Estimation, statistical packages, R software, dust, Sum-metric model, East of Iran

Document Type : Full length article


1 Assistant Professor, Department of Meteorology, Payame Noor University, Iran

2 Master of Payame Noor University, Iran


Extended Abstract
In studies by the World Meteorological Organization, winds with speeds of more than 15 meters per second (30 knots) and horizontal visibility below 1000 meters are known as dust storms. This is based on the Beaufort scale in the hurricane group and such storms can move particles with a length of more than 500 microns. In this case, the sandstorm settles for short distances, but the dust travels long distances in the form of suspended or fine dust.When the wind speed reaches 7 meters per second or less, it only has the power to move particles less than 20 microns long, and the rest of the storm is scattered in the air in the form of suspended particles or dust. Evidence shows that dust is increasing in the eastern regions of Iran. Consequently, on April 25, 2015, the amount of pollution in Mashhad reached 55 micrograms per cubic meter in 24 hours and, was alert. In the first six months of 2016, the cities of Tabas, Nehbandan, and Birjand had the highest number of dusty days with, 45, 29,and 27 days, respectively. The wind speed in Yazd had similar conditions, which reached 96 km / h on July 22, 2015, while it was 70 km / h in Kerman on February 20, 2015. In Zabol, the cost of respiratory diseases caused by dust from 1999 to 2004 is estimated at more than 70 million dollars. Considering the frequency of dust phenomenon in the eastern regions of Iran and the importance of spatial-temporal analysis and its prediction was suggested.
Materials and methods
The study area is located between 52 to 64 degrees longitude and 24 to 38 degrees latitude in the east of the country, which includes the provinces of Khorasan Razavi, South Khorasan, Sistan-Baluchestan, Kerman, and Yazd. Therefore, to estimate the number of dusty days, the statistics of 57 meteorological stations and the elements of wind speed of 15 meters per second and more, horizontal visibility of less than of 1000 meters from the statistical period of 1/1/1987 to 31/3/2017 were used.It was then considered for the forming of the m × n matrix and statistical matching for years without NA statistics. In the next phase, utilizing several software packages, including gstat, spacetime, SP, raster, spdep, RgoogleMaps, tseries, maptools, plm, randtest, and R the necessary programming was done next, all marginal variogram models, including Gaussian, spherical, linear, Bessel, exponential and Mattern were fitted separately with the experimental data model
Results and Discussion
The percentage of the number of monthly dust days indicates that the range of changes rises from 1.04% in October to 3.42% in March. This means that on average, in the study area, about 4% of the days in March were dusty.This month was chosen as the critical month for estimation.
The outputs revealed that the Sum-metric model, with the lowest mean squared error, has the best fit for estimating data. However, the experimental time variogram with an interval of 30 months shows that the gamma output values in the middle logs are closer, and the data are more interdependent, but their range is extended. In both variograms, the Partial Sill being more significant than the Nugget Effect has good conditions for model fit. Nevertheless, its spatial-temporal variogram shows that the estimated time will not be extended, and the data will move towards the average faster.
However, the four months were considered: March 2018, March 2019, March 2021, and March 2022.The results showed that no fundamental changes in the spatio-temporal distribution of data are seen from March 2018 onwards. Therefore, the variogram can only estimate March 2018.
Accordingly, the most critical estimated points for the number of dust days that have higher values in March 2018 are in 3 provinces, including: a) Khorasan Razavi, (Mashhad, Golmakan, and Fariman stations with three days, and cities of Quchan, Khaf, and Bardaskan with two days), b) Yazd province, (Abarkooh with four days ,and cities of Lalehzar, Eghlid, Bafgh, Meybod, Bahabad, and Harat with two days), c) Kerman province(Kerman station with three days, and Anar with two days), and d) Sistan and Baluchestan ( Zahedan with four days, Zabol, Mirjaveh, and Konarak with three days, and cities of Khash and Nusratabad with two days). Estimated values for March 2018 in South Khorasan Province show that these areas have the fewest dusty days.
Data analysis a 95% confidence level showed that Zahedan stations with eight days and Golmakan, Zabol, Mirjaveh, Konarak, and Abarkooh stations with seven days had the highest number of dusty days in March 2018 in the eastern regions of Iran.
The results reveal that since 1987, the number of dusty days in March starts at 47 and reaches 162 in March 2017. This means that with an average number of dust 58 dusty days, there is a positive deviation of 104 days, which is a very high figure and could indicate the severity of the air pollution crisis in the country's east in the coming years. The intrinsic structure of the data shows that the Sum-metric model can be estimatedonly in March 2018.Out of a total of 57 stations in the study area, 8 stations are in good condition, 28 stations are in normal condition, and 21 stations are in critical condition. Estimations of the probability of occurrence at the level of 95% of the number of dust days show that the lowest number of dust days in the east of the country in March 2018 is related to Kashmar, Birjand, and Boshrayieh stations with four days, while the highest number of days is related to Zahedan station with eight days, and Golmakan, Zabol, Mirjaveh, and Konarak, and Abarkooh stations with seven days the maximum probability of occurrence.


