cxEPA
United States
Envirbnmental Protection
Agency
Office of Air Quality
Planning and Standards
Research Triangle Park NC 27711
EPA-450/4-87-008
March 1987
Air
Protocol For
Reconciling
Differences Among
Receptor And
Dispersion Models
-------
EPA-450/4-87-008
March 1987
PROTOCOL FOR RECONCILING
DIFFERENCES AMONG RECEPTOR AND
DISPERSION MODELS
By
Air Management Technology Branch
Monitoring And Data Analysis Division
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Radiation
Office of Air Quality Planning and Standards
Research Triangle Park NC 27711
-------
This report has been reviewed by the Office of Air Quality Planning And Standards, U S Environmental
Protection Agency, and approved for publication. Any mention of trade names or commercial products is not
intended to constitute endorsement or recommendation for use.
EPA-450/4-87-008
-------
TABLE OF CONTENTS
Page
List of Figures 1v
List of Tables v
Introduction 1
CMB-DM Reconci 1 i ati on 3
Protocol Step 1: Compare CMB and DM Results 3
Selection of Sampling and Analysis Periods
for Analysis and Comparison 4
Background and Secondary Particulate Matter 5
Grouping of Sources 6
Comparison of .Results 8
Protocol Step 2: Verify Input Data in Both Models and Rerun
If Necessary 8
Protocol Step 3: Recompare Results 8
Protocol Step 4: Refine CMB Model Inputs 8
Protocol Step 5: Recompare Results 9
Protocol Step 6: Refine Dispersion Model Inputs and Rerun 9
Protocol Step 7: Recompare Results and Evaluate the Dispersion Model 11
Protocol Step 8: Final Model Estimates as Basis for Control Strategy 12
Exampl e 12
Acknowl edgments 14
References 15
Appendix A - Example Application of Protocol in an
Industrial Area A-l
iii
-------
LIST OF FIGURES
Number Page
1 Information Flow for Source Apportionment
and .Control Strategy Development 14
iv
-------
LIST OF TABLES
Number Page
A-l Types of Emissions Assigned to Each Source
Group in the Dispersion Modeling Analysis ............... A-8
A-2 Comparison of Initial Results by ISCST and
CMB for Subsets A and B (ug/m3) ......................... A- 9
A-3 Comparison of the Receptor and Disperson Model
Results for the October 1, 1983 PMio Samples ............ A-12
A-4 Comparison of the Receptor and Dispersion Model
Results for the October 25, 1983 PMjQ Samples ........... A-13
A-5 Comparison of Final Validation Results by ISCST
and CMB for Subsets A and B (ug/m3) ..................... A-19
A-6 Source Contributions Estimated by ISCST Using
5 Years of Meteorological Data (ug/m3) .................. A-20
-------
INTRODUCTION
The PMm SIP Development Guideline recommends that both Chemical Mass
Balance (CMB) and Dispersion Models (DM) be used to determine the relative
importance of various sources in contributing to ambient PM^g concentrations,
provided the models are applicable.! Dispersion model applicability is
discussed in Section 11.2.2 of the Guideline on Air Quality Models and in
the Interim Procedures for Evaluating Air Quality Models.2*3 The CMB
applicability is discussed in the Protocol for Applying and Validating the
CMB Model .4
Additionally, the PM^Q SIP Development Guideline provides for the use of
other receptor analyses to corroborate CMB and DM estimates. These other
methods include optical or automated scanning electron microscopy (OM,
ASEM), microinventory, factor analysis (FA), multiple linear regression
(MLR), and trajectory analysis (TA), as discussed an Appendix A to the
SIP Development Guideline. Use of several methods is desirable, because it
yields estimates of source contributions from several perspectives, based
on different assumptions and data. This approach greatly enhances the
likelihood that the nature of an ambient PM^g problem is correctly understood.
However, systems which rely on different measurements or use of different
estimation parameters will likely yield different results. The differences
may be negligible, but occasionally the differences may be large enough to
require that they be reconciled.
The protocol summarized in this report consists of an eight-step procedure
for examining and reconciling differences in model estimates in a systematic,
justifiable way. This procedure is illustrated in Appendix A. The emphasis
is on CMB and DM reconciliation, primarily because the SIP Development
-------
Guideline identifies CMB as the principal receptor modeling approach for
use in preparing SIP's for PM^o* Nevertheless, this protocol is intended to
be general enough so that the results of other methods (e.g., "OM," "ASEM,"
or "MLR") could be reconciled in the same manner. (In fact, OM is used in
the example in Appendix A.)
There are at least three generic cases which require model differences
to be resolved (reconciled). The first case is when the results strongly
suggest that at least one of the models has a significant error. The second
case is when the model estimates are not disparate, but the overall uncertainty
is judged to be large. Resources devoted to further refinement of the models
and data bases might significantly reduce the uncertainties. The third case,
a very common one, includes situations where one model is better for some
source types, and another is better for other source types. Refinement of
the final source contribution estimates should be cognizant of the suitability
of each model to the suspect sources. Trijonis reports that the literature
assumes that CMB models are more reliable than dispersion models for quanti-
fying the impact of source categories (i.e., steel mills, power plants,
woodstoves, road dust).5 According to Trijonis, this may be because the
CMB is tied directly to the ambient data that are being apportioned. The
CMB is 1imi ted to apportionment of source categories unless there is only
one source of that category upwind on the sampling day. In contrast, the
DM can deal explicitly with emissions from single, identifiable sources
within the same source category. However, in order to do so, the DM must
build on several potentially uncertain inputs (emissions data, meteorological
data, and the transport-diffusion-transformation-deposition mechanisms).
Thus, the CMB and DM are complementary in their approach to source apportion-
ment and using both can reduce the limitations of each alone.
-------
CMB-DM RECONCILIATION
The CMB-DM reconciliation process is based on the premise that the
receptor and dispersion models provide independent estimates of source
contributions, rely on different data bases, have different underlying
assumptions, and have different strengths and weaknesses. Generally, other
%
receptor methods are used first, then the CMB is performed and, finally, the
DM is run. Figure 1 depicts the flow of information if this order is maintained.
