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The second approach to the more detailed Level II dispersion, modeling
analysis depends on meteorological data recorded on specific days when
particulate matter sampling has also been conducted. This approach is
preferable if comparisons between measured and dispersion-modeled pollutant
concentrations are anticipated. If the meteorological data are available,
this approach has the advantage that the selection of meteorological input is
simple. The meteorological data used in the model must adequately represent
the actual meteorological conditions in the study area. As discussed earlier,
modeled impacts are very sensitive to wind direction. For this reason alone,
when modeling only involves a single sampling day and a single receptor, there
is little likelihood of agreement between measured and modeled
concentrations. The use of additional receptors in close proximity to the
monitoring site will illustrate the sensitivity of predicted values to wind
direction.
As mentioned earlier, it is often important to be able to determine
source/receptor relationships on an annual average basis. A number of
dispersion models are available for this purpose as shown in Table 4-4. The
available models use either an annual stability wind rose or a full year of
hour-by-hour meteorological data to calculate annual average source impacts.
Procedures for using these models with a full year of data are contained in
the respective model user's guides.
4.4.4.3 An Approach for Comparing Dispersion and Receptor Model Results
Comparisons between receptor and dispersion modeling results can provide
important insights into source-receptor relationships. The two analysis
approaches use different input information and incorporate different
assumptions. The uncertainties associated with each approach can be
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substantial, but the similarities and differences between results from the two
methods can often be used to identify erroneous or inadequate data and to
achieve more reliable dispersion model predictions.
At the outset, the key to making comparisons between source (dispersion)
and receptor modeling is to obtain source model results for the same
monitoring periods when ambient measurements were collected. The sampling
days selected for the analysis should represent a variety of dispersion
conditions. The uncertainties associated with results from a single day are
quite large; comparisons of results for a larger number of individual days (10
or 15 is recommended as a minimum) are more meaningful. The dispersion model
used in these analyses must provide impact predictions for individual
sources. The ISC models (ISCST and ISCLT) are best suited for this purpose
because, in addition to the features described earlier, the ISC models will
report impacts for selected groups of sources as well as for individual
sources. The dispersion and receptor modeling results can then be compared on
a source-by-source (or source group-by-source group) basis. Source groups are
defined by the receptor modeling results, since receptor modeling does not
distinguish among sources with similar emission compositions.
In the event that the receptor model results agree with the dispersion
model results on a source-by-source basis, the receptor model analyses would
constitute convincing evidence for the validity of the dispersion modeling
analyses and vice-versa, since each will have been conducted independently.
(As discussed previously, receptor model source contribution results should be
established using two or more receptor models.) It is more likely that the
source culpability information provided by the receptor model will differ
considerably from that provided by the dispersion model, particularly for
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fugitive dust sources. In the latter case, validated receptor model results
can be used to direct a re-examination of the dispersion model inputs to
determine where inadequacies may exist.
The process of modifying the dispersion model or model inputs, based on
comparisons between dispersion model predictions, observed ambient
concentrations, and receptor modeling results, is primarily intended to
identify and correct errors or shortcomings. The purpose of this exercise is
to obtain more reliable, technically supportable dispersion model predictions,
not to create the illusion of agreement between two problem-solving
approaches. If reasonable results can be obtained through dispersion
modeling, then a powerful analysis tool is available for estimating air
quality at locations other than monitors and for assessing control strategy
options.
The challenge is to accomplish this end in a systematic fashion, and to
minimize the use of subjective, trial-and-error techniques. One effective
device for controlling this process is the use of a "comparison protocol",
which defines the stepwise analysis procedures to be followed once dispersion
model and receptor model results are obtained. The protocol, prepared before
modeling is performed, anticipates areas of possible disagreement between the
two modeling approaches, identifies the model inputs and model features which
could produce such disagreement, and outlines the decision criteria for
revising the inputs or the model. Examples of the procedures such a protocol
might contain are discussed below. The concept of a model comparison protocol
is defined in considerable detail in the EPA document Interim Procedures for
Evaluating Air Quality Models (U.S. EPA, 1981c). This document describes
procedures for comparing the performance of two dispersion models and is not
entirely applicable to the present topic.
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There are a number of factors which cause dispersion model predictions to
disagree with measured particulate matter concentrations. Some of these
problems are source-specific, primarily affecting individual source-to-monitor
impacts, while others are more likely to produce systematic or widespread bias
in the model predictions. Examples of the former include:
• Erroneous emission rates caused by such items as the omission of
unknown sources from the modeling or the use of inaccurate
throughput information.
• The use of inappropriate emission rates, such as the use of total
particulate matter emission rates which include particle sizes
that settle out prior to impacting the monitoring site.
• Incorrect information concerning daily source operating
parameters. For example, while a source may operate at 45 percent
capacity on an annual basis, its actual mode of operation may be
at 90 percent capacity for 50 percent of the days in a year.
* Neglect or incorrect consideration of downwash from tall stack
sources.
• Neglect or incorrect consideration of resuspended particles.
• Building interference causing source-to-receptor (i.e., source-to-
monitor) geometry to be incompatible with Gaussian dispersion
assumptions.
• Local meteorology differing from that modeled. A typical problem
is wind direction shift or channeling caused by buildings or
topographic features.
Examples of factors that are not addressed by the dispersion model and
that may cause systematic biases in model results include:
• Heat island effects which cause the actual near-source dispersion
of elevated emissions to be greater than that modeled.
• Sea/land breeze effects.
• Effects caused by the fumigation of tall stack emissions.
• Effects caused by the development of thermal internal boundary
layers (TIBL) over areas with varying surface heating
characteristics.
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There are also potential systematic and random biases in receptor models.
Causes of such biases include:
• The selection of ambient sampler locations and filter samples for
analysis such that the data studied are not spatially or
temporally representative of the real air quality.
• Incomplete sampling schedules such that anomalous events are
disproportionately represented in the data base. For example,
samples may have been collected during periods of emission control
equipment failure, or, in contrast, during periods when important
sources were not operating. Conversely, a lack of such samples
would also bias the data base.
• Incomplete or inaccurate information on source emission
compositions, or the existence of sources with similar emission
compositions such that the contribution of one source cannot be
distinguished from that of another.
• Emission transformations where the amount or composition of
emissions changes between the source and the receptor. Examples
include particle deposition rates that differ among the chemical
components, chemical reactions in the atmosphere, and the
reentrainment of emissions that have already been deposited.
• Ambient sampling or filter analysis deficiencies where the data
collected do not accurately reflect what is in the air. Examples
include disparate collection efficiencies of the various filter
media, artifact formation on filters, and analysis techniques
that only see large particles, particles on certain parts of the
filters or certain types of particles.
• Unrepresentative meteorological data. Examples include
misoriented pollution roses and upper air trajectory analyses with
directional and distance errors.
A three phase process is suggested to determine the need for making any
changes in the dispersion model or input parameters. The first phase involves
taking steps to eliminate obvious dispersion model input errors; the second
involves modifying the dispersion model based on receptor model results for
those sources whose input parameters are most certain; and the third involves
the systematic comparison of predicted with measured concentrations to
document model performance.
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4.4.4.3.1 Phase 1. Refine Emissions Inventory. When large discrepancies
are found between observed ambient concentrations and dispersion model
predictions, emissions data are the first item to investigate. Receptor
modeling results will indicate the contributions from different sources to
observed concentrations. Comparison of source contributions predicted by
•
receptor and dispersion model analyses will serve to focus attention on
individual sources or source groups. Additional test data or more detailed
engineering information can then provide a basis for inventory revisions.
Phase 1 can be accomplished through the following stepwise process:
• Identify "significant" sources or source groups from receptor
model results. Sources which account for only a small fraction of
observed ambient concentrations should not be investigated further.
• Assign a "level of confidence" to emission parameter estimates for
each significant source or source group, based on engineering
estimates and on receptor model results. Table 4-7 illustrates
the confidence level information needed from this analysis.
• Obtain dispersion model results. Compare source contributions
predicted by dispersion and receptor models. Identify any large,
systematic discrepancies between source contributions from the two
models.
• Re-examine the emissions data for sources with large
discrepancies. Revise the inventory through additional source
testing or improved engineering estimates, where confidence in
existing data is low.
Recognizing that substantial uncertainty in particulate emission
parameters and dispersion model predictions is unavoidable for most urban or
industrialized regions, this Phase 1 effort is designed to focus attention on
significant sources and on obvious errors. Analyses should be based on
multi-day modeling results, rather than an individual day, since dispersion
model predictions for short-term averaging periods can be very sensitive to
small errors in plume transport direction.
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TABLE 4-7
ASSESSMENT OF CONFIDENCE IN DISPERSION MODEL INPUT PARAMETERS
Source Number/Type/Name
Source-Specific Errors
Emission rate 3 1 etc.
Particle size
characterization 2 1
Source locations/dimensions 3 2
Source operation parameters 1 3
Downwash 3 1
Building interference 1
Wind direction 2
Other meteorology 3
Other
Key:
0 = Least confidence (in parameter or effect as modeled)
3 = Greatest confidence (in parameter or effect as modeled)
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Confidence levels for emission parameters should be determined prior to
dispersion modeling, to minimize the bias introduced by knowing in advance how
well the dispersion model performs for any given source. Some receptor
modeling techniques also provide quantitative estimates of the uncertainty
associated with calculated source contributions. Model performance evaluation
studies for dispersion models have shown that uncertainties of 20 to 30
percent are common for long-term average concentrations predicted at a given
monitor location (Londergan, et al., 1983). Efforts to identify serious
errors in the emission inventory should focus on sources with discrepancies of
at least a factor of 2 between receptor and dispersion model predictions.
