A lot of data which are used in environment analysis of air pollution have characteristics that
are distributed continuously in space. In this point, the collected data value such as
precipitation, temperature, altitude, pollution density, PM10 have spatial aspect. When
geostatistical data analysis are needed, acquisition of the value in every point is the best way,
however, it is impossible because of the costs and time. Therefore, it is necessary to estimate the
unknown values at unsampled locations based on observations.
In this study, spatial interpolation method such as local trend surface model, IDW(inverse
distance weighted), RBF(radial basis function), Kriging were applied to PM10 annual average
concentration of Seoul in 2005 and the accuracy was evaluated. For evaluation of interpolation
accuracy, range of estimated value, RMSE, average error were analyzed with observation data.
The Kriging and RBF methods had the higher accuracy than others.