Sulfate
The simulation of sulfate aerosol and sulfate wet deposition is treated adequately in regional
models, including CMAQ. However, further improvements in model simulation could be achieved
by improved precipitation amount/spatial distribution. Also, the SO2 oxidation rate constants
should be adjusted to better match the winter-time SO4 concentrations.
Meteorology, Precipitation
The simulation of precipitation amount and spatial distribution is one of the weaknesses of
meteorological models. The assimilation of observation-based data would improve the model
performance for wet deposition.
Model Parameter, Reaction Rate
The 2004 version of the CMAQ model continues to underestimate the winter-time sulfate aerosol
concentration. It is presumed that this low bias is due to the unadequate consideration of liquidphase SO2 oxidationin the winter.
Model Parameter, Seasonal
As a consequence of the above, the seasonality of SO2 conversion rates would need to be
improved so as to reduce the winter-time negative bias.
Nitrate
The simulation of nitrate aerosol is one of the weakest areas of regional scale aerosol simulation.
Neither the emission drivers, nor the chemical kinetics are fully understood. Devising a
meaningful data assimilation scheme will require considerable iteration utilizing the best available
chemical observations, emission estimates (inverse modeling) and chemical modeling.
Meteorology, RH, Temperature, Clouds
It is presumed, that one of the drivers of NO3 precursor emissions is ambient temperature and
humidity. Evidently, during snow melt the deposited nitrate is released to the atmoshere and
causes the formation of nitrate aerosols. This phenomenon has been observed to cause
excessive nitrate aerosol concentrations in the upper Midwest (Iowa, Minnesota).
Meteorology, Precipitation
The simulation of precipitation amount and spatial distribution is one of the weaknesses of
meteorological models. As in the case for SO4, the assimilation of observation-based data would
improve the model performance for wet deposition.
Emission, Rate
The emission rate of NO3 precursors is poorly understood.
Emission, Spatial
The spatial pattern of NO3 precursors could be inferred from the spatial pattern of NO3 wet
deposition and NO3 aerosol concentration. These data are available through the NADP and
VIEWS monitoring networks.
Emission, Seasonal
Nitrate aerosol exhibits a very strong seasonality with a peak in the winter season. At this time, it
is not apparent if the seasonality is due to the emissions or it is driven by chemical kinetics.
Emission, Episodic
It is now well documented that winter-time nitrate aerosols tend to occur during nitrate events that
have well defined spatial and temporal boundaries. The nitrate concentration could be estimated
from the difference between the observed fine particle mass and the model sulfate concentration.
Model Parameter, Seasonal
Most nitrate aerosol is in meta-stable state. During the low temperatures in the winter, nitrate is
more in the aerosol phase while in the warm summer period the nitrate is virtually all in the gas
phase.The seasonal character of this equilibrium will need to be better understood.
OC Biogenic
Biogenic aerosols originate from the emission of the organic compounds, e.g. pinenes from the
vegetation. The current CMAQ model incorporates a sofisticated biogenic emissions module as a
driver of biogenic organic aerosol formation.
Emission, Rate
Formaldehyde is a stable chemical product of biogenic emissions which can be detected from
satellites in real-time. The absolute magnitude of biogenic OC emissions could be improved by
the incorporation of real-time formaldehyde data from the OMI sensor.
OC Smoke
Smoke aerosol originates from biomass burning of agricultural products, prescribed fires of
accumulated forest biomass, or from wild fires in forests or grassland. The main chemical
constituents of biomass smoke are organic carbon. However, smoke also contains water, sulfate,
minerals, and many other compounds. Biomass smoke emissions in inherently non-steady and
or unpredictable in time and space. The vertical transport of smoke is strongly influenced by the
thermal energy in smoke plumes and the smoke chemical composition varies considerably
depending both on emission characteristics as well as the ambient conditions. Modeling of
smoke emissions has been hampered by the poor characterization of emissions, transport, and
chemical processes that influence the dynamics of smoke aerosols.
Meteorology, Horizontal Transport
The onset of agricultural, prescribed as well as wild fires necessitates dry conditions over
extended periods. The emission rate is also affected by the horizontal winds.
Meteorology, Vertical Transport
The smoke plume height is determined by the buoyancy of the smoke plumes as well as the
stability of the ambient atmosphere. The plume height, in turn, determines the direction and
speed of plume transport as well as the horizontal and vertical dispersion. A particular question is
whether the smoke plume is elevated or is it diffused to the surface where it can impact human
health and safety.
Emission, Rate
The emission rate of biomass smoke aerosols is notoriously difficult to characterize in terms of
space, time, and chemical pattern. It is hoped that satellite detection of smoke will allow the
estimation of smoke emission rates.
Emission, Spatial
Satellite detection of smoke is currently the most reliable mean of estimating the smoke spatial
distribution.
Emission, Diurnal
The emission rate of biomass smoke is strongly modulated by a diurnal cycle of ambient
temperature. Thus, the smoke emission rates used in regional models need to include the diurnal
cycle.
Emission, Episodic
Biomass smoke emissions are inherently episodic since they require dry conditions for the
biomass fuel as well as for the ambient air. Furthermore, the onset of fires is typically caused by
unpredictably.
OC Anthropogenic
Anthropogenic organics are also significant contributors to PM2.5 particularly in urbanindustrial areas. OC emissions occur from both the transportation as well as the industrial
sector.
Emission, Rate
There is evidence that the anthropogenic OC emission rate in the current CMAQ model is biased
high. Observations of urban/rural OC indicate less urban impact than the models. This is
particularly true in the summer season.
Emission, Weekly
Emission inventories typically accommodate long-term trends as well as seasonal cycles.
However, neither the weekly nor the diurnal cycle is incorporated in standadrd emission
inventories. For anthropogenic OC emissions inclusion of the weekly and diurnal cycle would
considerably narrow the model observation difference.
EC Anthropogenic
The emission and the modeling of anthropogenic EC is reasonably well understood. The primary
EC source is diesel fuel combustion.
Emission, Weekly
For anthropogenic EC emissions inclusion of the weekly and diurnal cycle would considerably
narrow the model observation difference.
Fine Soil
Fine soil particles contribute 5-50% of the fine particle mass depending on the location and
season. The modeling of fine soil particles exhibits the lowest skill level among the PM2.5
species. In fact, in the earlier versions of CMAQ the observation-model correlation was negative.
Since the model placed most of the PM2.5 emissions into urban areas, while the observations of
fine PM2.5 soil indicated rural and intercontinental sources.
Meteorology, Vertical Transport
It is evident that a major source of fine particle soil is natural wind-blown dust from North
American, African and East Asian sources.
Meteorology, Precipitation
Dust is removed primarily by settling and wet deposition. An improved precipation module would
also benefit the simulation of PM2.5 concentration.
Emission, Rate
The inclusion of wind blown dust emission module into CMAQ would considerably reduce the
model bias and error.
Emission, Episodic
While anthropogenic dust emission from road dust industrial sources are regular and predictable,
the emission rate of wind-blown dust is highly episodic and driven by the occurance of strong
winds over poorly vegetated sandy surfaces that are dry and do not have solid crust.
Sea Salt
Seas salt is only present over the ocean and coastal areas. The concentration and the wet
deposition of the sea salt i reasonably weel quantified by the aerosl chemistry and precipitaion
chemistry networks. However, the modeling of the sea salt is either omitted or poorly
parameterized in current regional models. However, since sea salt contribution to PM2.5 is rather
limited the improvement of the sea salt module in CMAQ does not seem to be of high priority.