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 (Associate Professor, Department of Environmental Health Engineering, Faculty of Health, Gonabad University of Medical Sciences, Gonabad, Iran.)
Mehri Delsouz ² (Department of Environmental Health Engineering, Faculty of Health, Gonabad University of Medical Sciences, Gonabad, Iran)
Ghasem Zolfaghari 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))
Mohsen Mir Mohammadi ⁴ (Assesistant Professor, Department of Environmental Engineering, Faculty of Environment, Tehran University, Tehran, Iran.)
Hamed Adab 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.

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