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This study represents the first empirical estimation of threshold values between nitrogen dioxide (NO 2) concentrations and COVID-19-related deaths in France. The concentration of NO 2 linked to COVID-19-related deaths in three major French cities were determined using Artificial Neural Networks experiments and a Causal Direction from Dependency (D2C) algorithm. The aim of the study was to evaluate the potential effects of NO 2 in spreading the epidemic. The underlying hypothesis is that NO 2 , as a precursor to secondary particulate matter formation, can foster COVID-19 and make the respiratory system more susceptible to this infection. Three different neural networks for the cities of Paris, Lyon and Marseille were built in this work, followed by the application of an innovative tool of cutting the signal from the inputs to the selected target. The results show that the threshold levels of NO 2 connected to COVID-19 range between 15.8 μg/m3 for Lyon, 21.8 μg/m3 for Marseille and 22.9 μg/m3 for Paris, which were significantly lower than the average annual concentration limit of 40 μg/m³ imposed by Directive 2008/50/EC of the European Parliament. • Machine Learning approach based on ANNs techniques is proposed on NO 2 concentrations and COVID-19 deaths. • Hourly data (41 consecutive days) for three French cities we use for our analysis. • Analysis on the effects of three different threshold levels of NO 2 compared to COVID-19 deaths. • New threshold values on NO 2 concentrations are proposed to policy makers. [ABSTRACT FROM AUTHOR]

Being heavily dependent to oil products (mainly gasoline and diesel), the French transport sector is the main emitter of Particulate Matter (PMs) whose critical levels induce harmful health effects for urban inhabitants. We selected three major French cities (Paris, Lyon, and Marseille) to investigate the relationship between the Coronavirus Disease 19 (COVID-19) outbreak and air pollution. Using Artificial Neural Networks (ANNs) experiments, we have determined the concentration of PM 2.5 and PM 10 linked to COVID-19-related deaths. Our focus is on the potential effects of Particulate Matter (PM) in spreading the epidemic. The underlying hypothesis is that a pre-determined particulate concentration can foster COVID-19 and make the respiratory system more susceptible to this infection. The empirical strategy used an innovative Machine Learning (ML) methodology. In particular, through the so-called cutting technique in ANNs, we found new threshold levels of PM 2.5 and PM 10 connected to COVID-19: 17.4 µg/m3 (PM 2.5) and 29.6 µg/m3 (PM 10) for Paris; 15.6 µg/m3 (PM 2.5) and 20.6 µg/m3 (PM 10) for Lyon; 14.3 µg/m3 (PM 2.5) and 22.04 µg/m3 (PM 10) for Marseille. Interestingly, all the threshold values identified by the ANNs are higher than the limits imposed by the European Parliament. Finally, a Causal Direction from Dependency (D2C) algorithm is applied to check the consistency of our findings. [ABSTRACT FROM AUTHOR]