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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 dellAtmosfera 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.
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.
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.
BOLCHEM Flow Chart
Heterogeneous chemistry
Aerosol model M7
aerosol optical properties, cloud condensation
nuclei
Meteorological Model (BOLAM)
Winds, T, P, q, Clouds, Radiative Fluxes
Gas Chemistry (SAPRC90/CBIV)
(Vignati et al., 2004)
Transport diffusion
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.
Emissions gasaerosol
Dry and wet removal gasaerosol
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.
MODIS
BOLCHEM
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, doi10.1029/2003JD004485.
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.