###### Abstract  
This study investigates the potential association between the daily distribution of the PM\({}_{2.5}\) air pollutant and the initial spreading of COVID-19 in New York City. We study the period from 4 March to 22 March 2020, and apply our analysis to all five counties, including the city, plus seven neighboring counties, including both urban and peripheral districts. Using the _Granger_ causality methodology, and considering the maximum lag period (14 days) between infection and the correspondent diagnosis, we found that the time series of the new daily infections registered in those 12 counties appear to correlate to the time series of the concentrations of the PM\({}_{2.5}\) particulate circulating in the air, with 33 over 36 statistical tests with a \(p\)-value less than 0.005, thus confirming such a hypothesis. Moreover, looking for further confirmation of this association, we train four different machine learning algorithms on a portion of those time series. These are able to predict that the number of the new daily infections would have surpassed a given infections threshold for the remaining portion of the series, with an average accuracy ranging from 84% to 95%, depending on the algorithm and/or on the specific county under observation. This is similar to other results obtained from several polluted urban areas, e.g., Wuhan, Xiaogan, and Huanggang in China, and Northern Italy. Our study provides further evidence that ambient air pollutants can be associated with a daily COVID-19 infection incidence. COVID-19; New York city; time series analysis; daily infections; air pollution; machine learning; artificial intelligence +
Footnote †: journal:  
as winter nears, cases in city are ticking upward again, with a weekly average of some 4000 new infections and 40 deaths, as registered in the week of 17-24 November [4]. Ten months after the beginning of the pandemic, many studies have been developed by scholars to investigate how COVID-19 spreads and decays [5; 6]. While scientific investigations that look for the most effective non-pharmaceutical containment countermeasures are of great interest, as they could help to keep a lid on the epidemic (including contact tracing and testing) [7; 8; 9; 10; 11; 12; 13; 14], much attention has also been paid to the factors that can favor the contagion [15; 16]. In this broad spectrum of research, studies are emerging that try to understand if a possible association exists between exposure to air pollution and COVID-19 infection and deaths. This is a rapidly expanding research area that has attracted much interest, especially from China and various European countries. The rationale behind these studies is that fine particles (especially PM\({}_{2.5}\); particles with diameter, \(\leq\)2.5 \(\upmu\)m) have been linked to various adverse health events. Long-term exposure to this kind of particulate may negatively affect the respiratory and cardiovascular systems, and increase the mortality risk--thus, exacerbating the severity of COVID-19 symptoms, and worsening the prognosis of this disease [8; 9; 17]. Besides our present study, the relationship between COVID-19 and pollution has been researched intensively from various angles, yielding many published papers thus far [18]. We consider these kinds of studies as extremely relevant, since they have opened this new research area that is still flourishing through the collaboration of many different types of scientists, including virologists, biologists, chemists, physicists, and data scientists. In this regard, the first work we would like to mention is the one by [PERSON] et al. that investigates the association between exposure to air pollution and the death rate due to COVID-19, through a nationwide study of more than 3000 counties in the United States [19]. Fed with the values of PM\({}_{2.5}\), they developed a mathematical model able to predict the extent to the county-level long-term exposure to that particulate can be associated with an increase of the COVID-19 mortality rate.