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Spotlight on the development of the regional air quality
model BOLCHEM: adding aerosol model
Mihaela Mircea, Massimo D'Isidoro, Maria Gabriella Villani, Alberto Maurizi , Francesco Tampieri,
Maria Cristina Facchini, Stefano Decesari, Lorenza Emblico, Sandro Fuzzi, Andrea Buzzi
Istituto di Scienze dell’Atmosfera e del Clima, Consiglio Nazionale delle Ricerche, Bologna, Italy
PREAMBLE
In the last years, many studies have shown that the aerosols besides of changing climate, also affect health. The inhalation of particulate matter both by humans and animals can
produces asthma, lung cancer, cardiovascular issues, and premature death. Therefore, the forecast of aerosol by air quality models is a topic at issue for the scientific
community. Most of air quality models that include transport, dynamics and chemistry of aerosols are coupled offline to meteorology (EMEP, EURAD, CMAQ, CHIMERE). Here,
we spotlight the progress made on coupling online the aerosol model M7 (Vignati et al., 2004) to the regional air quality model BOLCHEM.
M7: size-resolved aerosol microphysical model
BOLCHEM
BOLCHEM is a modeling system that comprise the meteorological model BOLAM
(Buzzi et al., 1994, Buzzi et al., 2003), an algorithm for airborne transport and
diffusion of pollutants and two photochemical mechanisms: SAPRC90 (Carter,
1990) and CB-IV (Gery et al., 1989). The meteorology is coupled online with the
chemistry. The separation of meteorology and chemistry in the offline simulations
lead to a loss of potentially important information about atmospheric processes that
often have a time scale much smaller than the meteorological output frequency
(e.g., cloud formation, rainfall, wind speed and direction). Simultaneous integration
of chemistry and meteorology (without any interpolation in time or space as
generally performed by the offline air quality models) result in good air quality
forecasts over regions with complex topography, like Italy.
For example, ozone concentrations
calculated with BOLCHEM at various
locations in Italy are in good
agreement with measurements even
if they differ slightly: generally the
photochemical
mechanism
SAPRC90 gives higher values than
CB-IV. However, both photochemical
mechanisms reproduce well the
diurnal cycle of ozone.
The M7 model considers the aerosol population divided in two externally mixed
populations: an internally mixed water-soluble particle population and a population of
insoluble particles. The aerosol model includes the main chemical components
identified in atmospheric aerosols: sulfate, black carbon (BC), organic matter (OC),
sea salt (SS) and mineral dust and the composition of each internally mixed mode is
modified by aerosol dynamics, e.g. coagulation and by thermodynamical processes,
e.g. condensation of sulfate on pre-existing particles. The particle populations is
represented by four lognormal modes: nucleation, Aitken, accumulation and coarse.
The rate constants of coagulation and condensation of aerosol are calculated for the
average mode radius. In spite of the simplicity of the “pseudomodal” approach used to
describe the aerosol populations an to calculate the dynamics, M7 has proved to be
able to represent well the aerosol physics and chemistry.
BOLCHEM Flow Chart
aerosol optical properties,
cloud
condensation
nuclei
Meteorological
Model
(BOLAM)
Transport
& diffusion
Winds, T, P, q,
Clouds, Radiative Fluxes
Aerosol model
M7
Heterogeneous
chemistry
Gas Chemistry
(SAPRC90/CBIV)
Emissions
gas&aerosol
Dry and wet removal
gas&aerosol
(Vignati et al., 2004)
However, a lot of uncertainties in aerosol modeling arise from uncertainties in modeling
processes emissions such as dust production from crustal soil source, sea salt or from
gas emissions inventory. Therefore, now is underway the assessment of the
magnitude of these uncertainties over Italy by means of remote sensing and in-situ
measurements.
Saharan Dust over the Mediterranean Sea: July 16, 2003
The incorporation of the aerosol model M7 into BOLCHEM involves the addition of
other aerosol processes such as emissions, dry and wet removal, heterogeneous
reactions. The science modules used to represent these processes are selected
such as to preserve the computational efficiency of M7 and to include the most
advanced treatments.
In the future, the aerosols effects on the solar part of the spectrum and on
the microhysics and dynamics of clouds will be added since the online
coupling of the models favors the consideration of the aerosol feedbacks.
M7 has been already implemented and tested in ECHAM5 GCM model,
therefore, we plan to investigate with BOLCHEM-M7 the potential effect of
climate change on air quality at regional level, over Italy.
BOLCHEM
MODIS
REFERENCES
Buzzi, A.; Fantini, M.; Malguzzi, P.; Nerozzi, F., Meteorol. Atmos. Phys., 1994. 53, 137-153.
Buzzi, A.; D'Isidoro, M.; Diavolio, S.; Q. J. R. Meteorol. Soc., 2003, 129, 1795-1818.
Carter,W. P .L.; 1990, Atmos. Environ., 24A, 481-518.M.
Gery, W.; Witten, G. Z.; Killus, J. P.; Dodge. M. C.; J. Geophys. Res., 1989, 94, D10, 12925-12956.
Vignati, E., Wilson, J., Stier, P., J. Geophys. Res., 2004, 109, doi:10.1029/2003JD004485.
ACKNOWLEDGEMENTS
This work was conducted in the frame of EC FP6 NoE ACCENT
(Atmospheric Composition Change, the European NeTwork of
Excellence) and GEMS EC project.