Evaluatin of Deterministic and Geostatistics Methods for Particulate Matter Concentration (PM2.5 and PM10) Zoning Using GIS: case study, Sabzevar City

Subject Areas : Environmental pollutions (water, soil and air)

Seyed Ali Sajjadi ¹ , Mehri Delsouz ² , Ghasem Zolfaghari ³ , Mohsen Mir Mohammadi ⁴ , Hamed Adab ⁵

1 - Associate Professor, Department of Environmental Health Engineering, Faculty of Health, Gonabad University of Medical Sciences, Gonabad, Iran.
2 - Department of Environmental Health Engineering, Faculty of Health, Gonabad University of Medical Sciences, Gonabad, Iran
3 - Associate Professor, Department of Environmental Technology, Faculty of Environmental Sciences, Hakim Sabzevari University, Khorasan -e- Razavi, Sabzevar, P.O. Box: 397, Iran. *(Corresponding Author)
4 - Assesistant Professor, Department of Environmental Engineering, Faculty of Environment, Tehran University, Tehran, Iran.
5 - Assesistant Professor, Department of Climatology and Geomorphology, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran.

Received: 2016-11-18 Accepted : 2017-04-30 Published : 2019-12-22

Keywords: Geostatistic, Interpolation, GIS, Particulate matter,

Abstract :

Background and Objectives: Nowadays, people’s life is at risk because of various pollutants into the atmosphere by human action and biological activities. One of the major air pollutants are particulate matter. The aim of this study was to evaluate spatial interpolation methods to determine the concentration PM2.5 and PM10 in Sabzevar city and select the most suitable interpolation method for preparation of zoning maps particulate matter in GIS. Materials and Methods: Particulate matter were measured by a monitoring device, environmental dust model Haz-Dust EPAM 5000 at 48 stations in the city, then in ARC GIS software three well-known spatial interpolation techniques, namely Kriging, Inverse Distance Weighting (IDW) were applied for generating the prediction maps. Finally the best interpolation method was chosen according to the values of each algorithm error. Findings: The results indicated that the RMS was lower between geostatistical and deterministic methods, and the MAPE in deterministic methods was lower. Discussion and Conclusion: The best interpolation method for the particulate matter (PM2.5 and PM10) was deterministic method by IDW function.

References:

1. Burnham, P., Anderson, R., 2004. Multimodel inference understanding AIC and BIC in model selection. Sociological Methods and Research, Vol. 33,  pp. 261 -304.


2. Qorbani Salkhord, R., Mobasheri, M.R., Rahimzadehgan, M., 2012. A fast method for assessment of PM₁₀ concentration using MODIS images, a case study in Tehran. Hakim Research Journal, Vol. 15(2), pp. 166-177. (In Persian)


3. Data and Research Unit. Annual report of air quality of Tehran in 2012. Air Quality Control Company, technical report.


4. Keynejad, M., Ebrahimi, S., 1998. Environmental Engineering. Sahand University of Technology. (In Persian)


5. Rogge, W., Determination of key organic compounds in the particulate matter amission from air pollution sources. California Institute of Technology, Research Note 1994. Available From:URL:http://www.ard.ca.gov. Accessed Dec 25, 2008.


6. Naddafi, K., 2009. Air pollution with emphasis on dusts and their health and environmental impacts. 12^th National Confrance on Environmental Health, Tehran, Iran. (In Persian)


7. Bahari, R.A., Abaspour, R.A., Pahlavani, P., 2016. Zoning of particulate matters (PM) pollution using local statistical models in GIS (case study: Tehran metropolisies). Journal of Geomatics Science and Technology, Vol. 5(3), pp. 165-174. (In Persian)


8. Ghahroudi, M., 2005. Geographic Information System (GIS). Tarbiyat moalem Jahadedaneshgahi Publication. (In Persian)


9. Shad, R., Ashoori, H., Afshari, N., 2008. Evaluation of optimum methods for predicting pollution concentration in GIS environment. International archives of the photogrammetry, Remote Sensing and Spatial Information Sciences, Vol 17, pp. 315-320.