The pattern of discrepancies at different receptors may also aid in
identifying potential inventory errors. For example, large overproduction by
the dispersion model at a receptor very close to the source would suggest
possible errors in source geometry; large overprediction at receptors far from
a source would suggest an erroneous particle size distribution. Experience
with dispersion models and emission inventories will play an important role in
diagnosing potential inventory problems.
The protocol will serve as a check on the subjective process described
above. The knowledge that a discrepancy can be "fixed" by increasing the
emissions of a specific source is not sufficient grounds for revising the
inventory. Technical justification will be required before changes are made.
Through this process, however, efforts can be focused on the relatively few
sources which are critical to dispersion model performance.
4.4.4.3.2. Phase 2. Further Dispersion Model Modifications. After the
inventory revisions have been incorporated, revised dispersion model
predictions will be obtained. The revised source contributions predicted by
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the dispersion model are again compared with receptor model results, to
determine whether further modifications to the dispersion model or
meteorological inputs are warranted. For this comparison, attention is
focused on sources with the highest confidence rating for emission parameters
and receptor modeling results. Any large, systematic discrepancies between
dispersion and receptor model predictions for these sources would indicate
shortcomings in either the receptor or the dispersion model.
The protocol will identify, in advance, the limitations inherent in the
dispersion modeling approach. Such limitations may include meteorological
inputs, assumptions concerning dispersion rates, particle deposition, effects
of surface roughness or terrain, effects of land/water interface, etc.
Remedial measures could include acquisition of additional meteorological data
(if available>, choice of different model options, or modification of
dispersion algorithms to suit local conditions.
The pattern of model performance at different receptor locations and for
different source types can provide useful information for diagnosing
dispersion model shortcomings. Once again, however, emphasis should be placed
upon large discrepancies between dispersion and receptor model results,
recognizing the uncertainties inherent in both modeling approaches. Clusters
of receptors, placed around each monitor, can be used to assess the
sensitivity of dispersion model predictions to small changes in plume
transport direction (and indirectly to indicate model sensitivity to one cause
of prediction uncertainty).
Model modifications should be undertaken in an effort to improve
performance by incorporating features which take into account local dispersion
conditions and source characteristics. Problem-specific model modifications
will often prove a cost-effective alternative to additional sampling and
analysis.
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4.4.4.3.3 Phase 3. Document Dispersion Model Performance. After
revisions to the emissions inventory and/or the dispersion model have been
implemented, it is important to document model performance using available
monitoring data. Before the dispersion model is used to determine control
requirements or to assess alternative strategies, the uncertainty associated
with model predictions should be understood. For this purpose, observed and
predicted ambient particulate matter concentrations should be compared, with
primary emphasis on the concentration values associated with air quality
standards (e.g., annual average and peak 24-hour concentrations). The
comparisons should not be restricted to those days selected for receptor model
analysis, but should include at least one year of ambient monitoring data.
The meteorological data used should correspond to the particulate matter
sampling schedule.
Detailed recommendations concerning model performance evaluation are
contained in the reports Judging Model Performance, from the 1980 American
Meteorological Society (AMS) Workshop (Pox, 1981), and Interim Procedures for
Evaluating Air Quality Models (U.S. EPA, 1981c). In the present context, a
limited analysis of model performance is envisioned, rather than the
comprehensive statistical evaluation described in these reports, but the basic
analysis approach is quite similar.
Results from the performance evaluation will provide a basis for deciding
whether to rely upon the dispersion model to formulate control strategies, or
whether to use monitoring data and receptor modeling results. If the
dispersion model is demonstrably reliable at the monitoring sites then model
predictions can be made for the entire study area and control strategies
should be developed based on the model results. If the dispersion model is
not proven reliable, and measured concentrations show nonattainment, then
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control strategies would be developed based on the measured data and receptor
modeling results. However, in this case, the issue of the air quality at
non-monitored locations must be addressed. If the monitoring sites are not
representative of the entire study area, based on dispersion model
predictions, then additional monitors may be needed at locations of high
predicted impact so that additional receptor modeling analyses can be used to
determine the source impacts at such sites.
4.4.5 Resources
Resources required for Level II analyses are significantly higher than
those required for Level I. At least a mini-computer is required to run
programs, not all of which are in the public domain at this time. Data bases
are large and require sufficient storage. The analyses described here should
not require substantial additional monitoring, but it will usually be
necessary to perform a variety of laboratory analyses on existing samples.
Analysis costs will range from $25,000 to $100,000. Four months to one
man-year of effort will be required.
4.5 Level III: New Sampling, Analysis, and Model Development
The frequent need to solve problems associated with atmospheric
pollutants, other than those materials collected routinely by monitoring
networks, requires the development of sampling and analysis strategies
specifically directed to the individual problem. In many cases, these
problems can be explored using receptor models or dispersion models and can
use readily available sampling and analysis methodologies. However, sometimes
it may be necessary to incorporate vastly different approaches into the study
design. A few examples would be programs to define the sources of 1) sulfate.
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2) organic particulate matter, and 3) the mass of material in particular size
fractions. For each of the above, a similar general approach can be used in
the design of the experiment; however, the details for each case require
considerable discussion.
The following is structured to establish general criteria for the
development of Level III studies.
4.5.1 Source Data
As has been described in other sections of this volume, and other volumes
of this series, a major concern is the availability and subsequent cataloging
of source emissions data for both receptor and dispersion models. In Level I
and II, the methodology for acquiring useful information from existing source
emissions data bases was described. However, these will probably be
inadequate to address many specific air pollution problems because all the
sources, or at least the major sources, in a particular study area may have
only been tested for a limited number of parameters. Furthermore, some data
bases were not designed to mimic the emission patterns that would more closely
represent the physical and chemical fractionations and transformations that
occur in the environment and are observed in receptor samples (for example,
sulfate and organic species). Such a deficiency is important when using a
receptor model to allocate the percent of mass associated with a source or
when" trying to validate the model with some contribution estimates from
appropriately parameterized dispersion models.
The first stage of Level III is to draw upon the previously acquired data
in Levels I and II and outline a strategy based upon the strengths and
weaknesses of available emissions inventories. At present, these data could
be augmented by conducting actual source tests similar to those normally used
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in the federal New Source Performance Standards (NSPS) or National Emission
Standards for Hazardous Air Pollutants (NESHAP) programs, but adding trace
element and other chemical analyses would be required in the development of
source compositions for input to receptor models. In most cases, the
particulate matter size fractionation would not have been attempted, and the
results would be useful only if the chemistry of the emissions does not vary
appreciably between size fractions.
In place of the above, the critical question of compatibility of the
source compositions with those perceived at the receptor could be addressed
using dilution sampling or platform sampling downwind of plumes. These
techniques could also estimate the mass emission rates under a variety of
operating conditions. A selection criteria for the sources to be tested
should be established based upon 1) the adequacy of cataloged profiles (with
concern for regional differences in sources), 2) the emission rates of
sources, 3) the proximity of the source to the receptor site, and 4) the
potential hazard associated with the source. Additional source testing
criteria are discussed in Section 2.2 of Volume II of this series (U.S. SPA,
1981b).
Dilution source testing has been identified as a method to help increase
the possibility of having the source composition more closely reflect that
sampled at a receptor. It involves cooling of the sampled effluent to a
temperature which is nearly equivalent to that found in the ambient
environment to simulate the conditions under which condensation, growth and
other reactions of the emitted species occur. A further distinct advantage is
the possibility for sampling the diluted effluent with techniques equivalent
to those used at the receptor. In the case of particulate matter, the net
result could be the development of source composition catalogs which would be
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size classified and/or be able to capture more volatile species. As mentioned
in previous volumes of this series, a present drawback is that this sampling
is still in the development stage. Therefore, a standardized sampling
methodology is not available as of this writing.
Another approach to the collection of emissions at conditions which are
more nearly equivalent to those anticipated at a receptor would be platform
plume sampling. In the past, this methodology has been used in dynamic
experiments (Lagrangian coordinate system) where the production and transport
of ozone and secondary particulate species were studied in both urban and
power plant plumes. However, plume sampling can be accomplished in an
Eulerian experiment (fixed coordinates) by measuring the plume at a fixed
point with a suspended platform (skyhook, etc.) near the receptor. Obviously,
because of the expense, major sources along the prevailing wind direction
should be of prime consideration and the sampling platform, fixed or
otherwise, must have collection devices which are nearly equivalent to those
used at the receptor. Preliminary or informational studies on the anticipated
emissions should be completed so that the plume sampling is well focused. As
stated previously, the sampling should be size selective (one or two stage)
for particulate matter in order to accommodate future regulatory requirements
and to differentiate between the fine and coarse fraction compositions.
In some types of studies, ground based plume studies may also be
warranted. This technique will be quite useful for receptor sites located
near major automobile and truck traffic arteries, farmlands spraying
pesticides, fugitive dust emissions, and field burning activities. Properly
outfitted vans or temporary trailer monitoring stations can be used to house
equipment.
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The source characterization used in Level III studies will be directed
toward achieving the most representative compositions of the sources that can
affect a receptor. It will combine information available from Level I and
Level II activities with new source tests. These latter tests will be
designed to be conducted at conditions more closely associated with the
ambient environment. To reduce costs, each should be conducted at times when
ambient samples are collected at the receptor and the receptor is downwind of
the source.
4.5.2 Meteorological Data
The Level I and II approaches to source or receptor modeling involve the
accumulation of readily available meteorological data from NWS, State and
local government or privately operated stations. In addition, synoptic or
mesoscale meteorological data are processed via computer programs to give
either upper air or surface wind trajectories. In many cases, this is all the
resolution that may be required, since these data are available near most
major urban centers. For multivariate receptor modeling techniques the same
resolution is sufficient such that with large numbers of samples (200-400), it
will be possible to categorize the apportionment of a given species according
to wind direction. The trajectory analyses will also prove useful in
attempting to determine the contributions of chemical or other species from
local versus regional sources.