Main Subjects

  1. Annex II to the WMO Technical Regulations, Manual on Codes International Codes Volume1.1, Part A – Alphanumeric Codes, Updated in 2015.pp: Xviii
  2. Bivand, R. (2021). Maptools:Tools for reading and handling spatial objects. R package version 0.8-10, URL http://CRAN. R-project. Org/package= maptools.‌
  3. Bivand, R., M, Altman & L, Anselin. (2022). Spatial Dependense Weighting Schemes statistics and Model. Package ʽspdepʼ. R Core Development Team. Version ʼ.1.2-4. URL:
  4. Caeiro, F. & Mateus, A. (2015). Testing Randomness in R. Package ʽrand testsʼ. R Development Core TeamVersion 1.0.
  5. Cressie, N. ,1993, Statistics for spatial data,John Wily&Sons,New York.
  6. Croissant, Y. Millo. G., Tappe, K. A., 2021, Linear Models for Panel Data.plm package. R Core Development Team. Version 1.6-6 = plmFrontera, A., Cianfanelli. DOI:1007/s12517-020-06291-w
  7. Frontera, A., Cianfanelli, L., Vlachos, K., Landoni, G., & Cremona, G. ,2020, Severe air pollution links tohigher mortality in COVID-19 patients: The “double-hit” hypothesis. Journal of Infection, 81(2), 255-259.
  8. Gräler, B., Rehr, M., Gerharz, L. pebesma, E. ,2013, Spatio- Temporal Analysis and interpolation of PM10 measurements in Europe for 2009. Institute for Geo Information (IfGI), University of Münster, Germany. pp.33.
  9. Griffin, Dw. ,2007, Atmospheric Movement of Microorganisms in Clouds of Desert Dust and Implications for Human Health. Clinical Microbiology Reviews, 20(3), 459-577.
  10. Hamidianpour, M., Jahanshahi, S. M. A., Kaskaoutis, D. G., Rashki, A., & Nastos, P. G. ,2021, Climatology of the Sistan Levar wind: Atmospheric dynamics driving its onset, duration and withdrawal. Atmospheric Research, 260, 105-711.
  11. Hamidianpour, M., Mofidi, Sesah, m., & Alijani. B., (2017). The role of topography on the simulation of the Sistan wind structure in the east of the Iranian plateau. Applied Research in Geographical Sciences, 16, 1-13. [In Persian].
  12. Hoefer, A., Pampaka, D., Wagner, E. R., Herrera, A., Alonso, E. G. R., López-Perea, N., & Gallo, D. N. ,2020,. Management of a COVID-19 outbreak in a hotel in Tenerife, Spain. International Journal of Infectious Diseases, 96, 384-386.
  13. Hosni Pak, A. & Sharafuddin, M. (2011). Exploratory data analysis, firs edition. Tehran: Tehran University Press. [In Persian].
  14. Hyun C., Dong W S., Wonnyon K., Seon J D., Soo H L.,Minsoo N., ,2011, Asian dust storm particles induce a broad toxicological transcriptional program in human epidermal keratinocytes, Toxicology Letters, 200(1-2), 92-99.
  15. Issak, E. H., & Srivastar, R.M. ,1989, An Introduction to Applied Geostatistics. Oxford Univ. Press, Oxford.
  16. Iwashita, F., Monteiro, R.C., Landim, P.M. ,2005, An alternative method for calculating variogram surfaces using polar coordinates. Computers & Geosciences, 31(6), 801-803.
  17. Labban, A. H., & Butt, M. J. (2021). Analysis of sand and dust storm events over Saudi Arabia in relation with meteorological parameters and ENSO. Arabian Journal of Geosciences, 14(1), 1-12.
  18. Loecher, M. ,2020, Package ‘RgoogleMaps’. Overlays on Static Maps, URL