At each step, the results of the other analyses are used to corroborate the
.ongoing analysis. The use of other receptor methods to corroborate the CMB
is shown on Figure 1 for completeness but is discussed elsewhere.^
"The reconciliation of CMB-DM results should follow an eight-step protocol
involving comparison, reverification of input data, refining the inputs to
both models, and rerunning the model(s) if necessary. The reconciliation
protocol described in the remainder of this document assumes that both the
dispersion model and CMB have been determined to be applicable and that
preliminary results have been obtained for both models. The reconciliation
process ends with control strategy development using the dispersion model
unless the dispersion model is found to be inconsistent with the majority
of the physical data. In such case, the CMB along with other receptor models-
would be used with a proportional model to develop a control strategy.1
Protocol Step 1: Compare CMB and DM Results
There are several issues that must be resolved in order to compare CMB
and DM results: (1) consideration of suitable periods for sampling and
analysis, with particular emphasis on source emission variability and
-------
Source
Information
I
Recommended
Approach
Alternate |
Approach j
Ambient
Sample
Other
Data
t
Other
Receptor
Methods
h
1
1
1
1
1
1
r
i
T
D
1
r
f
CMB
Model
i
ispi
Mo
~ 1
l_
1
1
srsion '
del 1
I
I
Apportionment
by
Dispersion Model
Apportionment
by
Other Methods
i
Control Strategy
by
Dispersion Model
Control Strategy
by
Proportional Model
FIGURE 1. Information Flow For Source Apportionment
And Control Strategy Development
-------
meteorological conditions; (2) the issues of secondary participate matter
and background; and (3) grouping of the sources. These issues are discussed
in the following paragraphs.
Selection of Sampling Periods for Analysis and Comparison. Attainment of
the NAAQS is generally calculated based on ambient data. Ideally, for
comparison purposes, the DM would be run for the actual monitoring period
and CMB analyses would be available for each day during that same period.
It is preferable that the CMB be performed on days selected randomly during
the period. However, this ideal is virtually never achieved.for numerous
reasons of cost and practicality. For example, in assessing potential
causes for not meeting the annual NAAQS, the DM may have to be run using
meteorological data representing different years than those on which non-
attainment is based. In that case, it would be necessary to perform CMB on
a subset of days which are representative of the measured concentrations
during the time period covered by the DM but also representative of the
overall meteorology during that time period. In addition, the number of
samples to be compared must reflect the seasonal differences in measured
concentrations. In CMB applications relating to the annual NAAQS, a minimum
of five samples in each quarter are considered necessary to obtain representative
results.
For violations of the 24-hour NAAQS, it *is preferable to apply the CMB
model on the days on which "exceedances" of the NAAQS were observed. In this
case, one would analyze and compare all observations greater than the level
of the NAAQS. In order to obtain representative results, a minimum of five
samples (e.g., the five highest values) should be compared. If there are
fewer than five observed "exceedances" of the NAAQS, the five highest
-------
values overall should be used so that the analysis is based upon a repre-
sentative number of days. If a receptor model study were undertaken for a
short time period, it must be shown that the period covered was generally
representative of the types of source activity and meteorology which are
associated with exceedances measured outside of the receptor model study.
The source operating levels must be similar and the dispersion conditions
and wind deviation during the shorter period must be such that the same
sources would be expected to contribute to measured concentrations. The
receptor data are particularly useful if exceedances are measured during
the receptor study.
Unfortunately, a 24-hour emissions inventory appropriate for specific
days may not be available or obtainable. Inventories usually contain
annual average emissions. Thus, associating "dates" with the dispersion
model results, which are based on annual emissions, could be risky. In
this case, an "aggregate" comparison of the analysis results is recommended.
To make an "aggregate" comparison, select a subset of dispersion model
24-hour estimates that has dispersion meteorology similar to that which
occurred on the CMB analysis days (e.g., days with similar wind velocities
and precipitation/cloud cover). Then, compare the source contributions for
each CMB analysis wi.th the average-source contributions for the corresponding
subset of dispersion modeled days.
Background and Secondary Particulate Matter. In many cases, the receptor
model is used for source apportionment at urban or source oriented sampling
sites, and the apportionment will include not only the "urban" contribution
but also the background component which has been transported to the urban
-------
area from elsewhere. In such cases, it is usually necessary to perform an
analysis on a nearby background receptor so that the background or regional
contributions to ambient PM observed at urban or source-oriented sites can
be distinguished. This receptor analysis at a background site is
necessary, for example, to distinguish between locally generated soil dust
and that transported into the area. Receptor analyses from the background
and other ambient monitors are then compared. Source categories that were
identified on both urban and background monitors should be noted and estimated
contributions of these sources at the study (urban) monitor should be
reduced by the amount of their impact at the background site. In this
process, one must carefully select the background days for receptor analysis
to make sure they are "upwind" and are not impacted by local sources, and
adjustments must be made, to the CMB results prior to CMB-DM comparisons.
Also, this reduction in urban contributions must be made carefully when
background sources are chemically similar to sources in the urban area
(e.g., soil and resuspended street dust), especially if the background site
has considerable localized sources.
Grouping of Sources. CMB is usually only useful for identifying source
categories contributing to ambient PM^Q. In contrast, DM can identify
contributions from individual sources. Thus, source contributions from the
CMB and the DM must be grouped in such a way that contributions from similar
sources can be assessed. The DM results are usually regrouped because the
source chemical characteristics used in the CMB cannot usually be so done.
Therefore, for the purpose of comparing DM results with CMB results, sources
considered by the DM must be grouped into larger "categories" or source
groups.
-------
For example, perhaps a monitored sample was obtained from a coking
operation. From a literature review or from experience, it is determined
that the following operations at a steel making facility have emissions
chemically similar to the coke sample:
Quenching
Pushing
Charging
Finished Coke Storage Pile
The "coke" source contribution calculated by the CMB would conceivably
represent all of these specific sources. The DM output should group all of
these sources together into a "coking" group; alternatively, the DM estimates
for these sources could be manually grouped in a separate tabulation after
the model is run.
Although groups are often arbitrarily selected, for purposes of CMB-DM
model reconciliation, a little judicious planning can make the comparison
of CMB with DM results fairly straightforward. This can enhance confidence
in the models themselves if good agreement is found.
Grouping of DM sources is performed as follows:
(a) Review the detectable CMB source contribution categories to determine
what they really include or represent. This should be documented
with the best available references. For example, a "sea salt" source
might include salt from ocean spray and also salt which was spread
for snow control.6
(b) Review the Emission Inventory (El) and decide which DM sources
collectively comprise each CMB source category. Also, identify
any unmodeled sources (e.g., continental dust, sea salt, etc.)
that might be represented by the CMB source category.
8
-------
(c) Combine the DM sources, into groups that are consistent with CMB
source categories.
(d) Identify any sources which were significant contributors in the
CMB analysis but which were not in the DM emission-inventory.
(e) Add these sources to the El as appropriate.
* (f) Rerun the DM.
