CRC Report No. A-34 - Coordinating Research Council

April 2005
species (CNG and LPG for ethane and propane) that provided a way to account for the mass of
these species. As discussed above, the names (CNG/aged and LPG) attached to these profiles
needed careful interpretation because they describe chemical appearances rather than emission
inventory source categories.
Analyses of the grid model experiments revealed the extent to which source contributions were
degraded by chemical reaction. The extent of chemical degradation varied with source profile
and receptor location: high reactivity categories (e.g., biogenics) were depleted by up to 90%
whereas low reactivity categories (e.g., CNG, LPG) were depleted ~10% by chemical reaction.
As expected, the extent of chemical degradation was greater at downwind receptors.
The experimental findings reviewed above show that CMB generally was able to correctly
apportion the sum of PAMS species present in the air samples even when source profiles had
been altered by chemically aging. Chemical reaction also changes the relative amounts of low
and high reactivity source categories. For example, high reactivity biogenic emissions were
degraded much more than low reactivity CNG or LPG emissions. The impact of chemical aging
on source contributions must be considered when comparing source apportionment results to
emission inventories. The experimental findings were that: Source apportionment results for
highly reactive emissions categories (e.g., biogenic emissions) significantly under-estimate the
actual contribution due to chemical degradation (finding 23). Source apportionment results for
low reactivity categories (e.g. those labeled CNG or LPG) may over-estimate the real
contributions of these categories due to chemical degradation (finding 24). Apart from biases for
high and low reactivity categories, source apportionment results for other categories are not
greatly influenced by chemical degradation except at far downwind receptors (finding 25).
Spatial Heterogeneity in Emissions Sources
CMB source contributions are often compared to emission inventories with the goal of
evaluating and improving the emissions inventory. Air samples at a receptor location may be
dissimilar from the local emissions because of spatial heterogeneity in the distribution of
emissions sources. Comparisons of the known source contributions in air samples to local
emissions showed that the impacts of spatial heterogeneity were most pronounced for downwind
receptors and receptors located near major point sources (findings 23 and 24). 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.
Effects of spatial heterogeneity in emissions are likely under represented in the grid model
experiments of this study compared to the real-world because (1) the grid model cannot represent
micro-scale (sub 5-km) variations in emissions, and (2) grid modelers have limited information
to spatially allocate emissions and so use spatial surrogates to allocate emissions (e.g.,
population) which simplify the texture in the emissions inventory. Micro-scale impacts present a
challenge to selecting regionally representative monitoring locations in a real-world study where
the goal is to characterize a regional emissions inventory.
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