پیش بینی میزان دید افقی سالیانه حاصله از گرد و غبار در منطقه کم فشار حرارتی سیستان

نوع مقاله : مقاله پژوهشی

نویسنده

استادیار گروه آب و هواشناسی دانشگاه پیام نور ایران

چکیده

دید افقی یک نشانگر ساده از کیفیت هوا به شمار می رود که مقدار آن با جذب و خاموشی نور در اثربرخورد با مولکولهای گاز و ذرات تعیین میشود. امروزه، با افزایش فعالیت های انسانی در سالهای اخیر و افزایش غلظت ذرات معلق موجود در جو دید افقی کاهش یافته است، تحقیقات نشان می دهدکه در زمان اوج توفان گرد و خاک در منطقه کم فشار حرارتی سیستان سرعت باد گهگاه به بیش از 70 کیلومتر بر ساعت می رسد که با افزایش ذرات آلاینده ها جوی ، دید افقی به کمتر از 100 متر کاهش می یابد وضعیت منطقه مورد مطالعه نشان می دهد که از سال 1986 تا سال 2018، میزان دید افقی کاهش یافته است که از رقم 8/14 کیلومتر به رقم 5/9 کیلومتر رسیده است. بدین منظور، بررسی و پیش بینی میانگین سالانه دید افقی با توجه به داده های قابل دسترس تا سال 2022 با استفاده از روش آماری رگرسیون فضایی-زمانی و با کمک نرم افزار R و بسته های نرم افزاری spdep ‘plotKML’، RgoogleMaps , tseries’ وmaptools در این تحقیق در نظر گرفته شد تحلیل فضایی – زمانی میانگین سالیانه دید افقی نشان می دهد که ناحیه سیستان تا سال 2022 کمترین میزان دید افقی را خواهد داشت که میزان آن به 6 تا 7 کیلومتر می رسد و پس از آن ناحیه زاهدان و قاینات قرار می گیرند ناحیه بیرجند با ایستگاه های بیرجند وسربیشه و نهبندان با دید افقی 10 تا 14 کیلومتر بیشترین میزان دید افقی را دارند و از این نظر می توان گفت شرایط بهتری از نظرمیزان هوای پاک و سالم دارد.

کلیدواژه‌ها


عنوان مقاله [English]

Prediction the annual horizontal visibility in the low thermal pressure region of Sistan

نویسنده [English]

  • ahmad Hosseini
Assistant Professor, Department of Meteorology, Payame Noor University, Iran
چکیده [English]

Introduction

Horizontal vision is a simple observational indicator of air quality. In clean atmosphere, the field of view is between 145 and 225 km and in normal atmosphere between 10 to 100 km and in polluted areas it is less than this amount. Preliminary research shows that the accumulation of air vents in the atmospheric column severely reduces horizontal visibility Today, with the increase of human activities in recent years and the increase in the concentration of suspended particles in the atmosphere, including: air conditioners affecting the depth of light, has caused a decrease in horizontal vision. In Sistan and Baluchestan province, dust storms are in critical condition, so that on January 5, 2015, wind speeds reached 102 kilometers per hour in Zahedan and Nusratabad and dust concentration (〖PM〗_2.5)increased to 115 μg / m3. Research shows that during the peak of dust storms, the concentration of pollutant particles in Zabol station increases and the wind speed reaches 70 km / h and the horizontal visibility is drastically reduced to even less than 100 meters. Therefore, considering the importance of the visual quantity in the low thermal pressure region of Sistan, the study and forecast of its annual average until 2022 with the help of spatio-temporal regression was considered in this study.

