Статья

Machine learning reveals that prolonged exposure to air pollution is associated with SARS-CoV-2 mortality and infectivity in Italy

R. Cazzolla Gatti, A. Velichevskaya, A. Tateo, N. Amoroso, A. Monaco,
2020

Air pollution can increase the risk of respiratory diseases, enhancing the susceptibility to viral and bacterial infections. Some studies suggest that small air particles facilitate the spread of viruses and also of the new coronavirus, besides the direct person-to-person contagion. However, the effects of the exposure to particulate matter and other contaminants on SARS-CoV-2 has been poorly explored. Here we examined the possible reasons why the new coronavirus differently impacted on Italian regional and provincial populations. With the help of artificial intelligence, we studied the importance of air pollution for mortality and positivity rates of the SARS-CoV-2 outbreak in Italy. We discovered that among several environmental, health, and socio-economic factors, air pollution and fine particulate matter (PM2.5), as its main component, resulted as the most important predictors of SARS-CoV-2 effects. We also found that the emissions from industries, farms, and road traffic - in order of importance - might be responsible for more than 70% of the deaths associated with SARS-CoV-2 nationwide. Given the major contribution played by air pollution (much more important than other health and socio-economic factors, as we discovered), we projected that, with an increase of 5–10% in air pollution, similar future pathogens may inflate the epidemic toll of Italy by 21–32% additional cases, whose 19–28% more positives and 4–14% more deaths. Our findings, demonstrating that fine-particulate (PM2.5) pollutant level is the most important factor to predict SARS-CoV-2 effects that would worsen even with a slight decrease of air quality, highlight that the imperative of productivity before health and environmental protection is, indeed, a short-term/small-minded resolution.

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Версии

  • 1. Version of Record от 2020-12-01

Метаданные

Об авторах
  • R. Cazzolla Gatti
    Tomsk State University, Konrad Lorenz Institute for Evolution and Cognition Research
  • A. Velichevskaya
    Tomsk State University
  • A. Tateo
    Università degli Studi di Bari
  • N. Amoroso
    Università degli Studi di Bari, Università degli Studi di Bari
  • A. Monaco
    Istituto Nazionale di Fisica Nucleare, Sezione di Bari
Название журнала
  • Environmental Pollution
Том
  • 267
Финансирующая организация
  • Ministero dell’Istruzione, dell’Università e della Ricerca
Номер гранта
  • PONa3_00052
Тип документа
  • journal article
Тип лицензии Creative Commons
  • CC BY
Правовой статус документа
  • Свободная лицензия
Источник
  • scopus