In a Level III study it is possible that the meteorological detail will
need to be more extensive. This would be especially important in situations
where the influence of complex terrain, sea breezes or valley channeling will
distort the local wind and atmospheric conditions to such an extent that the
Level I and II meteorological data will be unreliable and comparisons of
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dispersion and receptor models will be difficult. In addition, there will be
situations where the available meteorological stations are too distant or, for
various reasons, are not sufficiently representative of the study site. It is
important to note that although the mass balance for particulate matter
requires as little as one sample to estimate the percent contribution from
individual sources, the support data requirements are significant. Depending
upon the location of the site, more samples will have to be taken for various
meteorological conditions to assess the frequency and magnitude of sources
influencing an area.
The Level III approach to meteorological data acquisition will require
location of a meteorological station either at or in close proximity to the
sampling location. For example, in the New Jersey ATEOS study (Lioy and
Daisey, 1983) the monitoring sites were within 10 miles of an airport NWS site
which eliminated the necessity for a local meteorological station. It should
provide, at a minimum, the types of meteorological data available on the local
meteorological data summary provided by the NWS from airport sites located in
the United States. However it would be advantageous to also have data on the
mixing height and the wind profiles at altitudes above the surface. While
there are various means of obtaining such data, it would be preferable to
install an acoustic sounder at a suitable location. This device is versatile
and will provide computerized output of meteorological information on a real
time basis (continuous) throughout the day. Indirect measurements made by
doppler type acoustic sounders include the wind speed, wind direction,
vertical motion, standard deviation of vertical motion, horizontal components
of turbulence, and echo strength (proportional to the temperature structure of
the atmosphere). In addition, with the use of appropriate algorithms, the
echo strength can be converted to mixing height.
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These devices can be operated for long durations, as long as there is
proper maintenance and periodic review of the data. The mixing height and
wind parameter data from the acoustic sounder will in fact provide a useful
way to estimate the burden of atmospheric pollutants of local origin versus
the amount associated with the regional background pollutant concentrations
(Gaynor, 1977).
4.5.3 Ambient Data
4.5.3.1 Size Selective Sampling for Particulate Matter
As stated previously (Levels I and II) the majority of available data for
particulate matter has involved the total mass collected on a hi-vol or
dichotomous sampler. The hi-vol has a nominal 50 percent cut size of 30-40
]om which indicates that large quantities of coarse materials (perhaps from
fugitive dust sources) will be collected.
Over the period extending from 1973-1983, an increasing number of studies
has been conducted by various organizations in which size selective sampling
of the particulate mass has been completed and composition data reported. In
many cases, a dichotomous sampler with a 2.5 \x& and 15 pm 50 percent cut
size was used for fine (respirable) and coarse particles. This appeared to
help differentiate sources in the specific cases used for source apportionment
studies (Dzubay, 1980; Kneip, et al., 1983; and Dzubay, et al., 1983.)
However, size selective inlet samples (15 \aa cut size), multi-stage
impactors, two-stage cyclone filter samplers and various other techniques have
been used to collect particulate mass samples.
These samples are collected on filter media which are then processed by a
variety of techniques for elements, inorganic compounds and organic
compounds. An up-to-date review of filter media and the concerns for sampling
and analysis has been published by Lippman (1983).
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Unfortunately, an all purpose sampler has not been developed; however,
several observations can be made. If the intent of the studies to be
conducted is on sources of sulfate, total carbon or size fractions of the
particulate mass, the dichotomous sampler would be the common sampling device
used for air pollution measurements. However, if the concern is for species
which need large quantities of samples in order to be above the detection
limit, such as polycyclic aromatic hydrocarbons or other organic species, a
different sampler with the appropriate size selective inlet would be required
for the study.
4.5.3.2 Time Resolution
This aspect of sampling is always difficult to judge precisely. The
purist would want to have a continuous record or at least frequent samples in
a given day. The pragmatist must make a cost effective compromise. What is
actually necessary is good planning, based upon the type of the pollution
problem suspected as well as the modeling techniques to be used for the source
apportionment studies. Traditionally, 24-hour sampling periods have been
found to be adequate for measuring the particulate matter levels and long-term
variations and trends in an area.
In the case of 24-hour samples, a large number of samples (>50) are
required to conduct multivariate analyses. In addition, these would have to
be either spaced over the seasons of the year or conducted in the same season
over a number of years. For mass balance techniques, much fewer samples are
required because the mass balance analysis can be performed on a single
sample. The number of samples required is determined by the extent of the
need to assess the problem on a 24-hour or annual basis.
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Alternatively, to focus on one particular source or to examine diurnal
differences, a series of shorter duration samples could be taken (from one to
six hours in duration). Stricter requirements will then be necessary for the
meteorological support data, and appreciation of seasonal differences must be
noted in the sampling strategy. In addition, these short-term samples must
contain enough material (mass of particulate matter) to make is possible to
perform gravimetric and chemical analyses.
Finally, in all receptor modeling studies that have used catalogued source
profiles, the adequacy of those profiles for representing emission conditions
during the sampling period is always a serious consideration. Therefore, any
new source test should be done as close to the time of sampling as possible.
4.5.3.3 Composition Determination
The previous volumes in this series and other articles, including the
results of the Quail Roost Workshops I and II have discussed, for possible use
in receptor models, a number of species and their limits of detectability. In
Volume II of this series, much time was spent on species which should be
determined for the mass balance technique. However, the use of other
approaches including multivariate modeling and electromicroscopic identi-
fication would have different requirements. To compound this problem, the
Level III approach may deal with some very unusual problems. Therefore, the
selection of -the correct list of species for analysis may require substantial
information gathering studies prior to the development of an analytical
protocol.
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4.5.3.4 Prioritization of Chemical Analysis
After selection of the variable (dependent) to be apportioned, a variety
of chemical analyses will be completed on the collected samples. The optimum
situation would be analysis for all species associated with the sources that
potentially will affect the site. However, some caution must be exercised
since this approach may, in fact, ignore a source that was not identified in
Level I or Level II studies.
Prioritization of the analyses to be completed is a necessity for cost
controls, and those species selected must, in many ways, be tied to the
objectives of the Level III study.
The case of particulate mass sampling (total or size fractions) is a good
example from which to develop some general analytical guidelines.
• Farticulate Mass: Since this is the dependent variable in
receptor models, and the objective of source/dispersion models it
must be measured. In traditional studies, it was TSP; however,
this measurement could be of a particular size selected fraction.
(In other cases, the material to be apportioned could be
S04 "2, carbon, organic compounds, etc.).
• Trace Elements: A majority of the information that has been
obtained from previous receptor modeling has come from trace
element source signatures. These will be of great necessity in
any Level III study. In fact, these data will be required for
both source and ambient samples since size selected studies will
probably become more frequent in the future and relatively few
particle size fractionated source test results are available.
Methods for elemental analysis described in previous volumes
indicate that the multi-element approach (as opposed to unique
tracers) seems to be the most effective. For each study, the
method of choice must be critically evaluated to prevent the
exclusion of a major element from the analysis. Morphological
studies may also be necessary to further assist in source
identification (see Volume IV, U.S. EPA, 1983b, for details).
• Inorganic Ions: In most instances, except those where one source
dominates the collected particulate mass, various anions (and
their cations) will comprise significant portions of the
particulate mass. Therefore, the immediate thought is to
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incorporate them into a receptor modeling study. Before this is
done, however, the partitioning between primary emissions and
secondary products, and the fractionation according to particle
size must be considered. Differentiation will assist in local
versus distant source allocations. To accomplish the task, a
Level III study may require a large amount of ambient samples and
meteorological data for the development of multivariate models and
before such information can be used in a mass balance study.
Without partitioning of the local and regional contributions, the
primary value of the ion data will be for composition rather than
apportionment of sources. In the case of inorganic ions such as
sulfate, the combined use of dispersion and receptor models will
be advantageous in helping differentiate local contributions.
Organic Matter: There are a number of different levels from which
to approach the use of total organic matter or individual organic
compounds in a receptor model.
- Extractable Organic Matter (EOM): Depending upon the fraction
of EOM examined, the extractable mass data can give information
on primary organic material and potentially on secondary
organic material. These types of data are useful in further
defining the sources of the material affecting a receptor.
However, these data may in fact be more effectively used as the
dependent variable in an organic particulate matter source
apportionment model.
- Organic Carbon/Elemental Carbon/14C: These data are
available from previously described analytical techniques, and
can be used for assisting in the apportionment of particulate
mass in the atmospheric samples. Emission tests and ambient
sampling can be completed for carbonaceous material.
Therefore, if these data can be obtained in the same samples as
the trace elements, the ability to differentiate sources may be
extended. A particularly promising tracer is that of
14C/12C, which may be useful in identifying the
contribution of "contemporary" carbon sources, such as
woodburning in fireplaces and stoves.
As in the case of EOM, these materials can also be used as the
dependent variable in a receptor model if the appropriate
emission inventories are available for use in a mass balance
model. However, a thorough catalog of sources would not be a
requirement for use in a multivariate model apportionment study.
- Polycyclic Aromatic Hydrocarbons (PAH): Because PAHs found in
airborne particulate matter are the most carcinogenic component
in animal bioassays, they may present major concern to the
public health. However, for apportionment studies, they can be
used in three ways: 1} specific PAHs can be used as tracer
variables for combustion processes; 2) as the dependent
variable in a receptor model or 3) as an emissions term in a
dispersion model. Depending upon the type of data (source and
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ambient) either mass balance, factor analysis or multivariate
target transformation or multiple regression techniques can be
used to analyze these data in receptor models.
