CRC Report No. A-34 - Coordinating Research Council

April 2005
Hourly vs. Average Results
13. Focusing on morning (6-9 am) samples did not clearly improve the accuracy of CMB.
Correspondingly, focusing on afternoon samples (1-4 pm) did not clearly degrade the
performance of CMB, especially for samples in areas of high emissions density. Problems
for downwind samples are largely independent of the time of day.
14. Reducing the number of samples at each receptor from 48 to 12 in order to restrict the timeperiod
of analysis to 3 hours (e.g., 6-9 am) did degrade CMB performance.
15. Looking at hourly CMB results degraded performance even more than looking at 3-hourly
results because the sample size was further reduced from 12 to 4.
16. The hourly CMB analysis could discern major features of temporal variations in emissions
(e.g., rush hour) but not minor features.
Experiments 9-12: Approaching Ideal Conditions
17. CMB performs very well for 3-D cases under ideal conditions where source profiles are well
characterized, source profiles are not co-linear, there is no chemical decay, and no sampling
noise.
18. Limitations to CMB performance in ideal 3-D cases are more related to co-linear source
profiles than either chemical decay or random measurement noise.
19. CMB performance was very robust against effects of chemical decay in an ideal case with
fairly simple source contributions.
20. The experiments performed leave open a possibility that chemical decay could be a greater
impediment to CMB in more complex cases with more sources that are more co-linear.
21. CMB performance was very robust against random sampling noise in an ideal case with
fairly simple source contributions. Experiment 8 showed that CMB performance also was
robust against random sampling noise with more complex sources and profiles.
Emissions Contribution vs. Ambient Contribution
22. The source category composition of air samples at a receptor location may be dissimilar from
the contribution of local emissions because of spatial heterogeneity in the emissions
inventory.
23. Source apportionment results for biogenic emissions significantly under-estimate the real
contribution of biogenic emissions due to chemical degradation.
24. Source apportionment results labeled CNG or LPG will over-estimate the real contributions
of these categories due to chemical degradation and because the category labels are
misleading.
25. Apart from biases for biogenics, CNG and LPG, source apportionment results for other
source categories are not greatly influenced by chemical degradation except at far downwind
receptors.
26. The impacts of spatial heterogeneity in emissions inventories were most pronounced for
downwind receptors and receptors located near major point sources.
27. The impacts of spatial heterogeneity in emissions inventories were least, but not absent, for
urban/suburban receptors with a mix of residential/commercial/industrial emissions.
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