Convolutional attention with roll padding: Classifying PM2.5 concentration levels in the city of Beijing

A precise and timely classification of particulate matter 2.5 concentration levels is important for the design of air quality regulatory measures in a contemporaneous context characterized by the transition to a low-carbon economy. This study uses a well-known air quality dataset retrieved from the University of California at Irvine repository, which consists of a multivariate time series covering particulate matter 2.5 concentration levels in the city of Beijing for a period of 5 years. We train, test, and validate several deep learning architectures for a multinomial classification of the target variable in the period of 24 h ahead from the contemporaneous moment of action relying on historical information about the last 168 h and considering a sliding window of 24 h to construct examples. Results indicate that the internationally patented Variable Split Convolutional Attention model exhibits the best accuracy. The main novelty of this model consists of introducing bidimensional convolutional operations inside the attention block to capture the relative attention weight given to patterns of contiguous segments within different time-steps for each input variable. Therefore, a valuable deep learning architecture is presented to properly classify particulate matter 2.5 concentration levels in the atmosphere.