(g) Make sure that the CMB has considered all nontrivial sources which
were identified in the DM inventory. Rerun the CMB if necessary.
Comparison of Results. The grouping of sources described above allows a
comparison of the DM and CMB estimates of the percentage contributions to
the total calculated PM^g mass. First, establish an interval of + or -
30 percent around the DM results and + or - one standard error around the CMB
results. If these intervals overlap, no further refinement is generally
warranted. However, if the DM results imply a source group is a major contributor
and the CMB results imply that it is not (or vice versa), the results must be
reconciled, even if the comparison criterion (above) is met. If further
refinement of the models or inputs are needed to meet the criterion, the
procedure for doing so is described in the following section. Note that the
+ or - 30.percent interval about the DM is only for use in this intercomparison;
it is not intended as a confidence interval for the DM.
Protocol Step 2: Verify Input Data in Both Models and Rerun if Necessary
This step ensures that the difference in CMB and DM results are not due
to trivial or inadvertent problems with data entry. It should focus on the
data entry process to verify that the models were run using the intended
-------
data. The data review should include the ambient and source profile data,
emissions data and meteorology. If all data are correct, skip to Step 4.
Protocol Step 3: Recompare Results
Any differences found in model estimates due to data entry should' now be
eliminated. Revised model results can now be compared, as discussed in
Protocol Step 1. If the comparison criteria are not met, the next step is to
reexamine model inputs, initially focusing on the CMB model.
Protocol Step 4: Refine CMB Model Inputs
This is an indepth review of the CMB model inputs, focused on those
source groups where large differences between CMB and DM source category
contribution estimates occurred. Because of the validation that the CMB
application should have received, it is unlikely that there are any substantive
CMB model input problems which were not apparent from the preliminary analyses.
However, insights available through the Dispersion Model analysis may
highlight new problems or confirm suspicions that the modeler may have already
had about weaknesses in the CMB that could not be previously substantiated.
Also, it is appropriate to review the results of other receptor analyses
at this point. This review may require modifying the model inputs or addressing
col linearity.4
All measurements have some imprecision or uncertainty about them, and
there will be some inherent underlying "noise" level in the data set which
cannot be reduced by a reasonable allocation of time and resources. The
user should review the CMB validation in light of the DM results.4 If any
weaknesses become apparent, address them to the extent time and resources
permit, focusing on the source groups with disparate estimates. -Then rerun
10
-------
the CMB model, if any refinements were made. It is not sufficient to know
just that a revision to a parameter will "fix" a discrepancy. Technical
justification is required to document all changes that are made.
Protocol Step 5: Recompare Results
Any remaining differences found between CMB and DM model estimates are
best addressed by closer examination of the dispersion model. If the comparison
criteria in Step 1 are met, skip to Step 7.
Protocol Step 6: Refine Dispersion Model Inputs and Rerun
This is an indepth review of the dispersion model, focusing on those
source groups (e.g., fugitive dust sources) for which large differences occur
between DM and CMB. This review should include (1) the appropriateness of
emissions data, which are based on the identification of sources and their
locations, activity rates, emission factors and release heights; (2) meteor-
ological data; "and (3) underlying assumptions in the model itself and its
dispersion, transformation, and removal algorithms. Reference 7 gives examples
of factors that primarily affect individual source-to-monitor impacts:
o Erroneous emission data caused by such items as the omission
of unknown sources from the modeling or the use of inaccurate
throughput information.
o The use of inappropriate emission rates, such as the use of
total particulate matter emission rates which include particle
sizes larger than PMjg that settle out prior to impacting the
monitoring site and are not collected by a PMig sample if they
do reach the receptor.
o 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.
11
-------
o Neglect or incorrect consideration of downwash from tall
stack sources.
o Neglect or incorrect consideration of particles resuspended
by the wind or mechanical action.
o Building interference causing source-to-receptor (i.e.,
source-to-monitor) geometry to be incompatible with
Gaussian dispersion assumptions.
o Local meteorology differing from that modeled. A typical
problem is wind direction shift or channeling caused by
buildings or topographic features.
In reference 7, Anderson suggests that these emissions and meteorological
data problems can often be satisfactorily resolved by:
(a) identifying significant sources or source groups from the CMB
results;
(b) assigning a "level of confidence" to emission and meteorological
parameter estimates for each significant source or source group, based on
engineering estimates. The confidence level information needed for this
analysis may be found in Volume V of the Receptor Model Technical Series.7
Ideally this analysis should be completed before the CMB and dispersion -
models are run.
(c) revising the emission and meteorology parameter estimates, as
appropriate. It is not sufficient to know just that a revision to a parameter
will "fix" a discrepancy. Technical justification is required to document
all changes that are made.
The pattern of discrepancies at different receptors may also aid in
identifying potential inventory errors. For example, large under- or over-
prediction by the dispersion model at a receptor very close to a source may
suggest possible errors in source receptor geometry. Large overprediction
at receptors far from a source would suggest an erroneous particle size
12
-------
distribution in emission factors. Experience with dispersion models and
emission inventories will play an important role in diagnosing potential
inventory problems.
The dispersion model is then rerun, using the revised input parameters.
Protocol Step 7: Recompare Results and Evaluate the Dispersion Model
The revised dispersion model results are again compared with the CMB
results to determine whether further modifications to the dispersion model
or meteorological inputs are warranted.
Anderson provides examples of factors that are not typically addressed
by the dispersion model that may cause systematic biases in model results.
These factors are addressed in various nonguideline models that are currently
available.
o Heat island effects which cause the actual near-source
dispersion of elevated emissions to be greater than that
modeled.
o Sea/land breeze and convergence zone effects.
o Effects caused by the fumigation of tall stack emissions.
o Effects caused by the development of thermal internal
boundary layers (TIBL) over areas with varying surface
heating characteristics.
Remedies to these problems might include acquisition of additional
meteorological data, choice of different model options or modification of
dispersion algorithms to meet local disperson conditions and source char-
acteristics. Such problem specific model modifications may prove a time and
cost effective alternative to additional sampling and analysis. These remedies
should be applied to the extent permitted by time and resource constraints.
13
-------
Protocol Step 8: Final Model Estimates as Basis for Control Strategy
The reconciliation process should help improve both the initial DM and
CMB. results so that the differences between models are resolved. If this
is the case, it is recommended that the DM model be used for control strategy
development. If, however, it is clearly evident that the dispersion model
results are inconsistent with the majority of the physical data and cannot
be made consistent through justifiable modifications to the input data, the
CMB estimates should be used as the basis for control strategy development.
It is better to use the results of the method believed to provide the best
results, rather than average the results of two or more methods.
