CRC Report No. A-34 - Coordinating Research Council

April 2005
Overall, there was little change in CMB performance between Rounds 1 and 2. The tunnel study
and other data provided in Round 2 did not improve the apportionment for gasoline emissions,
but did change apportionments for solvents and diesel. The designation of “aged” sample (i.e.,
use of Type 2 pm set of fitting species) in Round 2 was based on site and time rather than by
xylene/benzene ratios. The Type 2 am species fitting set was used for urban and suburban sites
for 0600 to 1800 samples and the Type 2 pm set was used for 1800 to 0600 samples. As
previously mentioned, exclusion of heavier hydrocarbons tends to result in co-linearity between
gasoline and diesel with over-estimation of diesel and under-estimation of surface coatings. This
demonstrates the potential for inter-dependence between CMB apportionments for different
categories and the strong influence of certain key fitting species.
Experiment 1 in Round 4
In Round 4, DRI had detailed information on source categories and profiles going beyond what
could ever be known in the real-world. The results of the Round 4 analysis for experiment 1 are
shown in Figure 4-1c. DRI was able to fit many more source categories in Round 4 than in
earlier Rounds but, to allow direct comparisons, the CMB categories have been aggregated in
Figure 4-1c to the categories used in Rounds 1 and 2.
There were some clear improvements in CMB performance in Round 4 over Rounds 1 and 2.
Since DRI now knew that CNG and LPG were not real emissions categories these
apportionments were eliminated from the CMB analysis (resulting in points at 0,0 in Figure 4-
1c) and consequently the apportionment for emissions sources in the “background” category was
improved. DRI was able to dispense with the CNG/aged and LPG profiles because they had
profile information to identify and account for the true sources of ethane and propane.
The Round 4 apportionments to gasoline were accurate at all sites except the downwind
receptors (Crestline, Lake Perris) and at Long Beach. The apportionments to solvents also
improved and were quite accurate. Biogenics remained accurately apportioned because the
PAMS contribution is dominated by a single species (isoprene). The CMB apportionments for
diesel showed good correlation but were too high, especially in downwind areas.
Performance for diesel systematically degraded from Round 1 to Round 4 as the source profile
information improved. The tendency was to over-estimate diesel. The reasons for this were not
confirmed, however diesel also was over-estimated in experiment 12 (discussed below) where
conditions for CMB were nearly ideal. It appears that diesel is susceptible to systematic errors
arising from profile co-linearity when only the 55 PAMS species are available. DRI encounters
this issue in real-world CMB studies and accordingly prefers to have heavy hydrocarbon data (>
C12) to support source apportionment of diesel emissions (DRI requested this type of
information in Round 2 but ENVIRON could not provide reliable heavy hydrocarbon data from
the available emissions profiles).
The Round 4 CMB apportionments were poorest at the downwind receptors (Crestline and Lake
Perris) and at Long Beach. The poorer performance at downwind receptors is attributed to
chemical aging of emissions during transport from upwind source regions and reliance on a
smaller set of fitting species for aged air samples. A comparison of actual/emissions
contributions (described below) showed that the downwind receptors were dominated by
transported emissions, as expected. Experiment 2 (discussed below) investigated atmospheric
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