###### Abstract
This model was also reinforced with some twenty potential confounders of various socioeconomic and demographic nature, including, for example, the percentage of the population older than sixty-five years, the percentage of African Americans, and the percentage of persons affected by obesity. The authors fitted a binomial mixed model, where the COVID-19 deaths values were the outcome. Results have shown that even a small increase in the long-term exposure to PM\({}_{2.5}\) is associated with a relevant increase of the COVID-19 death rate, in the county of interest. While that paper focused on U.S. COVID-19 data in general, at the moment of writing this paper, there appears to be no published work that concentrates exclusively on NYC, and its neighboring counties. However, there have been a number of relevant publications that address a similar topic within the context of other nations, especially in Italy and in China. For example, [PERSON] et al. studied the long-term exposure to air pollution and COVID-19 infections and mortality in Italy during the first wave of the pandemic [20]. The assumption at the base of their work is that air pollution, and in particular, the particulates, like PM\({}_{2.5}\), may be a carrier for the virus, and that a person who lives in a very polluted area has a limited capacity of reacting to the virus. They considered the annual values of both the PM\({}_{10}\) and PM\({}_{2.5}\) particulates, plus other factors that could have played a role, such as population density, average income, public transport usage, and number of lung ventilators, yielding a cross-sectional regression statistical model that, again, has provided a clue about the correlation between COVID-19 infections/mortality incidence and air pollution. Along the same line of reasoning is the paper by [PERSON] et al. [21]. Their initial statement is that an epidemic model, based only on close contacts and respiratory droplets, cannot explain the excessive number of people infected with COVID-19 in Northern Italy. Hence, with univariate analysis, they have verified the correlation between the number of exceedances of the daily limit value of the PM\({}_{10}\) particulate, finding that 39 of the 41 Northern Italian provinces most impacted by COVID-19 also reported the highest PM\({}_{10}\) levels. Their conclusion is that air particulates may favor an airborne transmission of this virus, up to a distance of eight meters in an outdoor environment. In the same vein of Reference [21], but with a different statistical model based on a time series analysis, the work described in References [22; 23] has provided further proof of the causal correlation between abnormal values of various types of particulate (including PM\({}_{2.5}\), PM\({}_{10}\), and NO\({}_{2}\)) and COVID-19 infection incidence in the Italian region of Emilia-Romagna. This region is one of the most polluted areas in Italy, and the district that has had the highest death toll in Italy so far, after Lombardy. Moreover, one of these two papers, using a machine learning-based methodology (plus pollution data from previous years), also predicted (with excellent accuracy) the occurrence of the second wave of the contagion that is currently raging in that Italian region, as of November 2020. Finally, the study by [PERSON] et al. [24] used a retrospective cohort, from 25 January to 29 February 2020, from the Chinese cities of Wuhan, Xiaogan, and Huanggang. The authors found that a daily COVID-19 infection incidence was positively associated with high values of both PM\({}_{2.5}\) and humidity, in all the examined cities. After this long introduction that was needed to frame the scenario, we can finally come to the intent behind this study: To investigate the association between particulate matter and the surge of COVID-19 infections suffered in NYC, during the 2020 spring. The main motivation is that, to the best of our knowledge, there does not exist similar studies for this iconic city. To this aim, some premises are in order.