Space-time interpolation by combining air pollution and meteorologic variables

In this paper, a technique is proposed in order to study triple time series. It combines the variable of interest, sulfur dioxide (SO₂) with two related meteorological variables. Hence, three variables measured at the same time points are jointly analyzed. Instead of using classical multiple time series analysis, it is suggested to consider the measurements of the two meteorological variables as coordinates of a two-dimensional space and the simultaneous observation of the third variable (associated SO₂ concentrations) at each pair of coordinates. Subsequently, well-known optimum interpolation is used for predicting the SO₂ concentrations on the basis of six meteorological variables. All the variables of the study are measured at the same times (all days in 2000) around the city of Istanbul, Turkey. The triple diagrams, in the form of contour maps, help to answer various questions concerning the SO₂ concentration variability with respect to meteorological variables. The same diagrams also provide a basis for the prediction of SO² concentrations. It is shown that the relative prediction error is less than 10%, which is acceptable for the practical studies.