CRC Report No. A-34 - Coordinating Research Council

April 2005
3. All significant sources have been identified and had their emissions characterized.
Results from this study confirm that this is an important assumption for CMB. Receptor models
in general, and CMB in particular, seek an optimum source apportionment of the ambient VOCs
among the source profiles allowed in the model. This creates a tendency to apportion all sources
to just the sources allowed in the analysis if source profiles have species in common. The
resulting bias over-estimates the contributions of expected or identifiable sources.
When hypothetical industrial sources were added to the emission inventory, CMB tended to
apportion the hypothetical industrial emissions to other sources, such as gasoline and solvents,
resulting in an over-estimation of gasoline and solvents. The significance of this result is open to
interpretation. Since the receptor modelers did not know that hypothetical sources had been
added to the emission inventory, it can be argued that important sources of emissions were
unidentified in the CMB because the presence of the source was unexpected, causing assumption
3 to be violated because sources were unidentified. An alternate view is that the receptor
modelers knew that real industrial sources were located close to the locations of the hypothetical
industrial sources, so that the hypothetical sources merely raised the importance of a known
source category, causing assumption 3 to be violated because sources were incorrectly
characterized. These possible interpretations lead to two conclusions: (1) That violating
assumption 3 can result in biased CMB apportionments, and: (2) That assumption 3 may be
violated either by failing to realize that a source exists or misunderstanding the characteristics of
a source.
Characterizing emissions sources (i.e., determining the source profiles) strongly influences the
outcome of CMB receptor modeling. With minimal source profile information (less than in
typical real-world CMB applications) the receptor modelers were able to select profiles that fit
the ambient data, in most cases resulting in source apportionments that showed some skill but
also contained biases. The biases tended toward over-estimating some sources (i.e., gasoline)
and under-estimating others (i.e., solvents). Some of the source category identifications (i.e.,
compressed natural gas and liquid petroleum gas) required careful interpretation because the
names assigned by the receptor modelers described the chemical nature of the emissions (i.e.,
ethane and propane, respectively) but not the source of the emissions.
With typically available source profile information (e.g., a tunnel study and fuel samples) the
performance of CMB was improved for solvents but degraded for diesel relative to analyses with
minimal information. The major changes were for solvents and diesel due to issues of profile colinearity
and choice of fitting species. The receptor modelers eliminated nonane, decane and
undecane as fitting species and the gasoline and diesel profiles became somewhat co-linear and
the apportionment for diesel was degraded.
When the receptor modelers were provided with detailed information on the sources present and
their source profiles (which is not a realistic scenario for the real world), the CMB results were
increasingly accurate and CMB performance was limited by other factors such as the degree of
profile co-linearity.
4. The number of source categories is less than the number of species; i.e., there are degrees of
freedom available in the analysis.
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