EXAMPLE
Appendix A describes the application of this protocol to an industrial
area. The example includes the use of preliminary analyses including pol-
lution rose and microinventory, dispersion modeling using the Industrial
Source Complex-Short Term Model (ISCST), and receptor modeling using the
Chemical Mass Balance (CMB) and optical microscopy (OM).
14
-------
ACKNOWLEDGMENTS
The primary authors of this document are Thompson G. Pace, P.E., of
the U.S. EPA, and Dr. John G. Watson of the Desert Research Institute.
Messrs. Richard DeCesar and Michael Anderson of TRC Environmental were
largely responsible for the Example in Appendix A. Dr. John Trijonis
provided valuable conceptual framework at the onset of this protocol
development. The following co-authors participated in a workshop to
review and revise the document in May 1986 in San Francisco: Dr. Kit
Wagner, Mr. Bart Croes, Mr. Duane Ono, Dr. Hal Javitz, Mr. Mike Naylor,
Dr. Judith C. Chow, Dr. David Maughan, Dr. Edwin Meyer, Mr. Luke Wijnberg,
Mr. Ken Axetell, Mr. John Core, Mr. Pat Hanrahan, Dr. Ron Henry, Mr. Bong
Mann Kim, Mr. Chung Liu, and Dr. Andy Gray.
Several others provided review and comment outside of the workshop.
The reviews by Dr. Glen Gordon, Mr. William Cox, Mr. Chuck Lewis,
Dr. Thomas D. Dzubay, Mr. Robert K. Stevens, Mr. Johnnie Pearson,
Dr. Richard DeCesar, Dr. Sylvia Edgerton, and Mr. Edward Lillis are
greatly appreciated. The typing and revisions by Ms. Cathy Coats and
Ms. Jo Harris are much appreciated.
15
-------
REFERENCES
1. U.S. EPA. PMTn SIP Development Guideline, EPA 450/2-86-001,
Research Triangle Park, NC27711.February, 1987.
2. U.S. EPA, Guideline on Air Quality Models (Revised). EPA 450/2-
78-027R. U.S. EPA, Research Triangle Park, N C 2"7711.
3. U.S. EPA, Interim Procedures for Evaluating Air Quality Models,
EPA-450/4-85-023, U.S. EPA, Research Triangle Park, NC27711,
September 1984.
4. U.S. EPA, Protocol for Applying and Validating the CMB Model,
EPA 450/4-87-010, U.S. EPA, Research Triangle Park, N.C. 27711, May, 1987.
5. Trijonis, J. "Model Reconciliation," Special Report prepared for U.S.
EPA and TRC Environmental Consultants under Contract 68-02-3886, Work
Assignment No. 13, Research Triangle Park, NC 27711, September 1985.
6. U.S. EPA, Receptor Model Source Composition Library, EPA 450/4-85-002,
U.S. EPA, Research Triangle Park, NC 27711, November 1984.
7. U.S. EPA, Receptor Model Technical Series, Volume V: Source Apportionment
Techniques and Considerations in Combining Their Use, EPA-450/4-84-020.
U.S. EPA, Research Triangle Park, NC 27711, July 1984.
8. Anderson, M. K., Richard Decesar, R. Longergan and E. Brookam, Example
Modeling To Illustrate SIP Development For The New Particulate Matter
TiflAQS, Prepared by TRC Environmental for the U.S. EPA under contract
68-02-3886, work assignments 37, 48 and 52, Draft, in Preparation.
9. OeCesar, R. T. personal communication to T. Pace, February 1987.
10. U.S. EPA, Receptor Model Technical Series, Volume III (Revised):
Chemical Mass Balance User's Manual, U.S. EPA, Research Triangle
Park, NC 27711.In preparation.
16
-------
APPENDIX A - EXAMPLE APPLICATION OF PROTOCOL IN AN INDUSTRIAL AREA
INTRODUCTION
This section describes an example application of the protocol for a
nonattainment site located within an industrial area. The example was
adapted from an actual environmental case study and modified where necessary
to enable its use with this protocol.^ All modifications were cognizant of
what could and would have actually been done in the reconciliation had time
and resources permitted.9 in this example, the causes of the elevated concen-
trations of PMio are not apparent due to the large number of point and area
sources located relatively close to the monitoring site. Based strictly on
the emission inventory, stack and fugitive dust emissions from a large steel
mill as well as traffic generated resuspended road dust appear to be potentially
large contributors to the particulate matter loadings observed at the monitoring
site. An investigation, which includes ambient and source data collection,
optical microscopy (OM), pollution rose (a form of trajectory analysis),
dispersion modeling (DM) and chemical mass balance (CMB) modeling efforts,
was conducted in order to apportion the source impacts with the level of
confidence necessary for making control strategy decisions. The results are
reconciled using the eight-step process previously described.
The industrial site PM^g source apportionment example presented in this
section is compiled primarily from experience gained from assessing source
impact at a single monitoring site. The purpose of this example is to Illus-
trate use of the eight-step reconciliation protocol described in this document.
Therefore, the identity of the site and details of the OM, CMB, and DM analyses
are unimportant to the objective of the example and are not presented in
detail.
A-l
-------
OVERVIEW OF THE SOURCE APPORTIONMENT STUDY
A combined receptor and dispersion modeling study was implemented with the
objective of identifying and quantifying impacts of the sources contributing
to violations of the PM^g NAAQS recorded at a monitoring site. To supplement
the emission inventory, pollution concentration roses were prepared using data
for eight TSP monitoring sites located near the PM^Q monitoring site. The
pollution roses clearly indicate that the major sources of TSP are located in
the quadrant south of the PM^Q monitor. That is, a preponderance of the high
24-hour average TSP measurements correspond with days having resultant winds
from the south. This preliminary receptor analysis suggests that additional
emphasis should be placed on characterizing steel mill related contributions
because the steel mill is the largest source of particulate emissions located
nearby and south of the monitoring site.
Receptor Modeling Data Collection Tasks
The ambient data base consisted primarily of: (1) ambient PM^g samples
collected for 1 year on an every-6th-day schedule; and (2) hourly measurements
of wind direction, wind speed, and temperature at the PM^g monitoring station.
The PM^Q samples were collected on quartz fiber filters using a size-selective
high volume (SSHV) sampler. In addition, for 1 month a dichotomous sampler
was in operation at the monitoring site and collected several 24-hour coarse
and fine fraction samples on Teflon filters. The results of the PMig monitoring
program provided an annual average PM^g concentration of 65 ug/m^ as well
as four exceedances of the 24-hour average PM^g standard of 150 ug/m^.
