Fatemeh Jahani Cherebargh; Mehdi Akhoondzadeh Hanzaei
Abstract
Extended Abstract Aerosols are small (sub-micron to several microns) suspended particles in the solid or liquid phase in the atmosphere. The main origins of aerosols are natural and anthropogenic. They can be directly emitted as particles (primary aerosols) into the atmosphere namely, mineral aerosol, ...
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Extended Abstract Aerosols are small (sub-micron to several microns) suspended particles in the solid or liquid phase in the atmosphere. The main origins of aerosols are natural and anthropogenic. They can be directly emitted as particles (primary aerosols) into the atmosphere namely, mineral aerosol, sea salt, volcanic eruptions, organic aerosols, industrial dust, soot, biomass burning, etc. They can also be the result of chemical reactions (secondary aerosols) namely, sulfates from biogenic gases or volcanic and nitrates from transportation and diffusion of aerosol particles from the source region depend on wind vector and wind strength. Aerosols are ever present and highly varying constituents of our atmosphere. They play roles in many physical and chemical processes that shape the composition of the atmosphere and thereby affect cloud formation, visibility, and air quality. They interact both directly and indirectly with radiation and thus affect the amount of radiative energy reaching the surface and reflected to space. The shortwave part of the radiative energy at the surface (insolation) is an important component of the surface energy budget, and a necessary input to models of land-surface processes. Aerosol Optical Thickness (AOT) is calculated by measuring light absorption at specific wavelengths of the visible spectrum. For the most widely used AOT data product, the absorption at 550 nm is the preferred wavelength for measurement (In the visible spectrum, humans perceive a light wavelength measuring 550 nm as a shade of green). AOT is a dimensionless quantity, expressing the negative logarithm of the fraction of radiation (e.g., light) that is not scattered or absorbed on a path. High AOT indicates a large quantity of aerosols, and thus a significant amount of absorption and scattering of radiation (i.e., light). Low AOT indicates clearer air with fewer aerosols and increased transmission of radiation. Increasing aerosol concentrations can thus affect global temperature and the radiation balance of the globe by reducing the amount of radiation reaching the Earth’s surface, and that reduction can result in lower air temperatures. Penetration of the large particles into the atmosphere in certain cases leads to decreasing the particles mobility and then dropping the conductivity, which will increase the electric field but aerosol measurements in the seismically active zones are more complicated due to the mosaic character of the gas emanation in the seismic zones and the uncertainty of aerosol origin in gas probes. Some remote sensing satellites due to their suitable temporal, spatial and spectral resolutions provide useful information of time and spatial distributions of Aerosols. This leads to creating an appropriate database for statistical study of the seismic atmospheric effects. The AOD measurement is taken by the MODIS sun-synchronous instrument onboard Terra and Aqua satellites every day. The satellites provide more continuous coverage nearer to the poles but there are more gaps in the coverage of the satellite nearer to the equator. AOT can be determined by implementing different methods on satellite images, but it is a difficult task to achieve it because solar lights are reflected by the atmosphere and the whole solar lights do not hit the ground. The most famous methods used to derive aerosol parameters are Dark Dense Vegetation (DDV), deep blue algorithm and synergy of Terra and Aqua MODIS (SYNTAM). SYNTAM approach can remove limitations in deriving AOT by combining data from two sensors of MODIS of TERRA and AQUA satellites and this method gives the right results. In this study, SYNTAM method has been applied over a region of Iran to produce an AOT map. The comparison between our results and NASA AOT products for the same time and location shows a good agreement. The result of comparing NASA data and SYNTAM approach with Newton iteration algorithm for the wavelength of 0.55 µm, gives the RMSE equal to 0.253. Therefore SYNTAM could be a robust method to derive AOT map over regions without AERONET ground stations. In the next section, SYNTAM method was combined with nonlinear parametric adjustment model. In this case, the results are more accurate than implementation of SYNTAM method alone. The result of comparing NASA data and SYNTAM approach with nonlinear parametric adjustment model for the wavelength of 0.55 µm, gives the RMSE equal to 0.207.
