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

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.

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.

The results indicated that the RMS was lower between geostatistical and deterministic methods, and the MAPE in deterministic methods was lower.

The best interpolation method for the particulate matter (PM2.5 and PM10) was deterministic method by IDW function.