Materials and methods

In this study, in order to predict the annual horizontal visibility, the statistical method of spatio-temporal regression with the help of R software and using the package spdep, plotKML, RgoogleMaps, tseries and maptools has been used. For this purpose, the data autocorrelation, data Stationary were first examined to determine the type of regression, error normalization test, error non-correlation test and error variance homogeneity test. Then, the test of significance of the regression line equation and the variance inflation index and the coefficient of determination of the data were calculated. Then, in order to predict the average annual horizontal visibility in the low thermal pressure region of Sistan, the spatio-temporal regression model was defined as follows:



z_(s,t)=β_0+β_1 z_(s_1,t-1)+β_2 z_(s,t-1)+β_3 x_s+β_4 y_s+ε_(s,t)



Where z_(s,t) the horizontal is view at time t in position s, s_1Position of the station closest to the station with position s, y_s and x_s Latitude and Longitude and ε_(s,t) is a set of errors. Then the upper and lower limits of the average annual horizontal visibility with 95% confidence interval were calculated according to the following relationship:

A≡(z ̂_(s_0,t_0 )-1.96σ_(s_0,t_0 ),z ̂_(s_0,t_0 )+1.96σ_(s_0,t_0 ) )



Where σ_(s_0,t_0 )is the standard deviation of z ̂_(s_0,t_0 )

Results and discussion

The results of spatio-temporal regression coefficients of the data show that the P-value for all variables is less than 0.05 and is significant in the model. And the generalized spatio-temporal regression model can predict the horizontal visibility variable in the coming years in the low thermal pressure region of Sistan.Spatial-temporal analysis of the average annual horizontal visibility shows that Sistan region has the lowest horizontal visibility until 2022, which is 6 to 7 km, followed by Zahedan region between 8 to 9 km. Predicted horizontal visibility In Ghaenat area, it is between 9 and 10 km, which is in the third rank among the stations in the region. The average horizontal visibility in Birjand area is between 10 and 12 km. Nehbandan station has better conditions than Birjand station in the coming years. However, the highest annual horizontal visibility is related to Sarbisheh station, which is 12 to 14 km, which in this regard can be said to have better conditions in terms of clean and healthy air. The plot points in 2022 show that Sistan and its southern parts, due to its proximity to the Loot Desert and the low altitude of the region compared to the surrounding areas, increase the intensity of its 120-day winds, which is one of the main reasons for the region's critical Horizontal view is towards its northern regions.

Conclusion

The general situation of Sistan low pressure region during the statistical period shows that its horizontal visibility decreases so that in 1986 with an average of 14.8 km started in 2018 it reaches 9.5 km. numerical model shows that the lowest average annual horizontal visibility during 2019 to 2022 with the rates of 6.9, 0.7, 7.1 and 7.2 km is related to Zahak station and its forecast shows that by 2022, it will increase by almost 300 meters, after that, Zabol station is located at 0.7, 7.1, 7.3 and 7.4 km, respectively, which the forecast of this station indicates that the average annual horizontal visibility will increase during the coming years. Its incremental figure is approximately 400 meters and 100 meters more than Zahak station. Nusrat Abad station is also facing an increase in annual horizontal visibility which reaches from 9.9 to 0.9 km, in Zahedan station, the average horizontal visibility has a decreasing trend, so that its value in 2019 is equal to 8.7 km, which in 2022 will reach 8.6 km. Which adds to the criticality of this station. Therefore, considering the high risk of all stations in the study area, it should be said that stations located in Sistan and Baluchestan province will have more severe conditions. The results show that the annual horizontal visibility increases in Birjand and Ghaen stations, which shows in 2019 with 10.0 and 9.2 km, respectively. And in 2022 it will reach 10.5 and 9.8 km. Finally, the results show that the average annual horizontal visibility in the coming years in the low-pressure region of Sistan will decrease, which can challenge the economic and demographic changes in the region.

