Air quality is crucial for public health and the environment, therefore, accurate prediction of particulate matter (PM) concentration is a significant challenge in the field of environmental protection. In this paper, we present and compare three approaches to predicting particulate matter concentration: convolutional-recurrent autoencoder, hierarchical autoencoder and variational autoencoder. We conducted experiments on real air quality datasets, evaluating the performance of the methods in terms of prediction accuracy. The results indicate that the proposed approaches can effectively predict changes in particulate matter concentration, which is of great importance for early warning systems and air quality management strategies.