Abstract: The Gaussian-process model is a probabilistic, kernel-regression model that can be used for the identification of nonlinear dynamic systems.  A prediction of the Gaussian-process model, in addition to the mean value, also provides information about the confidence of the prediction using the prediction variance. Its modelling methodology incorporates various methods for modelling of different kinds of systems, among them online-, sparse-, deep- and other modelling methods. Recently these methods have been used for empirical modelling of different dynamic systems related to air quality. The lecture focuses on application and comparative assessment of different Gaussian-process modelling methods applied to air-quality problems. Presented case studies comprise modelling local ozone pollution and modelling of some atmospheric variables.