کلیدواژه‌ها [English]

  • Sistan thermal low pressure
  • spatio-temporal regression
  • horizontal visibility
  • dust
  • Predict
  1. Asghari Mohammad Amir Hossein Meshkouti; Abbas Ranjbar; Mohammad Moradi. Study and evaluation of dust emission schemes in the WRF-Chem model of storm in the east and southeast of the country (Case study 11 to 13 August 2018). Climatological Research, 1399 (43), 87-98.
  2. Omidvar, Kamal, Nessa Sepandar (2018). Synoptic analysis and satellite monitoring of dust phenomenon in Kermanshah province in the period 1987 to 2010) Case study: Dust pervasive June 17 and 18, 2009. Journal of Applied Research in Geographical Sciences, 18 (49), 1- 18.
  3. Steadfast, Seyedeh Samaneh. Khosh Sima, Massoud. Ahmadi Givi, Culture. (2016). Investigation of changes in atmospheric extinction coefficient based on horizontal visibility in four busy airports in the country. Earth and Space Physics, 42 (2), 459-467
  4. Jabali Atefeh, Reza Jafari, Mohammad Zare, Mohammad Reza Ekhtesasi. 1399. Investigation of the range of horizontal visibility variability of areas affected by dust events in Yazd province. Desert Management, 8 (15), 21-36.
  5. Hatami Mohannad, Jalaluddin, Sabetgadam, Samaneh Ahmadi Givi, Farhang. (2019). Investigation of meteorological conditions of minimum daily horizontal visibility using the information of RVR device of Imam Khomeini Airport. Spatial Analysis of Environmental Hazards, 6 (1), 17-30.
  6. Hussein Hamzeh, Nasim. Fattahi, Ibrahim Ranjbar, Abbas. Zoljoudi, Mojtaba .Ghafarian, Parvin. Simulation of dust phenomenon in the west and southwest of the country in a case study with WRF / CHEM and EURAD models. Climatological Research, 1396 (31), 19-36.
  7. Hamidianpour, Mohsen 2013. The study of the formation of 120-day wind in Sistan with dynamic micro-rotation of downstream currents in the east of the Iranian plateau, PhD thesis, Kharazmi University, Faculty of Geography, Department of Climatology, Tehran.
  8. Meteorological Organization of Iran, daily horizontal visibility statistics from 1/1/1986 to 12/31/2018, available: https://www.irimo.ir/far/wd/2703.
  9. Mohammadzadeh, Mohsen 1394. Spatial statistics and its applications. Second Edition, Tarbiat Modares University Press, pp. 240,16020,13,
  10. Anselin, L. I, Syabri & Kho, Y. 2010. GeoDa: an introduction to spatial data analysis. In Handbook of applied spatial analysis .Springer, Berlin, Heidelberg.
  11. Annex II.WMO. Manual on Codes International Codes. Volume I.3. Technical Regulations Part D – Representations derived from data models WMO-No. 306. Pp : 80, 81 2015 edition. https://library.wmo.int/?lvl=notice_display&id=19508
  12. Anselin, L. 2003. An introduction to spatial regression analysis in R. University of Illinois, Urbana-Champaign، R Development Core Team. http://sal.agecon.uiuc.edu.
  13. Baumer, D., Vogel, B., Versick, S., Rinke, R., M¨ohler, O. and Schnaiter, M., 2008, Relationship of visibility, aerosol optical thickness and aerosol size distribution in an ageing air mass over South-West Germany, Atmos. Environ., 42, 989-998.
  14. Bivand, R. M, Altman & L, Anselin. 2021. Spatial Dependense Weighting Schemes statistics and Model. Package ʽspdepʼ. R Core Development Team. Version 0.7-4. URL: http://
  15. com/r-spatial/spdep/.
  16. Cressie, N. 1993. Statistics for Spatial Data, Revised Edition. Johan Wiley. New York.pp:122.
  17. Cryer, J. K, SD,Chan. 2008. Time series analysis: with applications in R. Springer Science & Business Media.