One caution which must be mentioned on this topic - the
partitioning of each PAH between the gaseous and particulate
phase must be considered before it is used in a receptor model
or dispersion model. In some cases, such as Benzo-a-pyrene
(BaP), this may be insignificant. However, for other compounds
a temperature dependence may be apparent and will affect the
validity of any apportionment conducted.
• Other Materials: Most of these may be available from routine
monitoring data, and can be useful either as the dependent
variable or as further qualitative information to define sources.
The primary species include: CO, SQz, N0«, Os and HC.
However, in the future, volatile organic compounds will probably
gain in importance from the point of view of toxic substances,
photochemical smog precursors, and as source signatures.
4.5.4 Procedures (Level III)
It is assumed that Level I and Level II and what has been discussed
previously in Level III is in place, or is available for use. In designing a
source/dispersion modeling study, one would select the monitoring network
which most closely reflects the majority of source-meteorological factors
(such as prevailing winds, sea breezes, etc.) that may be encountered in a
region. Thus, the maximum number of sites would be dependent on a coverage
factor for the area source, frequency of meteorological conditions and
strength of source.
Unfortunately, the case is not as simple for a receptor site which would
be an impact site for individual sources that are examined by the dispersion
model. Such a site can be influenced by many different sources depending upon
the season or daily meteorological conditions. A major concern would be
coverage by secondary sites surrounding the primary receptor site. Sometimes,
if a large urban area is to be studied, more than one receptor site could be
chosen since each may be representative of different activities of the area.
For instance, the sites could be distributed in downtown commercial.
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residential, or industrial areas. Obviously, in each case, a different aspect
of an air pollution problem will be examined and the final selection of the
number of sites would be dependent upon the overall goals of the
investigation. For instance, the sites selected for examining TSP or some
size fraction in an urban nonattainment area might not necessarily be
equivalent to a site where there is a potential impact of hazardous materials
in a residential section. For a TSP or PM10 study, two or three sites at
different locations may be warranted. In the latter case, one or two sites
may be used as the receptors in the same neighborhood.
Once the primary site or sites are selected, of utmost importance is the
selection of a rural site located on level terrain away from significant local
sources and meteorological perturbations. These sites must provide coverage
for the dominant wind directions; however, in many cases, at least two
"upwind" sites, i.e., along the dominant wind paths, should be required
although anywhere from 1 to 4 sites are conceivable. Under no circumstances
should the study be done without at least one background site.
A primary consideration at the rural sites is the travel time and
technician time needed to obtain the samples used for determining regional or
background levels of pollutants. Obviously, the major species should be
measured as should the major tracers. Beyond that, it depends upon the
availability of resources. The optimum situation would include completion of
the same set of analyses on the background and urban samples (receptor).
The sampling duration and sampling frequency will have been determined
prior to site selection; however, there should be some flexibility in the
design. It may be necessary to adjust the sampling schedule if a major event
occurs, unexpected results begin to appear, or unanticipated problems arise.
Crucial in all these studies is the development of an adequate sampling and
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analysis quality assurance program. Without this type of control on the
study, the results may be less than adequate for use in model development. In
essence, error terms must be minimized. EPA guidance on the preparation of
quality assurance plans is contained in a document entitled Interim Guidelines
and Specifications for Preparing Quality Assurance Project Plans, (U.S. EPA,
1980c).
The final aspect of this study design is the model development and a
number of a priori issues must be considered. At a minimum for dispersion
model validations, the cases selected require short duration samples and the
periods chosen should adequately reflect the emissions inventory or operating
parameters that could be in effect at that time. Thus, for example, the
analytical protocols could involve the following steps:
• Dispersion models are to be run using the source emission
inventories developed in Level III.
Selection of meteorological parameters corresponding to those
observed during the sampling study.
Selection of emission conditions which would closely
approximate the emission strengths that occurred during the
sampling period.
• Complete a mass balance or microscopic analysis on a particular
sampling day (or on days selected to represent an annual average).
Select a sampling day during which the source is more nearly
upwind of the receptor site.
- Complete mass balance or microscopic analysis on that day and
estimate mass contributions.
• Compare the dispersion model source contributions with those of
the receptor model. Those contributions estimated by each model
which do not differ by more than the sum of their uncertainties
are consistent. This consistency provides a good basis for
justifying a control strategy applied to these sources. The
dispersion model may be re-run for individual sources within one
of these source categories to specify which one might result in
the greatest pollution reduction per dollar spent.
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• If source and receptor model results differ by more than the sum
of their uncertainties, the data on which both operate need to be
refined. The nature of this refinement is often indicated by the
model results.
- If the receptor model identifies a source contribution which
wasn't included in the emission inventory for the source
model, an inventory for that source must be carried out.
Wood burning (Core, et al., 1982) was discovered as a major
source by this procedure.
- If the receptor model finds a "downwind" source to be a major
contributor, meteorological inputs to the source model should
be examined and improved.
If all source type contributions calculated from the source
model are high or low with respect to the receptor model, the
source model should be re-run with revised or corrected
stability and mixing height data.
If model calculations differ by a factor or two or more, the
emission factors and positions of the gridded emissions
inventory for the source model should be double checked.
Alternatively, additional source characterization tests for
the receptor model may need to be made. By confining these
activities to major contributors with major discrepancies,
the work can be focused with great savings of effort over
verifying all source emission rates and compositions.
• When the data have been refined, both models should be re-run and
the two preceding bulletad steps should be repeated.
• If comparisons are still inadequate for decision-making purposes,
and if the cost of a wrong decision merits it, artificial tracers
may be introduced into the questionable emissions, simultaneous
measurements may be made at both the source and receptors, and
source and receptor models may be re-run to identify where the
discrepancy lies. The basic assumptions and structure of the
models may have to be modified as a result.
Concurrently, the mass balance results should be intercompared with
studies that use any one of a number of microscopic techniques. Comparisons
with other mathematical techniques such as factor analysis or multiple
regression, or combinations o£ techniques such as target transformation factor
analysis, or factor analysis with multiple regression, may be useful.
Conversely, if microscopic methods are the primary receptor models used, they
should be intercompared with mathematical methods. In these cases, the
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sources' contributions to a percentage of the mass will be determined. The
results must be compared for consistency and errors.
4.5.5 Resources
Resource requirements for level III are large. A mini- or main frame
computer will probably be needed to store data files and to operate
comprehensive programs. Since new sampling programs will be conducted, the
requirements for monitoring equipment, personnel and laboratory analyses will
be sizeable. Manpower requirements may encompass man-years and study
durations can extend to a year or more. Costs will range from $100,000 to
over $1,000,000.
4.6 Summary of the Three Level Approach
Studies conducted to determine contributions to measured particulate
matter concentrations can require different levels of effort. The level of
effort required in each case will depend on the nature and extent of the
problem and on the amount of information already available. This volume
organizes the analyses used to solve source apportionment problems into a
three level format. Table 4-8 summarizes the various aspects of the three
levels of study. The three level format is flexible and the information in
Table 4-8 should not be rigidly confined to each study level. In real world
applications, there may be intermixing of components of the three levels due
to data and resource availability.
Two items are applicable to all three study levels. First, an analysis of
background concentrations is an essential preliminary step to the other
analyses in each level, and second, two or more source apportionment methods
should be employed, if possible, at each level in order to provide greater
confidence in the study results.
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5.0 CASE STUDIES OF COMPOSITE SOURCE APPORTIONMENT METHODS
There are a significant number of examples where composite source
apportionment methods have been used effectively to answer questions on the
degree to which various source categories cause a nonattainment situation or
other air quality situation of interest. These studies cover the full
spectrum of complexity from Level I to Level III, Since the dividing lines
between the three levels of complexity are not clear cut, it follows that many
of the case studies may contain analyses that might logically be part of more
4
than one complexity level.
The following case studies exemplify the three levels of complexity of
composite source apportionment methods.
5.1 Use of Microscopy and Filter Analysis (Level I)
Drafts, et al., (I960) of the Illinois Institute of Technology Research
Institute (IITRI) have developed a protocol using microscopic identification
of particles collected on hi-vol filters supplemented by chemical analyses of
the filter deposits to apportion the collected particles to various source
categories.
Polarized light microscopy is the principal tool used to identify particle
sources based upon the previous microscopic analysis of particle samples
collected from various sources. X-ray diffraction may also be used to provide
positive identification of mineral species and scanning electron microscopy
may be used for identification of extremely small particles.
Low temperature ashing is used to determine the organic content of the
collected particles. Ion chromatography is used to determine the anions and
cations present, and depending on the nature of the study, elemental analysis
of samples may be carried out using x-ray fluorescence.
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Data are presented in terms of percent contribution of source categories
such as combustion, specific metallurgical operations, road traffic, and
biological aerosols.
5.2 Linn County, Iowa Non-Traditional Fugitive Dust Study (Level I)
A recently conducted study by Brookman (U.S. EPA, 1983d) investigated the
primary non-traditional sources affecting the air quality in and around Cedar
Rapids, Iowa (Linn County). The approach used for this study was,
essentially, the Level I protocol involving many of the analysis discussed in
Section 2.3.7.
The data base used for this study included existing source, meteorological
and ambient data. The source data were in the form of existing
microinventories around the five county monitoring stations and one rural
background station, a point source inventory performed by the local agency,
and a "first-cut" area source inventory (part of this study was to update this
inventory and then use the results for the nonattainment study). The source
data were supplemented with topographic maps, aerial photographs, and an
industrial fugitive source inventory (performed as part of the project). The
meteorological data were obtained from the Cedar Rapids Airport in the
standard form of LCD's (Local Climatological Data Summaries). The ambient
data were obtained from the State authorities and consisted of seven years
(1976 - 1982) of hi-vol data collected at the one background and five county
stations.
