relationship between the light extinction coefficient (derived from properly filtered
visibility observations) and fine mass concentration.
Data Fusion
The general procedure for the integration of multiple data sets is referred to as data fusion. The
concepts of data fusion is best developed and applied in the field of robotics (Abidi and
Gonzalez, 1992), but data fusion efforts are also being pursued in environmental sciences. The
essence of data fusion is that sensory input may arise from different sensors with varying spatial
and temporal resolution. Hence, the raw signals are inherently incompatible for integrative
combined usage.
Data fusion applied in this context reconciles the multiple data inputs by re-projecting the data
sets onto a common spatial and temporal resolution. Following such reconciliation, standard data
operations, such as addition subtractions, etc. can be performed on spatially compatible data sets.
The results of such manipulations are fused data. In this particular report, the fusion is between
measured fine particle concentrations and the visibility derived light extinction data.
Data Flow Programming
The implementation of the data fusion process reported here, was accomplished using modern
computer science principles of data flow programming and distributed computing. A distributed
application is made up of independent modules and logical connections, "wires," among them.
The modules are generally reusable components, that can be "wired" together in many different
ways, creating new data processing applications. The logical connections are used for data
exchange between modules. In a data flow based distributed application, flow of control is
determined by availability of data; modules are "woken up" when data arrives, and they go back
to "sleep" once they have processed the data. A data processing application commonly uses three
categories of modules: data producers, data transformers, and data consumers. End users create
data processing applications by connecting these modules.
Schematic illustration of data flow programming.
Such data flow programming is particularly suitable for graphic user interfaces where the
selection of the components, the linking among the components, as well as the overall data
flow between components can be laid out using drag-and-drop visual languages. The
conceptual simplicity, along with the visual feedback from the data flow programming
language allows end-users (scientists) to create their own data processing programs without
the participation of professional programmers.
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Fine Mass Concentrations
Our current understanding of the US national fine mass concentration (FM) pattern arises
primarily from non-urban monitoring networks, the Interagency Monitoring of Protected
Visual Environment (IMPROVE) and Northeast States for Coordinated Air Use
Management (NESCAUM). Additional FM data are also available through EPA's
Aerometric Information Retrieval System (AIRS). However, the AIRS FM data were not
used in this analysis.
The IMPROVE/NESCAUM non-urban aerosol concentrations are measured at remote
sites, away from urban-industrial activities. The sites are located mostly in national parks
and wilderness areas. Size-segregated aerosol mass and chemical composition data are
available for 50 sites, through the IMPROVE (Eldred et al., 1987; Eldred et al., 1988;
Eldred et al., 1989) and NESCAUM (Flocchini et al., 1990, Poirot et al., 1990) networks.
The PM10 and PM2.5 mass concentrations are sampled and analyzed on separate filters.
The sampling frequency was generally twice a week for 24-hours. The PM2.5 samples are
analyzed for chemical composition which make the data sets suitable for chemical mass
balance computations (e.g. Gebhart and Malm, 1987; Schichtel and Husar, 1991; Sisler et
al., 1993; Eldred and Cahill, 1994; Sisler and Malm, 1994). The IMPROVE/NESCAUM
aerosol data are available from 1988 through 1993.
An example of national fine mass concentration pattern is shown below.
Fine Mass Concentrations
Quarter 1 (1988-92)
Quarter 2 (1988-92)
Quarter 3 (1988-92)
Quarter 4 (1988-92)
Annual (1992)
The Figures show the location of the monitoring sites, the magnitude of annual fine mass
concentration at these sites as well as the estimated contour lines. The contours drawn for
the eastern US are derived from only 15-20 stations. Given the sparseness of the data, the
contour lines are to be taken as guides to the eye and not as actual pattern. The coarseness
of the contoured data in the Figure confirms the need to develop higher resolution fine
mass concentration maps.
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Visibility as a Fine Particle Surrogate
Visual range monitoring at meteorological observation sites provides an attractive
surrogate and augmentation of fine particle concentration measurements. Computerized
visibility records exist since 1948 for several hundred US sites, compared to 20-50 fine
particle monitoring sites. The meteorological visibility data have been evaluated for pattern
and trends (Munn, 1973; Husar et al. 1976; 1979;1981; Robinson and Valente, 1982;
Trijonis, 1982; Sloane, 1982a; 1982b; Husar et al. 1986; Sloane, 1984; Wolff et al., 1982;
Elridge, 1966) and compared to the emission trends (Husar and Wilson, 1993).
The excellent spatial and temporal coverage of the visibility data base can be utilized only
after careful site by site scrutiny for interference and anomalous behavior. The limitations
of the visibility database includes threshold visibility, maximum visibility reported, lack of
suitable dark targets, visual acuity of human observers, and meteorological influences
(rain, fog, snow) on visual range. Nevertheless, the consistent visibility observations over
the past 50 years at over 280 US sites provide a stable, long-term, well behaving data set for
spatial and temporal pattern analysis.
