PREAMBLE

1. 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. 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. Dry and wet removal gas&aerosol Emissions gas&aerosol 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, 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 GEMSEC project.