Application of GIS for the modeling of spatial distribution of air pollutants in Tehran

Spatial modeling of air pollutants in the mega cities such as Tehran is a useful method for the estimation of pollutants in the non-observed positions in Tehran. In addition, spatial modeling can determine the level of pollutants in different regions of Tehran. There are some typical interpolation techniques (e. g., Inverse Distance Weighting (IDW), Thin Plate Splines (TPS), Kriging and Cokriging) for spatial modeling of air pollutants. In this study, different interpolation methods are compared for spatial modeling of carbon monoxide in Tehran. The three-hourly data of wind speed and direction was received from 5 meteorological stations in Tehran. The hourly data of carbon monoxide in 2008 have been extracted of 16 air pollution monitoring stations in Tehran. The hourly data of 3 selected days in 2008 (72 hours) and similarly, the daily data of 36 days in 2008 (3 days in each month) were utilized for spatial modeling in this study. Different typical interpolation techniques were implemented on different hourly and daily data using ArcGIS. The percent of absolute error of each interpolation techniques for each hourly and daily interpolated data was calculated using cross validation techniques. Results demonstrated that Cokriging has better performance than other typical interpolation techniques in the hourly and daily modeling of carbon monoxide. Because it utilizes three input variables (Latitude, Longitude and altitude) data for spatial modeling but the other methods use only two input variables (Latitude and Longitude). In addition, the wind speed and direction maps were compatible with the results of spatial modeling of carbon monoxide. Kriging was the appropriate method after Cokriging.