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  • 标题:Using MODIS data for nonlinear hazard analysis of the Middle East aerosols
  • 本地全文:下载
  • 作者:Reza Mehrnia ; Saeed Abbaszadeh
  • 期刊名称:Journal of Tethys
  • 电子版ISSN:2345-2471
  • 出版年度:2017
  • 卷号:5
  • 期号:2
  • 页码:184-193
  • 出版社:Journal of Tethys
  • 摘要:Aerosols are among the most important of atmospheric pollutants observed like the microscopic particulate matter in the lower parts of the troposphere. The main purpose of this study is introducing a new method based on satellite images processing results and nonlinear analysis (fractal based) to investigate the origin and dynamical mechanism of aerosols distribution in North Africa and the Middle East. As a rule, the Middle East aerosols subject to non-linear distributions because a chaos reign of atmosphere. We used NASA based MODIS images to measure the atmospheric content of aerosols optical thickness (AOT), nitrogen dioxide (NO2) and carbon monoxide (CO). Spatial analysis of data and seasonal - annual compare of them with together, is representing the exponential relative relationship in aerosols dispersion pattern. By using an innovative variance-distance equation, besides representing relationships between atmospheric pollutants of Iran, Iraq and Arabia with climate changes in the North Africa, two tropospheric regimes of hot and cold weathers were compared according to the energy content and chemical composition of different seasons. In practice, both natural aerosols and industrial pollutants are function of their chaotic behaviors in the cold seasons that are logically greater than the summer months of the years. Therefore, the environmental hazards of winter regimes are more than of the summer. Also, changes of fractal dimension (especially at the Brownian surface of AOT), the possibility of study the remote sensing criteria has provided on the basis of the fractal geometry principles, whereby, a new approach has been introduced to prediction the cumulative behavior and movement pattern of aerosols in Middle East.
  • 关键词:Aerosol; Fractal Analysis; Nonlinear Distribution; Middle East.
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