پیش‌بینی تعداد روزهای گرد و غباری سالیانه در استان خراسان رضوی با تحلیل‌های فضایی-زمانی

یکی از مهم‌ترین چالش‌های محیطی در سالیان اخیر در استان خراسان رضوی پدیده گرد و غبار است. بر این اساس پیش‌بینی فضایی-زمانی تعداد روزهای گرد و غباری سالیانه با استفاده از روش کریجینگ و با کمک نرم‌افزار r مورد بررسی قرار گرفت که در آن سرعت باد 15 متر در ثانیه و بیشتر و دید افقی زیر 1000 متر از نظر سازمان هواشناسی جهانی به عنوان روز گرد و غباری در نظر گرفته شد. پس از حذف همپوشانی داده‌ها از دو ماتریس فضایی زمانی دید افقی و سرعت باد، آرایه sp data به صورت ترکیبی از ماتریس و بردار در کلاس stfdf و stf ساخته شد. سپس تمامی مدل‌های تفکیک‌پذیر و غیر تفکیک‌پذیر، به مدل تجربی داده‌ها برازش داده شدند که تغییرنگار متریک با کمترین میانگین مربعات خطا به عنوان بهترین مدل برای پیش‌بینی تعداد روزهای گرد و غباری سالیانه انتخاب شد. خروجی مدل نشان داد که داده‌ها تا 5 سال دارای وابستگی فضایی زمانی هستند، لذا می‌توان تا سال 2022 تعداد روزهای گرد و غباری را برآورد نمود. حدود اطمینان تعداد روزهای گرد و غباری در سطح 95% نشان داد در سال 2019 ایستگاه سبزوار با 56 روز بیشترین و ایستگاه گناباد با 26 روز کمترین تعداد روز گرد و غباری را خواهند داشت. همچنین پیش‌بینی‌ها نشان می‌دهد تعداد روزهای گرد و غباری در استان خراسان رضوی از 39 روز تا 42 روز در سال 2022 می‌رسد که روندی افزایشی دارد.

Predicting the Annual Dusty Days in Khorasan Razavi Province Using SpatialTemporal Analysis

IntroductionOne of the most important environmental challenges in recent years in Khorasan Razavi province is the dust storm phenomenon. A survey on the average number of annual dust storm days in the Khorasan Razavi province shows that it has risen from an average of 6 days to 21 days from 1951 to 2016. And since 1993, it has grown strongly, which shows that reviewing and forecasting the coming years is of high importance. For the purpose of the study Gstat, Space time, SP, Raster, Spdep, and R Google Maps packages of R software are used alongside with Kriging method of spatialtemporal changes to investigate the number of annual dusty days and to predict their occurrence in the coming years.Materials and MethodsIn this study, the wind speed of 15 meters per second and more and a horizontal view below 1000 meters were considered by the World Meteorological Organization as a dusty day. Then, the SP Data array (Pebesma, 2013) was constructed as a combination of the matrix and vector in STFDF and STF classes (Hengel et al., 2015) according to the following equation:Where the index i refers to the location number (position of view) and the j index to the time number. After that, the empirical Spatial –Temporal variogram were calculated using the Kriging method (Mohammadzadeh, 2012) and were calculated from the following equation:where in: & nbsp; & nbsp; & nbsp;Represents the set of all couples of observations whose distance is in the neighborhood of the vector & nbsp;and its time interval near & nbsp; & nbsp; . Then all the separable and nonseparable models were fitted to experimental data model, where the metric variogram with the least average square error was selected as the best model for predicting the number of dust storm days in the year.Results and DiscussionThe output of the model showed that data up to 5 years have a spatialtemporal dependence, and it is possible to estimate the number of dust storm days until 2022. Therefore, the most important spatial points are the annual number of dust storm days from higher values. They include: Northeast, Northwest and South East of Razavi Khorasan province. The predicted values of annual dust storm days in the central areas of Khorasan Razavi province in 2018 and 2019 shows that these areas will have the smallest number of dust storm days, but will gradually face with an increase in the number of dust storm days.Accordingly, in 2022 most points except the limited points in the east, west and south east will have a significant increase in the number of dust storm days in the central areas. & nbsp; The results show in 2019, Ghochan, Mashhad, Sarakhs, Sabzevar, Khaf, Torbat Jam and Fariman stations, with a maximum reliability of 95%, will have 40.48.41.46,56,47.41,41 dusty days, respectively. The highest number of dust storm days is related to Sabzevar station with 56 days, and after that Sarakhs with 47 days. Spatialtemporal forecasting trend of the number of dusty days in 2022 indicates that Gonabad Station has the best air quality index of 7 days. Furthermore, the maximum probability of occurrence of dusty days in Ghouchan, Mashhad, Sarakhs, Sabzevar, Khaf, Torbat Jam, and Fariman stations reaches up to 51, 46, 49, 61, 51, 44, and 45 days respectively, and will increase from 39 days to 43 days throughout the province in 2022.ConclusionAccording to the increasing number of dusty days in Khorasan Razavi province, investigating the prediction models is very important. Spatialtemporal Kriging method can use the intrinsic stability of the data to predict the number of dusty days in three dimensions of x, y, and t. This means that we can predict the number of dusty days in different places and times. Indeed, the intrinsic structure of the data plays a significant role in this regard. If the data algorithm is annual, monthly or daily, the output of the model will be proportional to the algorithm. Annual algorithm was able to deliver acceptable results in this research.