تحلیل فراوانی تغییرات مکانی طوفان‌های گرد و غبار در فلات مرکزی ایران

یکی از بلایای طبیعی که متاسفانه هر ساله خسارت‌های فراوانی به ویژه در نواحی خشک و نیمه‌خشک بر جا می‌گذارد، پدیده ی گرد و غبار است. افزایش آلودگی و در پی آن کاهش شاخص کیفیت هوا برای جوامع بشری، از مهم ترین نتایج وقوع گرد و غبار می ‌باشد. به همین منظور، تحقیق و مطالعه ‌ی این پدیده امری ضروری به نظر می‌رسد. شناسایی کانون‌های گرد و غبار و آگاهی داشتن از فراوانی و تغییرات مکانی روزهای گرد و غبار، اهمیت زیادی دارد. طوفان‌های گرد و غبار در منطقه ‌ی فلات مرکزی ایران با وجود بیابان‌های لوت و دشت کویر (بیابان مرکزی)، از مهم ترین معضلات زیست محیطی این مناطق به شمار می‌ رود. برای بررسی این طوفان، از 37 ایستگاه هواشناسی سینوپتیک در طی دوره ی آماری 2018-1999 استفاده شد. برای شناسایی ایستگاه‌های سینوپتیک مهم و فراوانی روزهای گرد و غبار در دوره بازگشت‌های 5، 25 و 50 ساله، از توزیع‌های آماری استفاده شد. نتایج نشان داد توزیع‌های آماری johnson sb و (loglogistic(3p، مناسب ترین توزیع‌های آماری منطقه است. نتایج تحلیل فراوانی و تغییرات الگوی مکانی در منطقه ‌ی مورد مطالعه در دوره بازگشت‌های 5، 25 و 50 ساله نیز نشان داد که قسمت‌های جنوب شرقی منطقه (استان‌های سیستان و بلوچستان، استان کرمان) به دلیل مجاورت با تالاب های هامون و جازموریان و قسمت‌های شرقی استان اصفهان و جنوب استان سمنان که در مجاورت بیابان مرکزی قرار دارند بیشترین فراوانی روزهای گرد و غبار را خواهند داشت.

Frequency analysis of spatial variations of dust storms in the central plateau of Iran

Extended abstract1 IntroductionDust has a significant impact on radiation budget, global biogeochemical cycles, soil structure, atmospheric chemical composition, air quality and public health. Natural and human factors have increased the frequency and severity of dust storms in the country in recent years. The most important natural factors are rainfall, drought persistence and global warming, and human factors include surface water control, agricultural development and uncontrolled groundwater consumption, land degradation and excessive use of climatic and ecological potential of the region. These factors can lead to the development of dust centers and increase their activity.2 MethodologyThis study was conducted in Central Iran, where one of the most important environmental problems in this region is the occurrence of destructive dust storms. Arid and semiarid climate, bare soil with sparse vegetation and severe storms are the characteristics of the central region of Iran that prepare the conditions for the occurrence of dust storms. The provinces of Kerman, Yazd, Isfahan, western parts of Sistan and Baluchestan, Semnan province, Khorasan Razavi are directly affected by the consequences of these events. In order to analyze the frequency of dust days, dust codes taken from 37 synoptic meteorological stations with longer statistics were used. For each station, the number of dust days on an annual scale in the statistical period 1999 to 2018 was extracted. In order to analyze the frequency of dust days, Professional5 Easyfit statistical software was used, based on which the most appropriate statistical distribution function for each synoptic station was selected. EasyFit Professional software fits data into 61 distribution functions. Then, using the KolmogorovSmirnov test, the most appropriate probability distribution function was determined.3 Results The results show that the statistical distribution of Johnson SB was found to have the highest frequency in the study area. The LogLogistic (3P) statistical distribution ranks next. A number of stations such as Bam, Kerman, Nain, Arak, Saveh, Mahallat, Zabol, Semnan, Qom, Zahedan, Yazd and Tabas can have more than 100 days of dust in the return period of 50 or 100 years. Also, Tabas, Qom, Yazd, Zabol, Zahedan and Arak stations show at least 50 days of dust every year. The zoning of dust days with a return period of 5 years also shows that the southeastern parts of the study area, as well as areas in the center and northwest of the basin, can be identified as sources of internal dust. The zoning of dust days with a return period of 25 years shows that the southeastern parts of the study area below the Loot basin and also the northeastern parts of the study area below the central desert basin (desert plain) can have a maximum of 225 dust days in the return period. The results of frequency analysis of dust days with a return period of 50 years show that in some areas of the study area, more than 276 days of dust per year can occur and these areas are identified as dust sources in the study area.4 Discussion Conclusions In recent years, dust events in the West Asian region have occurred with greater frequency and intensity. Dust phenomenon in Sistan and Baluchestan, Kerman, Yazd, Isfahan, Semnan and Khorasan Razavi provinces is one of the most important challenges. Identifying and predicting dust source in these areas are of great importance.One of the most important forecasting methods in different return periods is the use of statistical distributions. In this study, using 61 statistical distributions and Easyfit Professional 5.5 software, the most appropriate statistical distributions were determined using the KolmogorovSmirnov test, the most appropriate probability distribution function in the study area. Johnson SB statistical distribution was found to have the highest frequency in the study area; also, LogLogistic (3P) statistical distribution is in the next rank. Then, using statistical distributions to identify indicator stations, the number of dust days in the return period was estimated from 2 to 100. The results showed that Bam, Kerman, Naein, Arak, Saveh, Mahallat, Zabol, Semnan, Qom, Zahedan, Khoor and Biabanak, Yazd and Tabas stations according to the analysis of the frequency and number of dust days estimated in the annual return period to Index station were identified in the study area. Also, changes in the spatial pattern of dust in the return periods of 5, 25 and 50 showed that the southeastern parts of the study area of Kerman, Sistan and Baluchestan provinces due to the proximity of Hamoon and Jazmourian wetlands and 120day winds of Sistan as one of the dust centers have been identified.