A-2
-------
A site visit was made to compile a microinventory of emission sources.
The microinventory procedure consisted of determining the nature, location
and spatial extent of all fugitive dust sources within one-quarter mile of
the PM^g monitoring station. The information was then used to calculate the
emission rates for input to a dispersion model. Source types included unpaved
roads, paved streets, railroads, and two coal storage piles. Emission rates
were determined using published emission factors together with the source
area and traffic volumes.
As part of the site visit, samples of emitted materials were collected
from six sources in the general vicinity of the site which had been identified
as potentially important contributors to PM^g. The samples were subsequently
analyzed to provide source profiles for use in the CMB model. The sources
which were sampled included: a steel mill (blast furnace, coke oven, and
basic oxygen furnace); two road shoulders (one near the monitoring site and
one near an inoperative lead plant); and an agricultural processing plant
(potash, corn gluten pellets, and soybeans).
Two subsets of the ambient PM^o filters were selected for analysis as
discussed above. Subset A contained the filters from 20 sampling days which
were selected to be representative of the annual average conditions at the
monitoring station. The average PM^o concentration calculated from the filters
in subset A was 67 ug/m^, which compares well with the annual average
calculated from all the filters (65 yg/m3). Subset B contained the
samples from the 4 days on which exceedances of the 24-hour average NAAQS
were recorded plus the highest nonexceeding day (a total of 5 days). This
subset was chosen to guide control strategy development related to the 24-hour
standard.
A-3
-------
Two multi-elemental characterization techniques were applied to the
source and ambient filters. X-ray fluorescence (XRF) was performed on the
Teflon filters, and Plasma Emission Spectroscopy (PES) was employed for the
quartz filters. The following species were analyzed: Na, Mg, Al, Si, K,"
Ca, Ti, V, Mn, Fe, Zn, Br, Pb, EC and $04. The elemental carbon (EC) content
of the samples was determined by the optical attenuation analysis method. In
addition to the chemical characterization procedures, optical microscopy was
applied to several of the ambient samples. Optical microscopy, which provides
reliable particle identifications and semi-quantitative source contribution
estimates, was used as a QA check on the receptor modeling source identifications
and to aid in the receptor/dispersion model reconciliation process.
Receptor modeling was performed for this study using the Chemical Mass
Balance (CMB) model.4'10 The CMB model requires an input file containing the
measured ambient concentrations of the elements for which the samples were
analyzed. This requirement was fulfilled by transforming the results of the
ambient filter analysis into the format specified by the CMB model. In
addition, the CMB model requires a file containing the source compositions
reported as the elemental mass fractions. A source composition file in CMB
specified format was compiled. The file contained the elemental composition
of the resuspended local sources as well as a number of source profiles which
were extracted from EPA's Source Composition Library.6 The emission inventory
and the microinventory were used to select sources for consideration in the CMB.
Dispersion Modeling Data Preparation
The emission inventory of all the point sources in the two counties
surrounding the PMjo monitoring site was obtained. A total of 140 point sources
A-4
-------
were included in the inventory. The point source inventory was modified for
use in modeling PMjg impacts by applying source specific PMjg/TSP emission
ratios to the TSP emission factors which were originally contained in the
inventory.
The Industrial Source Complex Short-Term (ISCST) model was used for this
%
investigation. ISCST was used because it is applicable to the dispersion con-
ditions found in the study area, and it contains several features that improve
the source apportionment process. Two potentially advantageous features of
ISCST are the ability to: (1) model microinventoried fugitive dust sources as
volume sources; and (2) calculate the combined impact for selected groups of
sources. This latter feature greatly decreases the manipulations necessary
to transform dispersion and receptor modeling results into comparable formats
providing basis for comparisons.
Two receptor oriented approaches are utilized in this example: the CMB
model and the optical microscopy technique. CMB modeling analyses were per-
formed on the data obtained from filters contained in subsets A and B described
in Section 2.1. The modeling procedure consists of obtaining a solution
using the CMB procedures found in reference 4.
Two SSHV samples, two coarse fraction dichotomous samples, and one fine
fraction dichotomous sample (ambient samples) underwent optical microscopic
» •
analysis to confirm the CMB source identifications. In addition, the results
of the particle counting performed as part of the microscopic analysis are
used to calculate semi-quantitative source contribution estimates.
A-5
-------
As part of the CMB modeling procedure, efforts are made to develop
information which could be used to confirm or refute the validity of the CMB
results. This information can be classified into four categories:
(1) applicability of the model to the situation; (2) evidence of adherence or
deviation from model assumptions by examination of the summary statistics
%
resulting from the use of model assumptions; (3) stability of source impact
estimates with respect to minor changes in the CMB model fitting parameters;
and (4) comparisons with preliminary analyses (e.g., pollution rose, OM and
microinventory analyses). By evaluating the CMB results on the basis of the
above four categories, the CMB analyses are determined to be valid.
Background concentrations are defined as that portion of the measured
ambient levels that is not attributable to emissions within the study area.
To estimate the appropriate background concentrations for the dispersion
model, an eclectic pollution rose* was developed using data from TSP monitoring
stations located outside the study area. Data were used only from the days
having persistent winds blowing into the study area from the direction of the
background TSP stations. An average TSP concentration of 26 yg/m^ was
determined to be attributed to sources outside the study area. Application
of the site-specific PMjo/TSP ratio (0.57) yields an average background
concentration of 15 ugm3. Background concentrations were also estimated
for each wind sector using this method.
*The term "eclectic pollution rose" is used to describe a rose developed by
compositing the data from several monitors surrounding the urban area such
that the eclectic rose reflects concentrations only when the wind is blowing
into the urban area.
A-6
-------
THE EIGHT-STEP RECONCILIATION PROCESS
Step 1 - Compare DM and CMB Results
The dispersion model simulation results for time periods corresponding
to the PM^o sampling schedule at the monitoring station were compared with
measured PM^g air quality data. This comparison suggests that the ISCST is
underpredicting PM^g concentrations at the monitoring site. Comparison of
the predicted PMig w^tn tne measured PM^g minus estimated background PM^g
concentrations provided an indication that the model was underpredicting.
The source contributions from the DM and CMB were grouped in such a
way that they could be compared. This was actually done to some extent before
the ISCST was run by examining the emission inventory. The emission inventory
contained the name of each plant and a description of each emission source.
