CRC Report No. A-34 - Coordinating Research Council

April 2005
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. The impacts of spatial heterogeneity in
emission 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.
Measure of “Total” Organic Compounds
There are several ways of defining the “total” of organic compounds for an emissions inventory
or a receptor model analysis. Measures that are most appropriate for an emissions inventory (e.g.
ROG, TOG) tend to be all inclusive, whereas a receptor model such as CMB is applied for a
specific set of VOC species such as the PAMS list. Bridging the gap between emission
inventories and receptor model results requires assumptions about relationships between
different measures of total organic compounds, such as PAMS/ROG ratios. The emission
inventory modeling for this revealed relationships between TOG, ROG and PAMS for 22 source
category groupings. The ROG/TOG ratios ranged from 0.09 to 1.0. The PAMS/ROG ratios
ranged from 0.23 to 2.4 (the sum of PAMS can be greater than ROG because ethane is included
in PAMS but excluded from ROG).
Relationships between different measures of total organic compounds are more difficult to
characterize for some source categories than others. Difficulties for biogenic emissions are that
the PAMS species list contains only a few biogenic compounds (e.g., isoprene, ethene) and that
many non-PAMS biogenic compounds require specialized measurement methods because they
are oxygenated and/or heavy (e.g., 15 carbon sesquiterpenes; Guenther et al., 1999). Solvents
have similar difficulties to biogenics, especially for oxygenated organics used in water-based
formulations. Diesel emissions include many heavier compounds (more than 12 carbons) that
require specialized measurement methods and may be difficult to classify as included or
excluded from measures of volatile organics (e.g., ROG, VOC). These considerations introduce
uncertainties and may result in positive or negative biases in source category contributions.
Source category contributions may vary significantly (by more than a factor of 2) depending
upon how total organic compounds are measured. It follows that comparing different measures
of total organic compounds between receptor modeling and emission inventories will introduce
biases (either positive or negative) in comparisons of source category contributions. Corrections
may be applied by assuming certain ratios (e.g., PAMS/ROG) for each category, but such
assumptions are external to the receptor modeling process and must be accounted for separately.
Recommendations for receptor modeling studies are: Clearly state the measure of organic
compounds that is apportioned by the receptor model and define any conversion factors used to
adjust this to a different basis. Conversion factors (e.g., PAMS/ROG ratios) must be consistent
H:\crca34-receptor\report\Final\ExecSum_r.doc
ES-9