10. Webster, R., Oliver, M.A., 2000. Geostatistics for Environmental Scientists. Wiley Press, 271 p.


11. Timonen, K., Tiittanen, P., Penttinen, P., 1997. Effects of fine and ultrafine particles on respiratory and cardiovascular health. Finish Research Programme on Environmental Health. (available at http://www.ktl.fi/sytty/ abstracts/ pekka1 htm).


12. Fathtabar Firouz Jani, S., Alesheikh A., Rangzan K., Chinipardaz R., 2011. Air pollution zoning using geostatistics methods and GIS: case study, Tehran. 5^th National Conference on Environmental Engineering, Hamedan, Iran. (In Persian)


13. Kumar, D., Sabesan, M., Das, A., 2011.Evaluation of interpolation technique for air quality parameters in Port Blair, India. Universal Journal of Environmental Research and Technology, Vol. 3, pp. 301 -10.


14. Asakereh, H., 2008. Kriging application in climatic element interpolation, a case study: Iran precipitation in 1996.12.16. Geqgraphy and Development Journal. Vol. 6, pp. 25-42. (In Persian)


15. Nadiri, A., Shakour, S., Asgharimoghadam, A., Vadiati, M., 2017. Investigation of groundwater nitrate pollution with different interpolation methods (case study: East Azarbayjan, Bilverdy plain). Journal of Hydrogeomorphology, Vol. 1, pp. 75-92. (In Persian)


16. Hasanipak, A., 1998. Earth Statitis (Geostatitial): Tehran University. (In Persian)


17. Ayobi, S., Hosseinalizadeh, M., 2007. Assessment spatil variability of soil erodibility by using of geostatiti and GIS (case study, Mehr watershed of Sabzevar). Iranian Journal of Natural Resources, Vol. 60(2), pp. 369 – 82. (In Persian)


18. 18-. Nasrollahi, Z., Ghaffari, M., 2010. Air pollution and its determinants (The case study of SPM, and SO2 emissions in Iran manufacturing industries). Journal of the Economic Research, Vol. 10(3), pp. 75-95.


19. Hadipoor, M., Poorebrahim, S.H., 2011. Locating residential land use in urban transportation planning by the application of GIS and mathematical emission modeling of air pollution. Journal of Environmental Studies, Vol 37, 135-149.


20. Mansouri, N., Esmaealzadeh, J., 2011. Investigating suspended particles resulted from Tehran`s highways traffic. Journal of Traffic Engineering, Vol. 44. (In Persian)


21. Mirmousavi, S.H., Mirain, M., 2012. The application of geostatistics methods in temporal precipitation distribution (Case study: Kerman Province). Journal of Geography and Planning, Vol 16, pp. 153-178.


22. Momeni Damaneh, J., Joulaei, F., Alidadi, H., Peiravi, R., 2015. Evaluatin of interpolatin methods to determine spatil variatins of groundwater Quali-tatie parameters (Case study: Gonabad plain). Iranian Journal of Research in Environmental Health, Vol. 1(3), pp. 165-176. (In Persian)


23. Sadeqi Aqdam, F., Asqari Moqadam, A., Nadiri, A., 2015. An evaluation of temporal and spatial variation of arsenic anomalies in water resources of sahand dam using statistical methods. International Bulletin of Water Resources and Development, Vol. 2, pp. 11-23. (In Persian)


24. Tombette, M., Mallet, V., Sportisse, B., 2009. PM 10 data assimilation over Europe with the optimal interpolation method. Atmos. Chem. Phys, Vol. 9, pp. 57–70.


25. Norazian, M.N., Shukri, Y.A., Azam, N.R., 2008.Estimation of missing values in air pollution data using single imputation techniques. ScienceAsia, Vol.34, pp. 341-45.