Visibility (Extinction Coefficient - 75th Percentile)
Quarter 1
(1988-92)
Quarter 2
(1988-92)
Quarter 3
(1988-92)
Quarter 4
(1988-92)
Annual
(1987-89)
Annual
(1990-92)
Annual
(1992)
The data quality control of the visibility data was implemented: (1) using visual inspection
of daily time series for each station, (2) observing the percentile trends, and (3) comparing
the time series for neighboring sites. The description of the methodologies used in visibility
data pre-processing and quality control is given in the companion report "Methodology for
Visibility Trend Analysis." The results of the visibility pattern and trend analysis have been
presented in an earlier report, Husar et al., 1994.
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Visibility Data Processing
For purposes of spatial-temporal trend analysis, raw visibility observations were
summarized as quarterly averages of extinction coefficient, but significant pre-processing
was applied. The data pre-processing included a time of day and a meteorological filters.
Only daily local noon observations were used. Nightime observations were ignored since
they are not uniquely related to the extinction coefficient.
Meteorological parameters such as precipitation (rain, fog, and snow) and humidity tend to
influence visibility and their role was minimized by the application of meteorological
filters. The role of these natural obstructions to vision can be eliminated by discarding the
data when these meteorological phenomena occur.
The aggregation of extinction coefficient data into monthly or quarterly averages was
performed using non-parametric quantiles of the distribution function. A major advantage
of quintiles in trends is that such nonparametric statistics require no assumptions about the
form of the actual distribution function. Trijonis and Yuan (1978), Husar et al. (1979), and
others have successfully used this approach. For each month 25th, 50th, 75th, and 90th
percentiles were computed. For this work the 75th percentiles were used.
For each month and station, three different extinction coefficients were calculated: 1) The
first set included all visibility data regardless of weather and pollutant conditions (BX); 2)
the second group (FX) is composed of extinction coefficients excluding precipitation, fog
events; and RH>90%; 3) the third set of extinction coefficients (RX) also used the
precipitation and fog filter but also included a RH correction factor to compensate for
water and vapor effects, RC=FX/C(RH). In effect, the role of the factor is to normalize all
the extinction coefficients to 60% relative humidity. The strongest correction factor is
applied at high humidities (90%). Bext values measured at RH>90% were eliminated. In
the analysis below the 75th percentile FX values were used, fog and precipitation values
removed.
The spatial pattern of extinction coefficients is presented as contour maps. The contouring
procedure is described in Appendix 1. The resulting contours as well as the corresponding
stations, Bext data (the square size is proportional to Bext at a monitoring site) are shown
in Figure 3. The shades are 0.03 km-1 apart. The darkest shade (red) has an extinction
coefficient (75th) percentile of >0.2 km-1 which corresponds to 1.9/0.2=9.5 km visual range.
The lowest contour is set at 0.05 km-1which corresponds to 1.9/0.05=38 km visual range.
Back to Contents
Bext-Fine Mass Relationship
The visual range or visibility is a subjective concept, being a maximum distance at which
an observer can discern the outline of an object. It is a measure of the quality of the
atmospheric optical environment. As a measure of air pollution, visual range has the
disadvantage that it is inversely related to aerosol concentration. A more suitable measure
is haziness or extinction coefficient, Bext, defined as Bext =K/visual range, where K is the
Koschmieder constant. The extinction coefficient is in units of km-1 and it is proportional
to the concentration of light scattering and absorbing aerosols and gases.
The value of K is determined by both the threshold sensitivity of the human eye as well as
by the contrast of the visible objects against the horizon sky. The original value of K
proposed by Koschmieder was 3.92 which corresponds to detectable threshold contrast of
2%. In this report, we have taken K=1.9 in accordance with more recent data by Griffing
1980; Dzubay et al., 1982; Stevens et al., 1984, Ozkaynak et al., 1985.
Bext to Fine Mass Ratio
Quarter 1 (1988-92)
Quarter 2 (1988-92)
Quarter 3 (1988-92)
Quarter 4 (1988-92) Annual (1988-92)
The Bext/FM ratio is a key variable in the present data fusion process. The use of Bext as a
fine mass surrogate relies on a spatially smooth Bext/FM ratio. In general, the spatial
pattern is smooth. Notable exceptions are high Bext/FM values over northern Minnesota,
eastern California and a site in southern Oregon, particulalry in quarters 1 and 3.
High values of Bext/FM will arise either due to higher than expected Bext, or lower than
expected FM.
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Data Flow for Fine Particle Mass Map Preparation
The procedure for creating fine particle maps using the visibility surrogate is shown the
data flow chart. The Figure contains two types of components: the shaded boxes represent
operators or (processors) that take input data, manipulate them, and produce an output.