This information was used to develop .a preliminary tabulation of the types of
materials (e.g., specific source chemistry) emitted by each source. The
many types of emitted materials were then associated with 1 of 17 general
categories which were determined to be consistent with the source profiles
used in the CMB analysis. Each of the 140 point sources and 25 volume
sources was assigned a code corresponding to 1 of the 17 categories. Some
overlap and "juggling" of categories was inevitable but a reasonably good
preliminary grouping was made. The results are presented in Table A.I.
Adjustment to this preliminary grouping may be necessary in some cases.
The source contribution estimates provided by the CMB and ISCST models
for subset A (annual average) and subset B (samples violating the 24-hour
NAAQS) are compared in Table A-2. The basic oxygen furnace and steel blast
furnace groups were combined for the same reason. The results of the two
A-7
-------
TABLE A-l
TYPES OF EMISSIONS ASSIGNED TO EACH SOURCE
GROUP IN THE DISPERSION MODELING ANALYSES
Group Number Type of Emission
1 * Oi 1 and Gas Combustion
2 Coal Combination
3 Wood-fired Boilers
4 Agricultural Products Handling
5 Coking Operations
6 Coal Handling
7 Steel Blast Furnace and BOF
8 Paint Production
9 Cement and Limestone
10 Motor Vehicle Exhaust and Lead Processes
11 Sand and Bentonite
12 Aluminum Production
13 Zinc Processing
14 Tire Production
15 Oil. Refining
16 Fertilizer
17 • Road Dust, Soil, and Motor Vehicle Exhaust
A-8
-------
TABLE A-2
COMPARISON OF INITIAL RESULTS BY ISCST AND CMB FOR
SUBSETS A AND B (ug/m3)
(Representing
Annual Average)
Subset A
(Average of 5 Highest
24-hr Exceedances)
Subset B
Source Categories
Oil & Gas Combustion
Wood-fired Boi lers
Coal Combustion
Coking Operations
Blast Furnace3
Basic Oxygen Furnace3
Coal Handling
Agricultural Prod. Handling
Paint Production
Cement & Limestone
Sand £ Bentonite
Aluminum Production
Zinc Processing
Oil Refining
Fertilizer
Tire Production
Motor Vehicle Exhaust
Road Dust & Soil
Secondary (NH4)2$04
Background
Total
Measured
ISCSI
1
0
0
1
- 0
0
1
1
. 1
0
0
15
15
45
67
.6
.2
.6
.4
.6
.3
.1
—
.9
.2
--
.8
.6
— .
--
.1
—
.0
.4
.0
CMB
3
2
8
12
3
2
3U
15
77
67
..
.2
.8
.6
.0
.8
—
—
--
--
--
--
—
—
--
--
.4
.0
.0
--
.8
.0
(1
(1
(4
(3
(1
(0
(7
(2
ISCS
.4)
.1)
.2)
.5)
.5)
.8)
.2)
.5)
2.
0.
1.
4.
2.
0.
1.
0.
1.
1.
0.
0.
1.
0.
2.
2.
31.
-
32.
89.
162
T
0
8
2
0
o
6
6
8
9
7
4
6
5
8
2
6
2
-
0
0
.0
CMB
5
8
23
33
10
2
79
27
189
162
..
.6
.0
.2
.6
.4
—
--
--
__
__
--
—
—
--
_-
.4
.2
.4
--
.8
.0
(2.1)
(3.2)
(8.7)
(8.9)
(2.7)
(0.9)
(20.3)
(5.0)
a Estimated as a single source by the ISCST because of lack of
detail in the emission inventory.
NOTE: Numbers in parentheses indicate one standard error in the
CMB analysis.
A-9
-------
models display reasonable agreement in two categories: (1) the background
estimates used by ISCST are approximately equal to the secondary sulfate
values estimated by CMB; and (2) resuspended road dust is listed as a major
source of PMjo by both methods. In addition, the similarities and differences
between CMB and ISCST are consistent between subsets A and B. Aside from the
relatively few similarities which have been enumerated, many differences
between the CMB and ISCST results are evident. For example, ISCST underpredicts
the measured mass and CMB overpredicts the measured mass. Furthermore, ISCST
assigns contributions to several source categories which were not identified
by the CMB method. Finally, CMB and ISCST estimated substantially different
impacts for wood-fired boilers, coal combustion, the road dust and motor
vehicle exhaust combination and the steel mill related emissions from coking
operations, blast furnace, and basic oxygen furnace. Unfortunately, Table
A-2 does not reflect conclusions drawn from any OM analysis, because the OM
analysis is available for only 2 days.
As a result of this comparison, four cases were identified (based on
comparison model results as discussed in Step 1 of the Protocol) where additional
model refinements may be necessary:
Case 1. The combined contributions of motor vehicle exhaust and resuspended
road dust, as estimated by CMB and ISCST, were in major disagreement.
Case 2. A significant disagreement exists between the CMB and ISCST estimates
of the coal combustion impacts.
Case 3. CMB estimates wood-fired boilers to be a much larger contributor to
PM10 than does ISCST.
A-10
-------
Case 4. There is a major disagreement between CMB and ISCST for steel mill
related impacts (i.e., coking operations, blast furnace and basic oxygen
furnace source categories).
Each case is discussed below under the appropriate step in the reconciliation
process.
Steps 2 and 3 - Verify Input Data, Rerun Models and Recompare Results
A review of the input data for both models indicated that they were
indeed using the data that were intended. None of the four cases were related
to inadvertent errors in data input.
Step 4 - Refine CMB Model Specification
Operationally, all four cases identified in Step 1 are reviewed at this
point. Only case 1 was found to be related to the CMB inputs. Thus, discus-
sion of the other three cases is included under step 6. Tables A-3 and A-4
compare the source impacts estimated by ISCST, CMB and optical microscopy for
the 2 days, October 1, 1983 and October 25, 1983, when OM analysis was performed.
Case 1. The combined contributions of motor vehicle exhaust and resuspended
road dust, as estimated by CMB and ISCST, were in major disagreement.
The first step taken to reconcile the disagreement between CMB and ISCST
with respect to the combined impact of vehicle exhaust and read dust was to
•
re-examine the comparisons between OM, CMB, and ISCST. This is shown in Tables
A-3 and A-4 for the 2 days for which OM data are available. In this case, OM
was found to agree very well with ISCST. The CMB input data were then evaluated
for potential errors related to estimating road dust and vehicle exhaust.