The first step of the protocol was to visit the monitoring sites to get a
"feel" for local contributing sources, topographic anomalies, siting
characteristics, etc. The second step was to evaluate the meteorological
data. This included determining wind persistence for each of the sampling
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days during the seven year period and noting precipitation and snow cover on
and preceding the sampling days. The third step was to determine the
background level for each year of the study period. This was done by using
the rural station hi-vol data. This level was found to be highly variable
from year to year, in direct correlation with the amount of precipitation for
that year (in fact, a linear relationship was shown to exist).
The next step in the protocol was to perform a trend analysis (see Section
2.3.7) and remove the background influence (thus effectively removing the
meteorological influence). The resulting trend plots for the five stations
showed definite anomalies at certain stations during certain years. Further
analyses were performed to try and discover the reasons for these anomalies.
First, the modeled point source influences were removed from the yearly
averages (the State has performed ISC modeling and information was available
on the relative monitor impact for each year). Second, pollution roses were
developed based on days with persistence greater than 0.71 (see Section
2.3.7). A pollution rose was also developed for the background station and
its influence was removed from the county station pollution roses.
Using the results of these basic analyses in conjunction with the
topographic maps, aerial photographs, site visits, discussions with County
officials, etc., probable causes for the nonattainment status of several
monitoring stations were uncovered. The principal findings of the study
showed that highway construction was the principal cause of TSP violations at
several stations throughout the years, industrial fugitive sources contributed
several micrograms per cubic meter to a few stations, and vehicle traffic on
paved and unpaved roads contributed heavily to most of the stations. The
results also showed that there was a general downward trend in the TSP levels
throughout the county throughout the years as a result of control programs
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(street cleaning, paving) and the economic recession (several large industries
had cut back their operations significantly).
The results of this study, which was conducted entirely using existing
data, were very helpful to the local authorities in planning future control
strategies (particularly in regard to construction projects). Example results
for one of the five monitoring sites are shown in Table 5-1. While the Level
I approach could not determine exact levels of source contributions or
distinguish between several fugitive sources in the same area that were
probably affecting a monitoring station, it did provide the authorities with
all of the information they desired. The next logical step for further source
contribution quantification would have to entail microscopic analyses of
filters and/or directional sampling.
TABLE 5-1
ESTIMATED SOURCE IMPACTS AT ONE LINN COUNTY, IOWA
MONITORING LOCATION (EQUIVALENT GEOMETRIC MEANS ug/m3)
Year
Source Type
Background
Traditional:
Stack
Fuel Combustion
Solid Waste Disposal
Auto Exhaust
Annual Recorded Mean
Non-Traditional Impact
1976
47.0
8.3
1.0
2.1
2.7
98.3
37.2
1977
38.6
8.8
1.0
2.1
2.7
109.3
56.1
5.3 Allegheny County Particulate Study
1978
37.9
3.3
1.0
2.0
2.7
90.6
38.2
(Levels
1979
35.8
3.8
1.0
1.8
2.7
95.0
44.9
I and
1980
40.7
3.3
1.0
1.7
2.8
106.5
51.5
ID
1981
36.5
3.3
1.0
1.5
2.8
80.0
29.4
1982
26.0
3.8
1.0
1.4
2.8
60.9
20.9
A study was carried out in Allegheny County (Yocom, et al., 1979, and
Brookman and Yocom, 1980) to investigate the particulate matter nonattainment
problem in the county and to determine the types of sources primarily
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responsible for the nonattainment situation. Of particular interest were the
relative contributions of the following types of sources:
• Traditional - Industrial point and process fugitive sources.
• Non-Traditional - In-plant open sources such as roads and material
piles and non-industrial sources such as public roads and
playgrounds.
• Background - Natural sources and sources outside the county.
This study was an example of a composite source apportionment method
applied principally to available TSP data collected by the County Bureau of
Air Pollution Control. Most of the methods described in Section 2.3.7
(Preliminary or Qualitative Receptor Models) were applied to this 'data base.
Dispersion modeling using a version of PAL together with a detailed inventory
of traditional and non-traditional sources at the large, integrated steel
mills was used to predict specific contributions of these sources on selected
sampling sites.
Background levels of particulate matter were determined by the "composite
particulate rose" method described in Section 2.3.7 and applied equally to all
sampling sites in the county.
The assumption was made that the industrial component of measured
particulate matter at stations near the steel mills was made up entirely of
steel mill contributions. These stations constituted most of the
nonattainment stations. A variety of techniques (e.g., pollution roses,
wet/dry day analysis, microinventories, etc.) was used to infer the relative
contribution of traditional and non-traditional sources for the remaining
sampling stations in the county.
Special short-term sampling runs using Millipore filters were carried out
at several of the sampling sites under predetermined meteorological
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conditions. These samples were subjected to automated scanning electron
microscopic and energy dispersive x-ray analysis (SEM/EDAX) to determine the
chemistry of the selected particles in relation to particle size. By making
certain assumptions on the contributions of traditional and non-traditional
sources to specific particle chemistries it was possible to reconcile the
dispersion modeling-plus-background source apportionment results with those of
the particle identification studies.
Using estimated predictions of future reductions in emissions from
traditional and non-traditional industrial sources, the number of nonattaining
stations decreased and strategies could be developed for bringing the
remaining nonattaining stations into an attainment status by application of
controls on various categories of non-traditional sources.
The actual contracted cost for this • study was about $60,000 in 1978
dollars. However it should be pointed out that the contractor served as a
consultant and management contractor and was asked to utilize much available
data and systems. One important feature was that 5 years of TSP and
meteorological data were already up on a large computer and were readily
accessible for a variety of manipulations. Furthermore, the dispersion
modeling and steel mill inventory were carried out by another contractor under
the sponsorship of a steel company consortium, and the special sampling and
SEM/EDAX work was funded directly by the county with yet another contractor
group. Thus the total costs for the project were estimated to be in the range
of $300-500,000.
5.4 Portland Aerosol Characterization Study (Level III)
The Portland Aerosol Characterization Study (PACS, Cooper, et al., 1979;
Watson, 1979; Core, et al., 1982) is a classic example of a Level III study.
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Its purpose was to quantify the major contributions to TSP concentrations
which were in violation of the NAAQS. It involved the following elements:
• Creation and use of a site specific air quality dispersion model
(Pabrick and Sklarew, 1975).
• Multiple site meteorological monitoring to create a complex wind
field.
• A site specific gridded emissions inventory for one year.
• A comprehensive study design and preparation (Mueller, et al.,
1977).
• Meteorological regime stratification of sampling schedules to
minimize costs while still representing an entire year.
• Characterization of all major source emissions with respect to
particle size and chemical composition.
• Time, space, size, and chemical resolution of ambient suspended
particulate matter.
• Identification and quantification of major sources by the mass
balance receptor model.
• Comparison of mass balance and dispersion model results and
adjustments to each.
One of the unique aspects of the PACS was the interaction of the source
and receptor models to reinforce each other. The emissions inventory created
for the dispersion model was used to identify the most likely sources for
emissions characterization. By its estimates of likely ambient concentrations
and variability with wind direction, it allowed necessary analytical
requirements (detection limits) and sample durations to be estimated.
Initially, mass balance calculations did not agree with dispersion model
results. An examination of the emissions inventory resulted in corrections
and results of the two models did agree within reasonable margins for error
(Core, et al., 1982). Control strategy options were outlined which probably
would not have been proposed without the results of the PACS. Several of
these options are being implemented and it appears that Portland's air quality
is improving.
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Because of the in-kind donations and development work which went into the
PACS, it is difficult to estimate its total cost. Estimates range from
$300,000 to $1,000,000. The cost of misplaced controls is agreed to have been
much higher than the upper limit of the cost of the study.
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6.0 SUMMARY AND CONCLUSIONS
6.1 Summary
It should be apparent from this technical document that, considering the
state-of-the-art of receptor and source (dispersion) modeling and composite
source apportionment methods, there are no fool-proof step-by-step procedures
or recipes for studying particulate matter apportionment and making decisions
on the results. Nevertheless, there are a large number of useful tools to
assist the air resource manager in carrying out such studies. The selection
and application of these tools will depend on:
Objectives of the study.
Resources available.
Technical expertise and innovativeness of the project team.
Availability and accessibility of data bases.
Availability of sophisticated sampling and analytical equipment.
Some of the techniques described are well within the resources of even
small agencies (e.g., the methods described in Section 2.3.7 - Preliminary or
Qualitative Receptor Models) while others require highly trained statisticians
and computers of significant size (see Section 2.3.2 - Factor Analysis) and
meteorologists with considerable modeling experience (the ISC model described
in Section 2.2.2).
It should be clear, however, that in apportioning source contributions in
ambient particulate matter studies no single method (e.g., dispersion modeling
or a specific receptor model) will ordinarily suffice. Invariably some sort
of a composite methodology will be needed.
It has been reasonably well established that most dispersion models when
properly applied will predict source impact at a receptor within a factor of
two of the correct or measured value. As was pointed out earlier, the mass
balance model can apportion generalized source categories within this same
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range. If one can assume that a dispersion model operating on several sources
and a single receptor is a more accurate representation of relative
contribution than of total impact, it follows that combining the dispersion
and receptor models or two receptor models will produce a much more accurate
representation of source contributions than use of each independently. While
it is easy for anyone working in this area to believe this statement, the
state of technology is such that it is not possible to put error bars on the
results of any application of composite methods. Suffice it to say that the
results would be better than the use of a single method. The same is true for
the combined use of two receptor models.