The oval elements represent the data inputs/outputs to/from the operators. The numbers
adjacent to the data elements are sequential step output counters.
Data Flow for Fine Particle Map
The first operations involving data, step numbers 1 and 2, entail aggregating daily Bext and fine mass data to
quarterly averages. The quarterly Bext and fine mass data were constructed in table formats, containing
columns for site name, longitude, latitude, and associated Bext or fine particle concentration values.
Additional tables containing averaged Bext data over the time periods 1987 -1989, 1990 - 1992, and 1992 was
also constructed as shown as step 9. The values for the annual Bext data table are displayed as squares in the
extinction coefficient maps.
The annual Bext tables were transformed to grid format using an operator called the Contourer. The inputs
consist of the table, as well as user specified inputs that include a distance weight function, a radius of
influence, a minimum required number of data points within the radius, a maximum number of data points
used in the calculation of the grid value, and the dimensions for the output grid.
The next step, 6, was to produce a quarterly Bext/FM ratio table. This was accomplished using a ratio table
producing operator. The inputs to the operator were the quarterly Bext grid and the quarterly fine mass
table. Since the fine mass data were in table format, each concentration value was associated with a particular
latitude and longitude. The Bext data were in the form of a 120 X 80 grid. The operator read through the two
inputs and from the fine mass table it extracted the longitude, latitude, and fine mass concentration data. The
operator used the longitude and latitude values from the table as search parameters in the grid. It extracted
Bext values from the grid nearest the latitude and longitude from the fine particle table. The operator then
divided the Bext value by the corresponding fine particle concentration value. An output table was produced
containing the latitude, longitude, and Bext/FM ratio value.
The Bext /fine mass ratio table was sent through the Contourer to produce a ratio grid. This process is
identical to that described in the conversion of Bext tables to grids. The resulting Bext/fine mass ratio grid
was used with the quarterly Bext grid as inputs to the multiplier operator which produced annual fine
particle grids. The multiplier read through the two input grids and multiplied the Bext values by the inverse
of the ratio values to calculate a visibility fine particle concentration grid.
Visibility Corrected FM = Bext * FM/Bext
The resulting grid is shown in the visibility corrected fine mass maps. The display and printing of all the
maps was accomplished through the MapEdit program.
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Resulting Fine Mass Concentration Maps
The resulting annual fine mass maps are shown below.
Visibility Corrected Fine Mass
Quarter 1
(1988-92)
Quarter 2
(1988-92)
Quarter 3
(1988-92)
Quarter 4
(1988-92)
Annual
(1987-89)
Annual
(1990-92)
Annual
(1992)
A comparison of the fine mass concentration maps using only the fine mass data with the visibility-augmented
FM maps shows significantly more detail for the latter. The fused fine mass concentrations show spatial
heterogeneity over both the eastern and western US. It is likely, however, that in reality the spatial gradients
are much stronger than depicted, particularly over urban centers and topographically confined valleys of the
western states.
The comparison of the three maps representing annual averages for 1992, 1990-92, 1987-89, show substantial
year to year and regional variation. For example, the Northeast and Mid-Atlantic states exhibit highest
concentration in 1987-89. On the other hand, the fine mass estimates for the Gulf states are highest for 199092.
The quarterly maps show the fine mass concentration peaks in quarters 2 and 3. The visibility surrogate
decreased concentrations from intial fine mass map in large areas of the west, such as the Oregon,Nevada,
and Northern California area and the region extending from Montana southward to Colorado. The east
shows more defined concentration contours with increases in concentrations in some areas, parts of Northa
Carolina and Virginia in quarter 3, for instance, and decreases in others, like the Appalaican area of
Virginia.
A verification of the fine mass maps was accomplished by comparing the measured fine mass concentrations
at about 50 IMPROVE/NESCAUM sites to the reconstructed fine mass following the data
fusion.
Ideally, the measured and reconstructed data should be identical. For most monitoring stations the measured
and fused values are virtually identical. However, for some stations the fused data are significantly less than
the measured values. This is due to the smoothing of data surfaces during the gridding of Bext/FM ratios.
Very high data values, such as those in eastern California and Oregon tend to be reduced by the inclusion and
weighing of other neighboring sites. Another cause of error in matching the measured concentrations occurs
when creating a grid from the point concentrations. The grid resolution (120X80) provides for a grid cell of
approximate dimensions 45kmX30km. The value associated with a cell is based on the centriod of the cell. In
most cases, fine mass sites were not located at the center of the cells so that the cells in which they were
located did not have the same concentration as the site itself. As a result, when concnetrations were extracted
from the grid they were different than the measured fine mass, especially in high station density areas where
multiple sites significanlty influenced the value of a single grid cell.
