Predicting ground-level nitrogen dioxide concentrations using the BaYesian attention-based deep neural network

Abstract

Nitrogen dioxide pollution is an ongoing and growing environmental issue that affects human health in developed Western countries. This study introduced a Bayesian attention-based deep neural network model for predicting ground-level nitrogen dioxide concentrations. The proposed model integrates the principles of the Bayesian neural network and the attention mechanism, enabling it to produce predicted values and their associated uncertainties, expressed as standard deviations. The proposed model was validated using 2020 data collected from 520 European Environmental Agency stations, located in Italy. The performance of the model was assessed using the mean absolute error.