Classification and Prediction of Nitrogen Dioxide in a Portuguese Air Quality Critical Zone

Abstract

This study presents classification and prediction exercises to evaluate the future behavior of nitrogen dioxide in a critical air quality zone located in Portugal using a dataset, the time span of which covers the period between 1 September 2021 and 23 July 2022. Three main results substantiate the importance of this research. First, the classification analysis corroborates the idea of a neutrality principle of road traffic on the target since the respective coefficient is significant, but quantitatively close to zero. This result, which may be the first sign of a paradigm shift regarding the adoption of electric vehicles in addition to reflect the success of previously implemented measures in the city of Lisbon, is reinforced by evidence that the carbon monoxide emitted mostly by diesel vehicles exhibits a significant, negative and permanent effect on satisfying the hourly limit value associated with the target. Second, robustness checks confirm that the period between 8 h and 16 h is particularly remarkable for influencing the target. Finally, the predictive exercise demonstrates that the internationally patented Variable Split Convolutional Attention model has the best predictive performance among several deep learning neural network alternatives. Results indicate that the concentration of nitrogen dioxide is expected to be volatile and only a redundant downward trend is likely to be observed. Therefore, in terms of policy recommendations, additional measures to avoid exceeding the legal nitrogen dioxide ceiling at the local level should be focused on reducing carbon monoxide emissions, rather than just being concerned about halting the intensity of road traffic.