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Limitations and Possible Improvements to Fine Particle
Maps
The fine particle maps of the conterminous US presented here have known limitations. The causes of these
limitations and possible remedies in future work are stated below.
Inadequate PM2.5 concentration data
Most of the currently available fine concentration data that have national coverage are available for remote
sites from the IMPROVE/NESCAUM networks. These provide a reasonably complete picture of the regional
background. However, higher concentration fine particle hot spots that occur in major urban industrial areas
of US and at topographically confined air basins of the mountainous western US are not adequately covered.
Consequently, the fine particle maps given here represent a regional pattern without the detailed influences
of urban and confined airsheds.
An improvement of the coverage of the local hot spot concentrations could be achieved by the gathering and
proper inclusion of more fine particle concentration data in the spatial analysis work. The candidate data sets
for this purpose include fine particle data in the national AIRS system, as well as numerous short term and
geographically limited fine particle data sets that have been gathered over the US, e.g. Philadelphia, St. Louis,
Salt Lake City, Los Angeles, Grand Canyon region, Oregon, and Washington. Prudent fusion of these data
sets would provide a direct sensory input for more spatially resolved fine particle maps.
Use of visibility as a surrogate and interpolator
The available visibility data set is about an order of magnitude higher in resolution than the
IMPROVE/NESCAUM fine particle network. However, the visibility network itself does not resolve the
spatial gradients within urban areas. The primary reason being that the visibility observations at airports are
generally outside of the urban areas. Consequently, visibility as a surrogate aids the depiction of more
detailed regional pattern, but it is of marginal help in delineating the pattern within urban areas and smaller
confined air basins. Another limitation of visibility as a surrogate is that the filtering of meteorological
influences (fog and precipitation) can not be performed with high precision.
A possible improvement would arise from the combination, i.e. data fusion of several different fine particle
surrogates in addition to visibility. Such surrogates may include concentrations of PM10 (available at over
1000 sites), concentration of aerosol sulfate or other chemical species and the possible use of aerosol remote
sensing by geostationary and polar orbiting meteorological satellites.
Spatial interpolation schemes
Currently applied spatial extrapolation schemes that relate the concentration at an arbitrary point to the
concentration at monitoring sites rely strictly on weighing functions that depend only on distance from the
station. Typically, this weighing of station influence is proportional to the inverse distance square (1/r2).
There is no physical principle that supports this extrapolation scheme, but it appears to work better than
alternative distance weighing functions.
An improvement in the interpolationpolation could be achieved through the consideration of other physical
or chemical factors that inhibit or enhance the spatial dispersion of air pollutants. The most obvious of these
factors is the terrain elevation. The use of elevation in the contouring process would necessitate certain
assumptions regarding the elevation dependence of aerosol concentration. Using this approach for example,
high elevation mountain peaks would be assigned low concentrations even though they may be in the vicinity
of high concentration valley sites.
Physico-chemical modeling
More sophisticated future data fusion procedures may include use of physico-chemical models that
incorporate transport, transformation, and removal processes. Such models, with the proper assimilation of
the existing monitoring data, would likely yield the highest quality concentration pattern. However, a
development of such integrated data and model based extrapolation schemes would probably require several
years of development and testing.
Back to Contents
Appendix I
An important data transformation processes used in this work is contouring. Most of the spatial patterns are
presented on contour maps. The contours were derived from the point observations using a spatial
extrapolation scheme. In the first step, the data from the random locations were projected to a uniform grid
with 120x80 nodes that covers the conterminous U.S. The gridding used inverse distance squared (1/r2) as the
station weighing factor. The extrapolations outside the U.S. boundaries were trimmed to eliminate spurious
values.
Contourer Pseudocode
Contour
Functionality:
transform data from table format into grid format
Input:
Table - a set of data points
WeightFunc - distance weight function
Radius - distance constraint
MinPoints - minimum required number of data points within Radius
MaxPoints - maximum number of data points used in calculation of a
grid cell
Output:
Grid - uniformly distributed set of data points
function Contour(out Grid, in Table, in WeightFunc, in Radius, in MinPoints,
in MaxPoints)
{
for each Cell in Grid
CalcCell
}
CalcCell
Functionality:
calculate value of a grid cell given a table of data
points around it
function CalcCell(in out Cell, in Table, in WeightFunc, in Radius, in
MinPoints, in MaxPoints)
{
PointList = an empty list of table points
for each point in Table that is not null
if the distance between the point and the cell is less than Radius
then
add point to the PointList, sorted by distance
Cell value = Interpolate PointList
}
Interpolate
Functionality:
interpolate over a sorted list of data points
function Interpolate(in PointList, in WeightFunc, in MinPoints, in MaxPoints)
{
if number of points in PointList
Back to Contents
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