  19. Mardia, V., Goodall, C. R. ,1993, Spatial- Temporal Analysis of Multivariate Enviromental Monitoring Data. In Multivariate Enviromental Statistics, North- Holland, Amsterdam, 347-386.
  20. Miri A, Ahmadi H, Ghanbari A, Moghaddamnia A, 2007, Dust Storms Impact on Air Pollution and Public Health under Hot and Dry Climate, International Journal of Energy and Environment, 1(2), 101-105.
  21. Mohammadzadeh, M. (2014). Spatial statistics and its applications, second edition. Tehran: Tarbiat Modares University Press. [In Persian].
  22. Montero, J. M., Fernández-Avilés, G., Mateu, J. ,2015, Spatial and spatio-temporal geostatistical modeling and kriging, John Wiley & Sons.
  23. National Meteorological Organization statistics, statistical data, available:[In Persian].
  24. Pebesma, E. & Gräler, B. ,2017, Introduction to Spatio-Temporal Variography. Ifgi Institute for Geoinformatics University of Münster, 1-11
  25. Pebesma, E. ,2012, spacetime: Spatio-temporal data in R. Journal of Statistical Software, 51(7), 1-30.
  26. Pebesma, E. ,2021, Classes and Methods for Spatio-Temporal Data. sp’Package. R Core DevelopmentTeamVersion 1.2-5. URL
  27. Pebesma, E. 2021,Spatio-temporal overlay and aggregation. Ifgi. Institute for Geoinformatics University of Münster, 1-12
  28. Pebesma, E. J. 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30(7), 683-691.
  29. Pebesma, E., & Heuvelink, G. 2016,Spatio-temporal interpolation using gstat. RFID Journal, 8, (1), 204-218.
  30. Pebesma, E.,2021, Spatial and Spatio-Temporal Geostatistical Modelling, Prediction and Simulation. ‘gstat’Package. R Development Core Team, Version1.1-5. URL
  31. Pebesma,E., Gräler, B., Gottfried,T., Hijmans, R. 2021,Classes and Methods for Spatio-Temporal Data. spacetime’Package. R Development Core Team, Version1.2-1. URL
  32. R Development Core Team. 2011, R, A language and environment for statistical computing.
  33. Rashki, A., Kaskaoutis, D. G., Rautenbach, C. D., Eriksson, P. G., Qiang, M., & Gupta, P. ,2012, Dust storms and their horizontal dust loading in the Sistan region, Iran. Aeolian Research,  5, 51-62.
  34. Rayegani, B., Barati, S., Goshtasb, H., Gachpaz, S., Ramezani, J., & Sarkheil, H. (2020). Sand and dust storm sources identification: A remote sensing approach. Ecological Indicators, 112, 106099.
  35. Robert, J., L. ,2021, Geographic Data Analysis and Modeling.‘Raster’Package.R Core Development Team. Version 2.5-8.URL
  36. Shi-gong, W., De-bao, Y., Jiong, J., 1995, Study on the Formative Causes and Countermeasures of the Castarophic Sandstorm Occurred in Northwest China, Journal of Desert Research, 15(1), 19-30.
  37. Statistics of the Meteorological Organization, statistical data, available:
  38. Trapletti, A., & Hornik, K., ,2020, Time series analysis and computational finance. Package ‘tseries’. R Core Development Team. Version 0.10-45 URL https://CRAN.R-
  39. United Nations Enviroment Program, 2005, Environmental News Emergencies, URL: http//: www unep org/ depi/ programs/ emergencies html.
Volume 54, Issue 2
September 2022
Pages 273-291
  • Receive Date: 26 March 2022
  • Revise Date: 30 May 2022
  • Accept Date: 28 August 2022
  • First Publish Date: 28 August 2022