Abstract: Aiming at the problem that the cooperative Kriging interpolation method has higher computational complexity when the auxiliary variables are numerous, and the interpolation precision is lower when the interpolation variables are less, a cooperative Kriging interpolation method based on principal components analysis is proposed (PCA-CoKriging). This method first uses the principal components analysis to reduce the dimension of the related interpolation variable, obtains several comprehensive indexes, and then uses these comprehensive indexes as the auxiliary variables to conduct cooperative Kriging interpolation. In order to verify the effectiveness of the method and the influence of the data distribution on the method, four groups of data about PM_2.5 concentration in the four seasons in Beijing in 2016 which meet different effects of normal distribution are selected, and PCA-CoKriging, ordinary Kriging interpolation method, and conventional Co-Kriging interpolation method are used to carry out interpolation experiments. The results show that the mean square error in the method of this article, ordinary Kriging interpolation method, and conventional Co-Kriging interpolation method are 4.91, 6.04 and 5.61 respectively, and the average root mean square errors are 6.65, 8.76 and 7.57 in the four groups. In comprehensive comparison, the mean absolute error and root mean square error in the proposed method have increased by 10.73% and 12.56% respectively compared with those of the conventional CoKriging interpolation, and 18.71% and 24.09% respectively compared with those of the ordinary Kriging interpolation method.

Key words: principal component analysis, CoKriging, Pearson correlation coefficient, PM_2.5