6.2 Conclusions
An analysis of source contributions to particulate matter levels at one or
more receptor locations should start the analysis with elements of the Level I
approach (Section 4.3). The air resource specialist responsible for the
source apportionment study would need first to become intimately familiar with
available data on particulate matter, their trends, the adequacy of the
sampling array and the influence of basic meteorological factors as outlined
in Section 2.3.7. Furthermore, if an adequate emissions inventory for
particulate matter sources did not exist, preparing such an inventory would be
a first order of business. The same can be said for the assembly of existing
meteorological data.
In progressing to higher levels and more complex and costly approaches,
the analysts must carefully weigh the objectives of the analysis and the cost
effectiveness of more sophisticated techniques. If the source contribution is
needed in only general terms or, more likely, if the sources of nonattainment
are relatively straightforward, the techniques of a Level I approach may be
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adequate. If, on the other hand, specific contributions of selected sources
in a complex airshed are needed for control strategy development on a size
selective basis, and where emission controls will be costly, a Level II or III
approach will be needed. The cost range between Level I and III approaches
can vary by more than an order of magnitude (e.g., $10,000 to $500,000).
The application of composite source apportionment techniques is a
stepwise, and to some extent, an iterative process where the effectiveness of
each added technique is evaluated in terms of increasing the accuracy of the
results in relation to the added time and costs.
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Washington, D.C.
Willard, H.H., L.L. Merritt, and J.A. Dean, 1974: Instrumental Methods
of Analysis. 5th Edition. D. Van Norstrand Company.
Yocom, J.E., E.T. Brookman, and B.I. Raffle, 1979: Strategies for
Control of Particulate Matter in Allegheny County. Final Report to
Allegheny County Bureau of Air Pollution Control, TRC Environmental
Consultants, Inc.
-158-
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APPENDIX A
RESULTS OF SELECTED MODEL VERIFICATION AND EVALUATION STUDIES
PERTAINING TO THE 6 ASSUMPTIONS EMPLOYED BY THE MASS BALANCE MODEL
-------
APPENDIX A
RESULTS OP SELECTED MODEL VERIFICATION AND EVALUATION STUDIES PERTAINING
TO THE 6 ASSUMPTIONS EMPLOYED BY THE MASS BALANCE MODEL
1. Source Composition
Source compositions will vary systematically and randomly. This
*
variability is caused by 1) transformations with transport time between source
and receptor (an example is the volatilization of bromine in auto exhaust;
e.g., Bowman, et al., 1974), 2) differences in the fuel type or operating
processes between similar sources or of the same source in time (an example is
the review of lead concentrations in auto exhaust from different automobiles
using different fuels by Pierson and Brachaczek, 1976), and 3) uncertainties
of the measurement process.
Watson (1979) and Currie, et al., (1983) introduced systematic errors into
source compositions to evaluate the effects on source contribution
calculations and found:
• The error in the estimated source contributions due to biases
(i.e., proportional increases or decreases) in the source
compositions is in direct proportion to the magnitude of that bias
(Watson, 1979).
• For random errors associated with estimates of average source
compositions, the magnitude of the source contribution errors
decreases as the number of components increases (Watson, 1979).
• In comparing a number of mass balance solutions on simulated data
in which random errors and biases were introduced to the source
compositions, Currie, et al., (1983) found "Only the effective
variance [solution] explicitly included the effect of source
profile uncertainties, but these were treated as random rather
than as systematic error components."
A-l
-------
2. Components Add Linearly
No studies have been performed to evaluate deviations from this
assumption. While the deviations from this assumption are generally
considered to be small, the conversion of gases to particles and
reactions between particles are not inherently linear processes (e.g.,
Priedlander, 1977).
3. All Sources Have Been Identified
Watson (1979) systematically increased the number of sources
contributing to his simulated data from 4 to 8 contributors while solving
the mass balance equations assuming only four sources. He also examined
the case of including more sources in the least squares solution than
those which actually contribute. The results wera:
• Underestimating the number of sources has little effect on the
calculated source contributions if the prominant properties
contributed by the source are excluded from the solution.
• When the number of sources is underestimated and when prominent
properties of the omitted sources are included in the calculation
of source contributions, the contributions of sources with
properties in common with the omitted sources are overestimated.
* When source types which are actually present are excluded from the
effective variance least squares solution, ratios of calculated to
measured concentrations are often outside of the 0.5 to 2.0 range,
and the sum of the source contributions is much less than the
total measured mass. The low calculated/measured ratios indicate
which source compositions should be included.
• When the number of sources is overestimated, the sources which are
not actually present yield contributions less than their
precisions if their properties are significantly distinct from
other sources. The over-specification of sources decreases the
precisions of the true source contribution estimates.
A-2
-------
4. Number of Sources Less Than Number of Components
It is likely that the number of individual sources contributing to
receptor concentrations is much larger than the number of properties which can
be measured. It is therefore necessary to group sources into source types of
similar compositions such that this assumption is met. For most commonly
measured aerosol properties, meeting Assumption 5 practically defines these
groupings. Henry (1977) speculates that the linear programming solution might
work if the number of sources is greater than the number of components, but he
offers no proof. None of the least squares solutions will work if this
assumption is not met.
5. Compositions are Linearly Independent
Watson (1979) examined effects of deviations from this assumption with
simulated data while Gordon, et al., (1981) studied it with ambient
measurements. Henry (1982) has devised an analytical method of determining
the degree of linear dependence in typical mass balance applications for which
only a few tests are available. His algorithm could be incorporated into the
routine mass balance model to identify linear independence at given precision
levels for all combinations of sources and components. The results of these
studies show:
• With most commonly measured components (i.e., ions, elements,, and
carbon) and source types (e.g., motor vehicle, crustal, residual
oil, sea salt, steel production, wood burning, and various
industrial processes), from five to seven sources are linearly
independent of each other. Some of the source compositions used
in the solution may be better expressed as linear combinations of
measured source compositions (Henry, 1982).
• Henry (1982) determined the modified source compositions which
would give the same results as a ridge regression solution and
noted that: "The apparent ability of ridge regression to solve
the multicollinearity problem is seen to be based on implicitly
changing the [mass balance] problem to one which is not physically
meaningful."
A-3
-------
• Gordon, et al., (1981) found instabilities in the ordinary
weighted least squares solutions when they removed elemental
concentrations which were known to be unique to certain source
types. Using simulated data with known perturbations of 0 percent
to 20 percent, Watson (1979) found: "In the presence of likely
uncertainties, sources such as urban dust and continental
background dust cannot be adequately resolved by least squares
fitting, even though their compositions are not identical.
Several nearly unique ratios must exist for good separation.
6. Measurement Errors Random and Uncorrelated
These least squares solutions methods are derived from maximum likelihood
theory which requires this assumption. In reality, we 'know very little about
the distribution of errors for the source compositions and the ambient
concentrations. For small errors (i.e. <10 percent) the actual distribution
may not be important, but for large errors it probably is; a symmetric
distribution becomes less probable as the coefficient of variation of the
measurement increases. Though evaluation of deviation from this assumption
could be undertaken by generating simulated data perturbed by random numbers
drawn from lognonnal, uniform, and other distributions, these tests have never
been performed.
A-4
-------
APPENDIX B
SOURCE COMPOSITION REFERENCES
-------
APPENDIX B
SOURCE COMPOSITION REFERENCES
Air Conditioners;
Buchnea, D. & Buchnea, A. (1974) "Air Pollution by Aluminum Compounds
Resulting From Corrosion of Air Conditioners". ES&T, 8, 752.
Animal Feed and Wastes:
Capar, S.G., Tanner, J.T., Friedman, M.H., and Boyer, K.W. (1978)
"Multielement Analysis of Animal Feed, Animal Wastes, and Sewage Sludge".
ES&T, 12, 785.
Agricultural Burning;
Core, J.E. and Terraglio, F.P. (1978) "Field and Slash Burning Particulate
Characterization: The Search for Unique Natural Tracers". Annual Meeting of
Pacific Northwest International Section of APCA, Portland, Oregon.
Shum, Y.S. and Loveland, W.D. (1974) "Atmospheric Trace Element Concentrations
Associated with Agricultural Field Burning in the Willamette Valley of
Oregon". Atmospheric Environment, £, 645.
Auto Exhaust:
Ganley, J.T. and Springer, G.S. (1974) "Physical and Chemical Characteristics
of Particulates in Spark Ignition Engine Exhaust". ES&T, 8, 340.
Gillette, D.A. and Winchester, J.W. (1972) "A Study of Aging of Lead Aerosols
- - I". Atmospheric Environment, (5, 443,
Gillette, D.A. (1972) "A Study of Aging of Lead Aerosols — II". A Numerical
Model Simulating Coagulation and Sedimentation of a Leaded Aerosol in the
Presence of an Unleaded Background Aerosol". Atmospheric Environment, 6, 451.
Habibi, K. (1973) "Characterization of Particulate Matter in Vehicle
Exhausts". ES&T, 7, 223.
Harrison, R.M. and Sturges, W.T. (1983) "The Measurement and Interpretation of
Br/Pb Ratios in Airborne Particles". Atmospheric Environment, 17, 311.
Hasanen, E., et al. "Benzene, Toluene and Xylene Concentrations in Car
Exhausts and in City Air". Atmospheric Environment, 15, 1755.
Hirschler, D.A. and Gilbert, L.F. (1964) "Nature of Lead in Automobile
Exhaust". Archives of Environmental Health, 8, 297.
Hirschler, D.A. et al. (1957) "Particulate Lead Compounds in Automobile
Exhaust Gas". Industrial and Engineering Chemistry, 49, 1131.