The coarse fraction filter on which the resuspended road dust source sample
was collected appeared to have lost a substantial fraction of the sample
A-ll
-------
TABLE A-3
COMPARISON OF THE RECEPTOR AND DISPERSION MODEL RESULTS FOR THE OCTOBER 1, 1983 PM,0 SAMPLES
Dispersion Modeling
Source Groups
Optical Microscopy
Source Categories
CMB Modeling
Source Types
Modeled Concentrations
Coarse Fine
Dispersion SSHV Dichot Dichot
Modeled Optical Optical by Fine Coarse
Impacts Micros. Micros. Diff. SSHV Dichot Dichot
(A) (B) (A-B) CMB CMB CMB
r\>
Oil & Gas Combustion
Hood Combustion
Coal Combustion
Coke
Steel
Coal
Agricultural Products
Paint Products
Cement, Limestone
Lead
Sand, Bentonite
Aluminum
Zinc
Misc. Chemicals
Refinery Catalysts
Misc. Refinery
Fertilizer
Tire Products
Road Dust
Background
Total
Measured PM».$
Measured PM2.$ -1o
Measured Dichot. PM|0
Measured SSHV PM,0
Fuel Comb., Carbon
Fuel Comb..Part.Pyr.Coal
St.Mill, Part. Pyr. Coal
Steel Mill, Coke
Steel Mill
Coal Handling
Agr. Prod. Handling
Spray Painting
Cement Handling
Clay Handling
Alumina Handling
Oil Refining
Traffic, Tire Fragments
Traffic,
-------
I
i—*
CO
TABLE A-4
COMPARISON OF THE RECEPTOR AND DISPERSION MODEL RESULTS FOR THE OCTOBER 25, 1983 PM,0 SAMPLES
Modeled Concentrations (yiq/m1)
Dispersion Optical
Modeled Microscopy
Dispersion Modeling
Source Groups
Oil & Gas Combustion
Wood Combustion
Coal Combustion
Coke
Steel
Coal
Agricultural Product
Paint Products
Cement, Limestone
Lead
Sand. Bentonite
Aluminum
Zinc
Misc. Chemicals
Refinery Catalysts
Misc. Refinery
Fertilizer
Tire Products
Road Dust
Background
Total
Measured PM* . $
Measured PMj . s-io
Measured Dichot. PMio
Measured SSHV PM,0
Optical Microscopy
Source Categories "•
Fuel Comb. , Carbon
Fuel Comb. .Part. Pyr. Coal
St. Mill, Part. Pyr. Coal
Steel Mill, Coke
Steel Mill
Coal Handling
Agr. Prod. Handling
Spray Painting
Cement Handling
Clay Handling
Alumina Processing
Oil Refining
Traffic, Tire Fragments
Traffic,
-------
between the time it was weighed and the time it was analyzed. Further
evaluation of the sample indicated that this was indeed true. Thus,
the correct mass was used to recompute the road dust profile.
The CMB model was rerun with the revised road dust profile and good
agreement was now found between CMB and ISCST with respect to the combined
impacts of road dust and vehicle exhaust. The CMB estimated impacts of the
other sources remained unchanged by the revised road dust profile. Finally,
the other source samples were re-weighed and no other cases of significant
mass loss were identified.
Step 5 - Recompare Results After Rerunning CMB
The CMB model was rerun, the results were recompared and no significant
difference in model estimates remained.
Step 6 - Refine Dispersion Model Inputs and Rerun
Case 2. A significant disagreement exists between the CMB and ISCST estimates
of the coal combustion impacts.
The first step taken in reconciling the coal combustion impact estimates
was to review the ambient and source composition data for errors which could
potentially invalidate the CMB results. This review uncovered no obvious
errors in the CMB input data related to coal combustion. The next step was
to re-examine the comparisons between OM, CMB, and ISCST which are shown in
Tables A-3 and A-4. These tables show that OM predicts coal combustion
impacts in reasonable agreement with CMB and larger than ISCST. The emission
inventory was then reviewed with respect to coal combustion sources. The
review identified four major coal combustion facilities. For the closest of
A-14
-------
these sources to the receptor, the emission inventory contained a very high
value for the efficiency of the emission controls. Discussions with the
plant personnel revealed that the inventory contained an erroneous value and
a correct value was obtained. ISCST was then rerun with the corrected inventory
and much closer agreement was found between CMB and ISCST with respect to
coal combustion impacts.
Case 3. CMB estimates wood-fired boilers are a much larger contributor to
PM10 than does ISCST.
The first step in reconciling the CMB and ISCST estimates for the wood-
fired boiler impacts was to review the CMB input data. The review did not
identify any obvious errors in the data. Next, the OM results were examined.
In this case, OM was of little assistance because OM did not identify any
impact from wood-fired boilers while CMB and ISCST both did. The emission
inventory was then evaluated in terms of wood-fired boilers. There were no
obvious problems found for the two wood-fired boiler sources in the inventory.
Therefore, a site visit was conducted to each of the two plants. The site
visits identified the possibility that one of the wood-fired boilers, located
at a lumber mill, could be emitting particulate matter at a much higher rate
than was listed in the inventory. Therefore, emission testing was conducted
at the mill. The emission testing demonstrated that the emission inventory
had severely underestimated the emission rate of the wood-fired boiler located
at the lumber mill. ISCST was then rerun with the revised emission rate for
the lumber mill boiler and good agreement was found between CMB and ISCST
with respect to the wood-fired boiler source category.
A-15
-------
Case 4. There is a big disagreement between CMB and ISCST for steel mill
related impacts (i.e., coking operations, blast furnace, and basic oxygen
furnace source categories).
The steel mill source is of additional interest due to the fact that
there is a big disagreement between CMB and ISCST for each of the three main
PMjo emitting activities within the steel mill. This disagreement results in
CMB predicting that the steel mill is the largest industrial source of the
PMjo levels observed at the receptor site while ISCST predicts that the steel
mill is a relatively minor source. This discrepancy will have a big impact
on control strategy development and therefore must be reconciled very carefully,
The first step in the reconciliation process was to review the CMB input
data. The review did not identify any obvious anomalies in the CMB input
data. In fact, the steel mill related source profiles were judged to be of
very good quality due to the close agreement which was found between the
plasma emission spectroscopy analysis of the resuspended samples which were
collected on qoartz fiber and the XRF analysis of those collected on Teflon.
The next step was to examine the OM estimated impacts for the steel mill. In
this case, OM was found to agree much better with CMB than with ISCST. The
emission inventory was then reviewed with respect to steel mill emissions.
The stack emission rates were found to be in good agreement with emission
rates for similar activities at other steel mills. However, the review
revealed that the emission inventory did not contain any fugitive emissions
from the steel plant. This omission was viewed as a potentially serious
deficiency; and, therefore, a site visit was conducted to reassess the stack
and fugitive emission rates at the steel mill. The results of the stack
testing were in good agreement with the original stack related emission
A-16
-------
rates. However, substantial fugitive emissions were found to be associated
with the coking operations, blast furnace, and basic oxygen furnace. ISCST
was then rerun with the new fugitive emissions for the steel plant included
in the inventory. This resulted in very good agreement between CMB and ISCST
with respect to the steel mill contribution.