  18. Griffin Dw, Atmospheric Movement of Microorganisims in Clouds of Desert Dust and Implications for Human Health, Clinical Microbiology Reviews 2007; 20(3): 459-577
  19. Hengl ,T.; P, Roudier. D, Beaudette & E. Pebesma. 2015. Plot KML: Scientific Visualization of Spatio-Temporal Data. Journal of Statistical Software. 63, 5. http://www.jstatsoft.org/ Institute for Geoinformatics University of Munster, Germany.
  20. Hejazi, A., Mobasheri, M.R., & Majidi, D., (2014). Using satellite images to calculate atmospheric visibility. Climate Research, 5(17), 47-56.
  21. Hengl, T., Roudier, P., Beaudette, D., Pebesma, E., & Blaschek, M. (2021). Visualization of Spatial and Spatio-Temporal Objects in GoogleEarth, Package ‘plotKML’. R Core Development Team. Version0.8-1.URLhttps://github.com/Envirometrix/plotKML.
  22. Hu, B., Zhang, X., Sun, R., & Zhu, X. (2019). Retrieval of Horizontal Visibility Using MODIS Data: A Deep Learning Approach. Atmosphere, 10(12), 740.
  23. Jarque, Carlos M.; Bera, Anil K.1987. "A test for normality of observations and regression residuals". International Statistical Review. 55, 2: 163–172.
  24. Loecher, M. 2016. Overlays on Static Maps. Package R Google Maps. R Development Core Team. Version 1.0.4.1.
  25. Maddala, G. S.; Lahiri, K.Introduction to Econometrics (Fourth ed.). Chichester: Wiley.pp:216.
  26. Malm, W. C. (1999). Introduction to visibility (Vol. 40). Fort Collins, CO: Cooperative Institute for Research in the Atmosphere, NPS Visibility Program, Colorado State University. Section 6: Particle Concentration and Visibility Trends. pp: 33.
  27. Mie, D., Xiushan, L., Lin, S., Ping, W. (2008). A dust-storm process dynamic monitoring with multi temporal MODIS data. The International Archives of photogrammetry, Remote Sensing and Spatial Information Sciences, 37(3): 965–969.
  28. Montgomery, D. C. E, A, Peck & G. G. Vining 2012. Introduction to linear regression analysis. John Wiley & Sons. Vol. 821, pp: 23, 25,27,34,35,118.
  29. Pebesma, E. (2012). spacetime: Spatio-temporal data in R. Journal of Statistical Software, 51(7), 1-30.
  30. Pebesma, E.; B, Gräler . 2017. Spatial and Spatio-Temporal Geostatistical Modelling, Prediction, and Simulation. R Development Core Team. Version 1.1-5. URL https://github.com/edzer/gstat/
  31. R Development Core Team. 2018. A language and environment for statistical computing computer program. Version 3.5. 0.
  32. Schaap, M., Timmermans, R. M. A, Koelemeijer, R. B. A, de Leeuw, G. and Builtjes, P. J. H., 2008, Evaluation of MODIS aerosol optical thickness over Europe using sun photometer observations, Atmos. Environ., 42, 2187- 2197.
  33. Shepherd, G., Terradellas, E., Baklanov, A., Kang, U., Sprigg, W., Nickovic, S & Joowan, C. (2016). Global assessment of sand and dust storms.
  34. Studenmund, A. H. 2006. Using Econometrics: A Practical Guide (5th ed.). Pearson International.pp:258.
  35. Verbeek, M.A Guide to Modern Econometrics (4th ed.). Chi Chester: John Wiley & Sons.pp:117.
  36. Wang, J. and Christopher, S. A., 2003, Inter comparison between satellite derived aerosol optical thickness and PM2.5 mass: implications for air quality studies, Geophys. Res. Lett., 30, 2095-2116
  37. Wikle, C. K., Zammit-Mangion, A., & Cressie, N. 2019. Spatio-temporal Statistics with R. Chapman and Hall/CRC.pp:24, 100,101.
  38. Wu, J., Fu, C., Zhang, L. and Tang, J., 2012, Trends of visibility on sunny days in China in the recent 50 years, Atmos. Environ., 55.
  39. Xuan J, Sokolik In, Hao J, Guo F, Mao H, Yang G. Identification and characterization of sources of atmospheric mineral dust in East Asia, Atmospheric Enviroment 2004; 38(36): 6239-6252.