Holiday, E.P. and Parkinson, M.C. (1978). "Another Look at the Effects of
Manganese Fuel Additive (MMT) on Automobile Emission". APCA Meeting Houston.
B-l
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SOURCE COMPOSITION REFERENCES (continued)
Larsen, R.I. (1966) "Air Pollution from Motor Vehicles". Annals of the New
York Academy of Sciences, 136, 275.
McKee, H.C. (1970) "Discussion: Characterization of Particulate Lead in
Vehicle Exhaust: Experimental Techniques". ES&T, 4, 253.
McKee, H.C. and McMahan, W.A. (1960) "Automobile Exhaust Particulates - Source
and Variation". JAPCA, 10, 456.
Moran, J.B. et al. (1972) "Effect of Fuel Additives on the Chemical and
Physical Characteristics of Particulate Emissions in Automotive Exhaust".
U.S. EPA, Report EPA-R2-72-066.
Nielsen, T. (1979) "Determination of Polycyclic Aromatic Hydrocarbons in
Automobile Exhaust by Means of High-Performance Liquid Chromatography with
Fluoresence Detection". Journal of Chromatography, 170, 147.
Ninomeya, J.S. et al. (1970) "Automotive Particulate Emissions". Second
International Clean Air Conference, p. 663.
Ondov, J.M. et al. (1982) "Trace Element Emissions on Aerosols". ES&T, 16,
318.
Pierson, W.R. and Brachaczek, W.W. (1976) "Particulate Matter Associated with
Vehicles on the Road". SAE Automotive Engineering Congress and Exposition,
Detroit, Mich., Feb 23-27, 1976.
Pierson, W.R. et al. (1978) "Methylcyclopentadionyl Manganese Tricarbonyl
Effect on Manganese Emissions from Vehicles on the Road". JAPCA, 28.
Sampson, K.E. and Springer, G.S. (1973) "Effects of Exhaust Gas Temperature
and Fuel Composition on Particulate Emission from Spark Ignition Engines".
ES&T, 7, 55.
Ter Haar, G.L. and Boyard, M.A. (1971) "Composition of Airborne Lead
Particles" Nature, 232, 553.
Ter Haar, G.L. et al. (1972) "Composition, Size and Control of Automotive
Exhaust Particulates". JAPCA, 22, 39.
Von Lehmden, D.J. et al. (1974) "Determination of Trace Elements in Coal, Fly
Ash, Fuel Oil and Gasoline A Preliminary Comparison of Selected Analytical
Techniques". Analytical Chemistry, 46, 239.
Wilson, W.E. et al. (1973) "The Effect of Fuel Composition on Atmospheric
Aerosol Due to Auto Exhaust". JAPCA, 23, 949.
Carbon Black;
Serth, R.W. and Hughes, T.W. (1980) "Polycyclic Organic Matter (POM) and Trace
Element Contents of Carbon Black Vent Gas". ES&T, 14, 298.
B-2
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SOURCE COMPOSITION REFERENCES (continued)
Cement Production;
Cooper, J.A. at al. (1976) "Analysis of Portland Cement, Clinker, Raw Mix, and
Associated Ceramic Materials Using an Energy Dispersive X-Ray Fluorescence
Analyzer With Inter-Element Corrections". Advances in X-Ray Analysis, Vol.
19, Dubuque, Iowa, Kindall/Hunt Publishing Co.
Coal-Fired Boilers:
Bennett, R.L. and Knapp, K.T. (1978) "Sulfur and Trace Metal Emissions From
Combustion Sources". April 24-26, Southern Pines, N.C. Workshop on
Measurement Technology and Characterization of Primary Sulfur Oxides Emission
from Combustion Sources.
Block, Chantal and Dams, R. (1976) "Study of Fly Ash Emission During
Combustion of Coal". ES&T, 10, 1011.
Coles, D.G. et al. (1979) "Chemical Studies of Stack Fly Ash from a Coal-Fired
Power Plant" Environmental Research and Technology, 13, 455.
Conway, R.P. (1980) Chemical and Physical Characterization of Sulfates
Associated with Coal Fly Ash, Ph.D. Dissertation, Colorado State University,
Ft. Collins, CO.
Fisher, G.L. et al. (1978) "Physical and Morphological Studies of
Size-Classified Coal Fly Ash". ES&T, 12, 447.
Germani, M.S. (1980) Selected Studies of Four High Temperature Air Pollution
Sources Ph.D. Dissertation, Univ. of Maryland, College Park, MD.
Gladney, E.S. et al. (1976) "Composition and Size Distribution of In-Stack
Particulate Material at a Coal-Fired Power Plant". Atmospheric Environment,
10, 1071.
Homolya, J.B. and Cheney, J.L. (1978) "An Assessment of Sulfuric Acid and
Sulfate Emissions from the Combustion Fossil Fuels". Workshop Proceedings on
Pjcimary Sulfate Emissions from Combustion Sources Volume 2, Characterizations.
EPA Publication 600/9-78-036.
Klein, U.K. et al. (1975) "Pathways of Thirty-Seven Trace Elements Through a
Coal-Fired Power Plant". ES&T, 9, 973.
Nadkarni, R.A. (1975) "Multielement Analysis of Coal and Coal Fly Ash
Standards by Instrumental Neutron Activation Analysis". Radiochemical and
Radioanalysis Letters, 21, 161.
Ondov, J.M. et al. (1979) "Emissions and Particle Size Distributions of Minor
and Trace Elements at Two Western Coal-Fired Power Plants Equipped with
Cold-Side Electrostatic Precipitators". ES&T, 13, 947.
B-3
-------
SOURCE COMPOSITION REFERENCES (continued)
Ondov, J.M. et al. (1981) "Elemental Composition of Atmospheric Fine Particles
Emitted from Coal Burned in a Modern Electric Power Plant Equipped with a Flue
Gas Desulfurization System". Atmospheric Aerosol, Source/Air Quality
Relationships, edited by E.S. Macios and P.K. Hopke, American Chemical Society
Symposium Series 167, Washington, D.C.
Richards L.W. (1981) "The Chemistry Aerosol Physics, and Optical Properties of
a Western Coal-Fired Power Plant Plume". Atmospheric Environment, 15, 2111.
Small, J.A. (1976) An Elemental and Morphological Characterization of the
Emissions from the Dickerson and Chalk Point Coal-Fired Power Plants Ph.D.
Dissertation, University of Maryland, College Park, MD.
Small, M. (1979) Composition of Particulate Trace Elements in Plumes from
Industrial Sources Ph.D. Dissertation, University of Maryland, College Park,
MD.
Small, M. et al. (1981) "Airborne Plume Study of Emissions from the Processing
of Copper Ores in Southeastern Arizona". SS&T, 15, 293.
Smith, R.D. et al (1979) "Concentration Dependence Upon Particle Size of
Volatized Elements in Fly Ash". ES&T, 13, 553.
Surprenant, N.F. (1981) "Emissions Assessment of Conventional Stationary
Combustion Systems: Volume IV: Industrial Combustion Sources".
EPA-60Q/7-81-003C, Research Triangle Park, NC.
Taylor, D.D. and Flanagan, R.C. (1980) "Aerosols from a Laboratory Pulverized
Coal Combustor". Atmospheric Aerosol: Source/Air Quality Relationships,
edited by E.S. Kacias and P.K. Hopke, American Chemical Society Symposium
Series, No. 167, Washington, D.C.
Ulrich, G.D. (1976) "An Investigation of the Mechanism of Fly-Ash Formulation
in Coal-Fired Utility Boilers". U.S.-ERDA Report FE-2205-1.
Coke Ovens
Barret, R.E. et al. (1977) "Sampling and Analysis of Coke Oven Door
Emissions". EPA-600/2-77-213, Research Triangle Park, NC.
Copper Smelters
Germani, M.S. (1980) Selected Studies of Four High Temperature Air Pollution
Sources Ph.D. Dissertation, University of Maryland, College Park, MD.
Schwitzgebel, K. et al. (1978) "Trace Element Study at a Primary Copper
Smelter". EPA-600/2-78-065a, Research Triangle Park, NC.
Small M. (1979) Composition of Particulats Trace Elements in Plumes from
Industrial Sources Ph.D. Dissertation, University of Maryland, College Park,
MD.
B-4
-------
SOURCE COMPOSITION REFERENCES (continued)
Zoller, W.H. et al. (1978) "Atmospheric Trace Elements Emissions from Copper
Smelters". Presented at Division of Environmental Chemistry, American
Chemical Society, Miami, Fla.
Cotton Gin:
Lee, R.E. (1975) "Concentration and Size of Trace Metal Emissions from a Power
Plant, a Steel Plant, and a Cotton Gin". SS&T, 9, 543.
Diesel Exhaust:
Prey, J.W. and Corn, M. (1967) "Physical and Chemical Characteristics of
Particulates in a Diesel Exhaust". American Industrial Hygiene Association
Journal, 28, 468.
Schreck, R.M. et al (1978) "Characterization of Diesel Exhaust Particulate for
Mutagenic Testing". APCA Meeting, Houston.
Yergey, J.A. (1981) Chemical Characterization of Organic Adsorbates on Diesel
Particulate Matter, Ph.D. Dissertation, Pennsylvania State University.
NTIS (1982) "Diesel Exhaust Emissions. 1974-May, 1982". (Citations from the
American Petroleum Institute Data Base).
Glass Production;
Mamuro, T. et al. (1979) "Elemental Compositions of Suspended Particles
Released in Glass Manufacture". Annual Report of the Radiation Center of
Osaka Prefecture, 20. 29.
