NO2 levels as a contributing factor to COVID-19 deaths: the first empirical estimate of threshold values

Marco Mele*, Cosimo Magazzino, Nicolas Schneider, Vladimir Strezov

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    30 Citations (Scopus)

    Abstract

    This study represents the first empirical estimation of threshold values between nitrogen dioxide (NO2) concentrations and COVID-19-related deaths in France. The concentration of NO2 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 NO2 in spreading the epidemic. The underlying hypothesis is that NO2, 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 NO2 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.

    Original languageEnglish
    Article number110663
    Pages (from-to)1-11
    Number of pages11
    JournalEnvironmental Research
    Volume194
    DOIs
    Publication statusPublished - Mar 2021

    Keywords

    • COVID-19
    • NO₂
    • Machine learning
    • Artificial neural networks

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