Industrial Atmospheric Pollution Estimation Using Gaussian Process Regression

Industrial pollution remains a major challenge in spite of recent technological developments and purification procedures. To effectively monitor atmosphere contamination, data from air quality networks should be coupled with advanced spatiotemporal statistical methods.

Our previous studies showed that standard interpolation techniques (like inverse distance weighting, linear or spline interpolation, kernel-based Gaussian Process Regression, GPR) are quite limited for the simulation of a smoke-like narrow-directed industrial pollution in the vicinity of the source (a few tenths of kilometers). In this work, we try to apply GPR, based on statistically estimated covariances. These covariances are calculated using СALPUFF atmospheric pollution dispersion model for a one-year simulation in the Kryvyi Rih region. The application of GPR permits taking into account high correlations between pollution values in neighboring points revealed by modeling. The result of the GPR covariance-based technique is compared with other interpolation techniques. It can be used then in the estimation and optimization of air quality networks.