Jet Aircraft Exhaust:
Fordyce, J.S. and Shebley (1975) "Estimate of Contribution of Jet Aircraft
Operations to Trace Element Concentrations at or Near Airports". JAPCA, 25,
721.
Mamuno, T. et al. (1973) "Activation Analysis of Particulates Emitted from
Aircraft Jet Engines". Annual Report of the Radiation Center of Osaka
Prefecture, 14, 7.
Kraft Paper Mills;
Augustine, E. (1973) Airborne Sampling of Particles Emitted to the Atmosphere
from Kraft Paper Mill Processors and Their Characterization by Electron
Microscopy (Corvallis, OR: OSU Air Resources Center.)
Keith, L. (1976) "Identification of Organic Compounds in Unbleached Treated
Kraft Paper Mill Wastewaters". ES&T, 6, 555.
B-5
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SOURCE COMPOSITION REFERENCES (continued)
Nitrogen Containing Fuels;
Dubay, G.R. and Kites, R.A. (1978) "Cyano-arenes Produced by Combustion of
Nitrogen-Containing Fuel". ES&T, 12, 965.
Oil-Fired Boilers
Bennet, R.L. and Knapp, K.T. (1978) "Sulfur and Traca Metal Emissions from
Combustion Sources". April 24-26, Southern Pines, NC Workshop on Measurement
Technology and Characterization of Primary Sulfur Oxides Emission from
Combustion Sources.
Dietz, R.N. et al. (1978) "Operating Parameters Affecting Sulfate Emissions
from an Oil-Fired Power Unit". Workshop Proceedings on Primary Sulfate
Emissions from Combustion Sources, Volume 2 Characterization (EPA Publication,
500/9-78-0206).
Homolya, J.B. and Cheney, J.L. (1978) "An Assessment of Sulfuric Acid and
Sulfate Emissions from the Combustion of Fossils Fuels". Workshop Proceedings
on Primary Sulfate Emissions from Combustion Sources, Volume 2
Characterization (EPA Publication, 500/9-78-0206).
Mroz, E.J. (1976) The Study of the Elemental Composition of Particulate
Emissions From an Oil-Fired Power Plant Ph.D. Thesis (University of Maryland,
College Park, MD).
Mamuro, T. (1979) "Elemental Compositions of Suspended Particles Released from
Various Boilers". Annual Regort of the Radiation Center of Osaka Prefecture,
20, 29.
Van Lehmden, D.J. et al. (1974) "Determination of Trace Elements in Coal, Fly
Ash, Fuel Oil, and Gasoline - - A Preliminary Comparison of Selected
Analytical Techniques". Analytical Chemistry, 46, 239.
Zoller, W.M. et al. (1973) "The Sources and Distributions of Vanadium in the
Atmosphere". Trace Elements in the Environment Ed. E.L. Kothny (Washington,
D.C.: American Chemical Society).
Paste Plants;
Bjorseth A. and Lunde G. (1977) "Analysis of the Polycyclic Aromatic
Hydrocarbon Content of Airborne Particulate Pollutants in a Soderberg Paste
Plant". American Industrial Hygiene Association Journal, 38, 224.
Petroleum Pitch:
Grienke, R.A. and Lewis, I.C. (1975) "Development of a Gas Chromatographic
Ultraviolet Absorption Spectrometric Method of Monitoring Petroleum Pitch
Volatiles in the Environment". Analytical Chemistry, 47, 2151.
B-6
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SOURCE COMPOSITION REFERENCES (continued)
Refuse Combustion:
Campbell, W.J. et al. (1977) "Determination of Trace and Minor Elements in the
Combustible Fraction of Urban Refuse". Methods and Standards for
Environmental Measurement, (NBS Special Publication 264: Washington, D.C.), p.
157.
Clement, R.E. (1981) Development of Analytical Methodology for Airborne
Particulate Municipal Incinerator Ash, Ph.D. Dissertation, University of
Waterloo, Canada.
Eicemen, G.A. (1979) "Analysis of Fly Ash from Municipal Incinerators for
Trace Organic Compounds" Analytical Chemistry, 51, 2343.
Germani, M.S. (1980) Selected Studies of Four High Temperature Air Pollution
Sources Ph.D. Dissertation, University of Maryland, College Park, MD.
Greenberg, R.R. (1976) A Study of Trace Elements Emitted on Particles from
Municipal Incinerators Ph.D. Thesis (University of Maryland, College Park, MD).
Greenberg, R.R. et al. (1978) "Composition of Particles Emitted from the
Nicosia Municipal Incinerator". ES&T, 12, 1329.
Law, S.L. and Gordon, G.E. (1979) "Sources of Metals in Municipal Incinerator
Emissions". ES&T, 13, 433.
Mamuro, T. and Mizohato, A. (1978) "Elemental Composition of Suspended
Particles Released in Refuse Incineration". Annual Report of the Radiation
Center of Osaka Prefecture, 19, 15.
Shen, T.T. (1978) "Air Pollutants from Sewage Sludge Incineration". APCA
Meeting, Houston.
Road Dust;
Blumer, M. (1977) "Polycyclic Aromatic Hydrocarbons in Soils of a Mountain
Valley: Correlation with Highway Traffic and Cancer Influence". ES&T, 11,
1083.
Ciacco, L.L. (1974) "Composition of Organic Constituents in Breathable
Airborne Particulate Matter Near a Highway". ES&T, 2, 935.
Soil:
Rahn, K.A. (1976) "Silicon and Aluminum in Atmospheric Aerosols: Crust-Air
Fractionation?" Atmospheric Environment, 10, 597.
Taylor, S.R. (1964) "Abundance of Chemical Elements in the Continental Crust:
A New Table". Geochemica et Cosmochimia Acta, 28, 1273.
B-7
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SOURCE COMPOSITION REFERENCES (continued)
Thomae, S.C. (1977) Size and Composition of Atmospheric Particles in Areas
Near Washington Ph.D./ Thesis, University of Maryland, College Park, MD.
Steel Production;
Jacko, R.B. and Neuendorff (1977) "Trace Metal Particulate Emission Test
Results from a Number of Industrial and Municipal Point Sources". JAPCA, 21,
989.
Lee, R.E. et al. "Concentration and Size of Trace Metal Emissions from a Power
Plant, A Steel Plant and a Cotton Gin". ES&T, 9, 643.
Mamuro, T. et al. (1979) "Elemental Compositions of Suspended Particles
Released from Iron and Steel Works". Annual Report of_ the Radiation Center of
Osaka Prefecture, 20, 19.
Tire Dusts:
Dennis, M.L. (1974) "Rubber Dust from the Normal Wear of Tires". Rubber-
Chemistry and Technology, 47, 1011.
Pierson, W.R. and Brachaczek, W.W, (1974) "Airborne Particulate Debris from
Rubber Tires". Ecology Symposium, ACS Rubber Division, Toronto, Ontario May
7-10, 1974.
Pierson, W.R. and Brachaczek, W.W. (1975) "Airborne Particulate Debris from
Rubber Tires". Presented at the Conference on Environmental Aspects of
Chemical Use in Rubber Processing Operations, University of Akron, March
12-14, 1975.
Pierson, W.R. and Brachaczek, W.W. (1975) "In-Traffic Measurement of Airborne
Tire-Wear Particulate Debris". JAPCA, 25, 404.
Veneer Dryers;
Cronn, D.R. et al. (1983) "Chemical Characterization of Plywood Veneer Dryer
Emissions". Atmospheric Environment, 17, 201.
Volcanoes;
German, M.S. (1980) Selected Studies of Four High Temperature Air Pollution
Sources Ph.D. Dissertation, University of Maryland, College Park, MD.
Welding;
Akellson, K. et al. (1974) "Elemental Abundance Variation with Particle Size
in Aerosols from Welding Operations". Proceedings of the Second International
Conference on Nuclear Methods in Environmental Research (University of
Missouri), p. 385.
B-8
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SOURCE COMPOSITION REFERENCES (continued)
Wood Stoves:
Butcher, S.S. and Sorenson, E.M. (1979) "A Study of Wood Stove Particulate
Emissions". JAPCA, 29, 724.
Cooper, J.A. (1980) "Environmental Impact of Residential Wood Combustion
Emissions and its Implications". JAPCA, 30, 855.
B-9
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse befoie completing)
EPA-450/4-84-020
3. RECIPIENT'S ACCESSION-NO.
TITLE AND SUBTITLE Receptor Model Technical Series, Vol. V:
Source Apportionment Techniques And Considerations In
Combining Their Use
5. REP
6. PERFORMING ORGANIZATION CODE
7 AUTHORIS)
Michael K. Anderson, et al.
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
TRC Environmental Consultants, Inc.
East Hartford, CT 06108
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS
Office Of Air Quality Planning And Standards
U. S. Environmental Protection Agency
Research Triangle, NC 27711
13. TYPE OF REPORT AND PERIOD COVERED
(MD 14)
14. SPONSORING AGENCY CODE
IS SUPPLEMENTARY NOTES
EPA Project Officer: Thompson G. Pace
16. ABSTRACT
This volume 1) discusses models which identify source contributions to receptor
concentrations, their input data, the assumptions on which they are based, and the
effects of typical deviations from those assumptions; 2) identifies measurements
which these models require, their availability, the additional assumptions imposed
by these measurements, and the effect of their precision and accuracy on modeling
results; and 3)presents approaches, pertaining to three levels of analysis detail,
for the optimum combinations of models and measurements in practical situations, and
illustrates these protocols with case studies.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lOENTIFIERS/OPEN ENDED TERMS C. COSATI Field/Group
13. DISTRIBUTION STATEMENT
19. SECURITY CLASS (This Reporlj
21. NO. OF PAGES
192
20. SECURITY CLASS (This page)
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