کاربرد تکنیک‌های چندمتغیره در منطقه‌بندی هواویزه‌ها (Aod) بر روی ایران

در مطالعه حاضر با استفاده از داده های روزانه ماهواره ای شبیه سازی شده از عمق نوری آئروسل (aod) در 550 نانومتر بر روی گستره ایران سعی شد تا مناطق همگن آئروسل بر روی ایران با استفاده از تکنیک های چند متغیره مدلسازی و آشکار گردد. تکنیک های بُرداری با کاهش حجم متغیرها به بُردارهای ویژه و صدک 85، شش الگوی مجزای مکانی را منطقه بندی و تفکیک کرد که از پهنه های عمده تحت تاثیر آئروسل بر روی پهنه ایران هستند. جزئیات پراکنش رخدادهای حدی بر پایه صدک 95 بی هنجاری روزانه ی سیگنال ها نشان داد که انتشار و دوری و نزدیکی مناطق منبع آئروسل از مناطق شتاسایی شده ی روی ایران موجب شکل گیری الگوهای غالب مکانی آئروسل در گوشه و کنار کشور شده است. زیرا، رشد و گسترش فرین های آئروسل و انتشار آنها از مناطق منبع آئروسل در سطح خاورمیانه و آسیای مرکزی نمایان کرد که تفکیک جغرافیایی آئروسل به ساختارهای فضاییمکانی ساده و همگن با شباهت های درون گروهی زیاد در حین تفاوت آشکار برون گروهی تایید می کند که هر کدام از مناطق همگن ایران تحت تاثیر جریان و پراکنش مکانی فرین های منطقه ای متفاوتی تشکیل شده است. بنابراین، تلفیق معیارهای ماهواره ای با نکنیک های کاربردی بُرداری در علوم جویدینامیکی، نه تنها خلاء داده های مشاهداتی سطحی را پر کرده است، بلکه رویکردی به مراتب جغرافیایی تر از مخاطرات محیطی را در فالب شکلی ساده تر نشان داده و آن اشکال همگنِ تفکیک شده ی جغرافیایی را قابل تفسیرتر کرده است.

Application of multivariate techniques in-line with spatial regionalization of AOD over Iran

Application of multivariate techniques inline with spatial regionalization of AOD over Iran IntroductionModels, satellites and terrestrial datasets have been used to detect and characterize aerosol. Nontheless, micoscale classification using remote sensing parameters considers as a deficiency. Thus, regionalizion and modeling aerosol without regard to political boundaries or a specific stations over Iran demonstrates the spatial distribution of simple AOD structures. Materials and methods Present study attempted to simulate and detect homogeneous areaes of aerosol in Iran using AOD (areosol optical depth) datast at 550 nm across Iran. Among the eigen techniques, principal component analysis (PCA) is the most applicable and controversial classification applied as multivariate analysis approach. In the line of the target, PCA, SMode separate the AOD subgroups with similar correlations. In the mode, m time series apply to each n station or grid points as a variable in the analysis, which is the territory of the region or geographical area. Mathematically, if the input data column in the Z matrix is applied as mathematical variables and the Z matrix has n points in the time series and m is the time step, then in the Zs decomposition has 3654 ×9985. In addition, the scree test and North #39;s rule were used to cutoff the principal components and to select the number of appropriate special vectors to be kept. Results and Discussion For the study purpose, 85 percentaile of loadings were used to determine AOD areas over Iran. Using the method, the spatial patterns of Iran #39;s aerosolshave been divided into six subregions, which are the major centers affected by the AOD. These major AOD hotspots affect by AOD extermes that are originated from aerosol surrounding sources. So that, the geographical location of sources areas have caused the northeastern atmosphere of Iran to be influenced by severe storms originating from the Karakum Desert. The same is correct concerning the East and Southeast regions. While, the intensification and transfer of aerosol from the Sistan plain to the south is increased AOD load over southeast Iran. Moreover, this study revealed a set points associated with distinguishing spatial differences between the westnorthwest and southwest regions as well as central region that have not addressed in previous studies because of focus on groundbased observations. Also, the method illustrated that formation of the identified regions are a function of the volume, growth, and spread of aerosol particles resulting from the source regions in the Middle East. Finally, the classification techniques converting dynamic phenomenon such as aerosol into simpler structures presented a interpretable understanding of the geographical distribution of the phenomenon.ConclusionThe present study identified the spatial patterns of AOD hotspots into six distinct regions including northeast, westnorthwest, southeast, southwest, central and eastern Iran affected by the aerosol as well as major centers or high gradient areas. In addition, the present study not only supported by previous studies, but also it make sense a regionalization that was neglected by former studies, whileseperated the boundaries of the AOD areas without considering provincial boundaries. Overall, the classification techniques, PCA, simplified a dynamic phenomenon such as aerosol into a simpler and illustrated geographical and interpretable understanding of the spatial distribution of the phenomenon.Keywords: Aerosol Optical Depth (AOD), Multivariate Techniques, Regionalization, Iran