Step 7 - Recompare Results and Evaluate the Dispersion Model
The previous section presented the reconciliation of the CMB and ISCST
source impact estimates and resulted in a number of revisions to the input
data used by the models. Following Step 4, the CMB was rerun and after Step
6, the ISCST was rerun for.subsets A and B using the revised data. The
results obtained using the revised data are shown in Table A-5. Good
agreement now exists between the source impacts estimated by CMB and ISCST
for subsets A and B. Comparison of the ISCST results for subset A with the
observed minus background concentrations is also significantly improved.
ISCST was then run using 5 years of meteorological data to obtain estimates
of the annual average PMjg and the six highest values. The results provided by
ISCST are shown in Table A-6. ISCST predicts that both the annual average
and 24-hour PM^g NAAQS will be exceeded at the monitoring station. Steel mill
related activities are the major contributors to PMjo at the receptor site.
The second largest contributor is resuspended road dust. The highest predicted
PMlQ concentrations all occur with persistent south winds, which further
substantiates the steel mill contributions.
A-17
-------
Step 8 - Final Model Estimates
The dispersion model results have been improved after extensive interaction
with the CMB results. Thus, the dispersion model results in Table A-6 would
be used for control strategy development.
A-18
-------
TABLE A-5
COMPARISON OF FINAL VALIDATION RESULTS BY ISCST AND CMB FOR
SUBSETS A AND B (ug/m3)
(Representing Annual)
Subset A
(Avg. of 5 24-hr runs)
Subset B
Source Categories
Oil A Gas Combustion
Wood-f i red Boi lers
Coal Combustion
Coking Operations
Blast Furnace3
Basic Oxygen Furnace3
Coal Handling
Agricultural Prod. Handling
Paint Production
Cement & Limestone
Sand & Bentonite
Aluminum Production
Zinc Processing
Oil Refining
Fertilizer
Tire Production
Motor Vehicle Exhaust
Koad Dust X Soil
Secondary (NH4)2S04
Background
Total '
Measured
ISCST
1
3
2
10
14
0
1
1
1
0
0
15
!5>
68
67
.6
.5
.3
.1
.9
.3
.1
--
.9
.2
—
.8
.6
--
--
.1
—
JD
.4
.0
CMB
3
2
8
11
3
2
15
15
62
67
..
.4
.6
.4
.6
.8
—
--
--
--
__
--
--
—
--
--
.2
.1
.5
.6
.0
(1
(1
(3
(2
(1
(0
(3
(2
.6)
.4)
.7)
.5)
• 7)
.6)
.4)
.7)
ISCST
2
6
9
19
41
0
1
0
1
1
0
0
1
0
2
2
31
32
157
162
.0
.8
.2
.0
.0
.6
.6
.8
.9
.7
.4
.6
.6
.8
.2
.6
.2
--
.0
.0
.0
CMB
.
5.
8.
22.
31.
10.
-
-
_
_
_
_
-
-
.
.
2.
39.
28.
-
148.
162.
.
5
2
0
6
4
-
-
-
_
_
-
-
-
-
_
6
2
6
-
1
0
(2.2)
(3.0)
(8.5)
(6.9)
(2.6)
(0.8)
(8.1)
(4.4)
a estimated as a composite by the ISCST
NOTE: Numbers in parentheses indicate one standard error in the
CBM analysis.
A-19
-------
TABLE A-6
SOURCE CONTRIBUTIONS ESTIMATED BY ISCST USING 5 YEARS OF
METEOROLOGICAL DATA (ug/m^)
Source Categories
Oil & Gas Combustion
Wood-fired Boilers
Coal Combustion
Coking Operations
Blast Furnace
Basic Oxygen Furnace
Coal Handling
Agricultural Prod. Handling
Paint Production
Cement & Limestone
Sand & Bentonite
Aluminum Production
Zinc Processing
Oil Refining
Fertilizer
Tire Production
Motor Vehicle Exhaust
Road Dust & Soil
Background
Total
Annual
Average
1.4
3.0
2.0
8.7
10.1
2.7
0.3
0.9
0.1
1.6
1.0
0.1
0.6
0.3
0.1
0.4
12.7
15.0
61.0
24-hr 6th Highest
Concentration
1.7
6.0
7.5
27.6
38.0
14.8
0.4
0.4
1.1
2.1
0.7
0.2
0.6
2.1
0.9
1.5
45.4
32.0
183.0
A-20
-------
TECHNICAL REPORT DATA
(PLasc rnJ Instructions an the rcveni before compltlinxi
1. REPORT NO.
EPA-450/4-87-008
3. RECIPIENT'S ACCESSION NO.
4. TITLE ANOSUBTITLE
Protocol For Reconciling Differences Among
Receptor And Dispersion Models
5. REPORT DATE
March 1987
6. PERFORMING ORGANIZATION CODE
7. AUTMOR(S)
Air Management Technology Branch
Monitoring And Data Analysis Division
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
10. PROGRAM ELEMENT NO.
Office Of Air Quality Planning And Standards (MD 14)
U. S. Environmental Protection Agency
Research Triangle, NC 27711
11. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS
13. TYPE OF REPORT AND PERIOD COVERED
14. SPONSORING AGENCY CODE
IE. SUPPLEMENTARY NOTES
Project Officer: Thompson G. Pace
16. ABSTRACT
The PM,n SIP Development Guideline, EPA-450/2-86-001, prepared by U. S. EPA,
recommends that both dispersion models (DM) and receptor models, primarily the
Chemical Mass Balance (CMB) model, be used to determine the contributing sources.
When two or more independent methods, such as DM and CMB, are used to develop
estimates for the contributing sources, differences are bound to occur.
This orotocol consists of an eight step procedure for examining and reconciling
differences in model estimates an a systematic, justifiable way. The steps outline
a procedure for intercomparison of results, model refinement, recomparison and
development of final model estimates.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.IDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
State Implementation Plan
Receptor Model ing
Dispersion Modeling
PM10
Model Reconciliation
Protocol
18. DISTRIBUTION STATEMENT
19. SECURITY CLASS (This Keportj
21. NO. OF PAGES
42
20. SECURITY CLASS (This page I
22. PRICE
EPA Form 2220-1 (R«v. 4-77) PREVIOUS EDITION IS OBSOLETE
------- |