Model assessment and verification - Air Pollution Modeling and Its Application - page 303

and lowest in the opposite case - wind from areas, such as Atlantic ocean, with
low and also partly missing emissions.
The main contributions to modelled AOD at 550 nm averaged over the whole
period of computations, come from sulphates (47%), nitrates (30%), and fine-
mode primary particles (16%) while the contributions of sea salt and coarse
primary particles are negligible. The contribution of fire emissions gives about one
fourth of the total AOD raising up to 30% for specific periods.
3. Conclusions
Selected cases by-construction evaluate the model ability to work in complicated
conditions sayng little about the standard situations.
The model tends to fit well or over-estimate PM under-estimating most of
gases (except for the highest PM-10 peak 27.2.2003). PM also has the highest
correlation at hourly averaging. Comparison with MODIS AOD showed that the
model correctly reproduces the overall spatial pattern being in most cases within a
factor of 1.5 from the total spatially-averaged AOD.
The pattern was sensitive to chemical composition of the emission. In spring-
2006, the visibility degradation was largely driven by sulphates, nitrates and
primary PM-2.5, while PM-10 and sea salt contributions were negligible. Wild-
land fires were the major contributor of both PM and reactive gases.
Synchronization by meteorological conditions was found between the plumes
from different sources, such as anthropogenic emitters and biomass burning.
References
Kaufman, Y. J., Tanre D., Remer L. A., Vermote E. F., Chu A., Holben B. N. (1997) Operational
remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging
spectroradiometer, J. Geophys. Res , 102 , D14. 17,051-17,067.
Sofiev, M., Galperin, M., Genikhovich, E. (2008) Construction and evaluation of Eulerian
dynamic core for the air quality and emergency modeling system SILAM. NATO Science for
piece and security Serties C: Environmental Security. Air pollution modelling and its
application , XIX , Borrego, C., Miranda, A.I. (eds.), Springer, pp. 699-701.
Sofiev, M., Siljamo, P., Valkama, I., Ilvonen, M., Kukkonen, J. (2006) A dispersion modelling
system SILAM and its evaluation against ETEX data. Atmosph.Environ. , 40 , 674-685,
DOI:10.1016/j.atmosenv.2005.09.069.
Stern, R., Builtjes P., Schaap M., Timmermans R., Vautard R., Hodzic A., Memmesheimer M.,
Feldmann H., Renner E., Wolke R., Kerschbaumer A. (2008) A model inter-comparison study
focussing on episodes with elevated PM10 concentrations Atmosph. Environ. , 42 , 19 , 4567-
4588.