Semivolatile POA and parameterized total combustion SOA in CMAQv5.2: impacts on source strength and partitioning
Mounting evidence from field and laboratory observations coupled with atmospheric model analyses shows that primary combustion emissions of organic compounds dynamically partition between the vapor and particulate phases, especially as near-source emissions dilute and cool to ambient conditions....
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-09-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/17/11107/2017/acp-17-11107-2017.pdf |
| Summary: | Mounting evidence from field and laboratory
observations coupled with atmospheric model analyses shows that primary
combustion emissions of organic compounds dynamically partition between the
vapor and particulate phases, especially as near-source emissions dilute and
cool to ambient conditions. The most recent version of the Community
Multiscale Air Quality model version 5.2 (CMAQv5.2) accounts for the semivolatile
partitioning and gas-phase aging of these primary organic aerosol (POA)
compounds consistent with experimentally derived parameterizations. We also
include a new surrogate species, potential secondary organic aerosol from
combustion emissions (pcSOA), which provides a representation of the secondary organic aerosol (SOA) from
anthropogenic combustion sources that could be missing from current chemical
transport model predictions. The reasons for this missing mass likely include
the following: (1) unspeciated semivolatile and intermediate volatility
organic compound (SVOC and IVOC, respectively) emissions missing from current
inventories, (2) multigenerational aging of organic vapor products from known
SOA precursors (e.g., toluene, alkanes), (3) underestimation of SOA yields
due to vapor wall losses in smog chamber experiments, and (4) reversible
organic compounds–water
interactions and/or aqueous-phase processing of known organic
vapor emissions. CMAQ predicts the spatially averaged contribution of pcSOA
to OA surface concentrations in the continental United States to be 38.6
and 23.6 % in the 2011 winter and summer, respectively.
<br><br>
Whereas many past modeling studies focused on a particular measurement
campaign, season, location, or model configuration, we endeavor to evaluate
the model and important uncertain parameters with a comprehensive set of
United States-based model runs using multiple horizontal scales (4 and
12 km), gas-phase chemical mechanisms, and seasons and years. The model with
representation of semivolatile POA improves predictions of hourly OA
observations over the traditional nonvolatile model at sites during field
campaigns in southern California (CalNex, May–June 2010), northern
California (CARES, June 2010), the southeast US (SOAS, June 2013; SEARCH,
January and July, 2011). Model improvements manifest better correlations
(e.g., the correlation coefficient at Pasadena at night increases from 0.38 to
0.62) and reductions in underprediction during the photochemically active
afternoon period (e.g., bias at Pasadena from −5.62 to
−2.42 µg m<sup>−3</sup>). Daily averaged predictions of observations
at routine-monitoring networks from simulations over the continental US
(CONUS) in 2011 show modest improvement during winter, with mean biases
reducing from 1.14 to 0.73 µg m<sup>−3</sup>, but less change in the
summer when the decreases from POA evaporation were similar to the magnitude
of added SOA mass. Because the model-performance improvement realized by
including the relatively simple pcSOA approach is similar to that of
more-complicated parameterizations of OA formation and aging, we recommend
caution when applying these more-complicated approaches as they currently
rely on numerous uncertain parameters.
<br><br>
The pcSOA parameters optimized for performance at the southern and northern
California sites lead to higher OA formation than is observed in the CONUS
evaluation. This may be due to any of the following: variations in real pcSOA
in different regions or time periods, too-high concentrations of other OA
sources in the model that are important over the larger domain, or other
model issues such as loss processes. This discrepancy is likely regionally
and temporally dependent and driven by interferences from factors like
varying emissions and chemical regimes. |
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| ISSN: | 1680-7316 1680-7324 |