Assessing correlations between short-term exposure to atmospheric pollutants and COVID-19 spread in all Italian territorial areas

Abstract

The spread of SARS-CoV-2, the beta coronavirus responsible for the current pneumonia pandemic outbreak, has been speculated to be linked to short-term and long-term atmospheric pollutants exposure. The present work has been aimed at analyzing the atmospheric pollutants concentrations (PM₁₀, PM_2.5, NO₂) and spatio-temporal distribution of cases and deaths (specifically incidence, mortality and lethality rates) across the whole Italian national territory, down to the level of each individual territorial area, with the goal of checking any potential short-term correlation between these two phenomena. The data analysis has been limited to the first quarter of 2020 to reduce the lockdown-dependent biased effects on the atmospheric pollutant levels as much as possible. The analysis looked at non-linear, monotonic correlations using the Spearman non-parametric correlation index. The statistical significance of the Spearman correlations has also been evaluated. The results of the statistical analysis suggest the hypothesis of a moderate-to-strong correlation between the number of days exceeding the annual regulatory limits of PM₁₀, PM_2.5 and NO₂ atmospheric pollutants and COVID-19 incidence, mortality and lethality rates for all the 107 territorial areas in Italy. A weak-to-moderate correlation seems to exist when considering the 36 territorial areas in four of the most affected regions (Lombardy, Piedmont, Emilia-Romagna and Veneto). Overall, PM₁₀ and PM_2.5 showed a higher non-linear correlation than NO₂ with incidence, mortality and lethality rates. As to particulate matters, PM₁₀ profile has been compared with the incidence rate variation that occurred in three of the most affected territorial areas in Northern Italy (i.e., Milan, Brescia, and Bergamo). All areas showed a similar PM₁₀ time trend but a different incidence rate variation, that was less severe in Milan compared with Brescia and Bergamo.

Keywords: COVID-19; Non-parametric correlation; PM(10); PM(2.5), and NO(2); Short-term exposure.

Graphical abstract

Fig. 1

Air quality indicators, air quality index (AQI) and COVID-19 spread in the 107 Italian territorial areas: Panels A, B, and C, report the number of days (at least 45) that exceeded the annual regulatory limits in the period from January 1^st to March 31^st, 2020, in all 107 Italian territorial areas, with reference to PM_2.5 (panel A), PM₁₀ (panel B) and NO₂ (panel C). Panel D reports the AQI for each territorial area (measured as in section 2.5), and the average index in the period from January 1^st to March 31^st, 2020. Panels E and F report the COVID-19 incidence rate (per 100,000 inhabitants) at territorial and regional level, respectively, in the period from February 20^th to March 31^st, 2020. Data references are available in Table 1.

Fig. 2

Scatter plots for all 107 Italian territorial areas: number of days exceeding the annual regulatory limits (y-axis) for PM_2.5, PM₁₀, and NO₂, from January 1^st to March 31^st, with respect to: COVID-19 incidence rates (panels A–C, x-axis), COVID-19 mortality rates (panels D–F, x-axis), and COVID-19 lethality rates (panels G–I, x-axis), in the period from February 20^th to March 31^st. Incidence and mortality rates are reported for 100,000 inhabitants. Data references are available in Table 1.

Fig. 3

Scatter plots for the 36 territorial areas in four of the most affected Italian regions: number of days exceeding the annual regulatory limits (y-axis) for PM_2.5, PM₁₀, and NO₂, from January 1^st to March 31^st, with respect to: COVID-19 incidence rates (panels A–C, x-axis), COVID-19 mortality rates (panels D–F, x-axis) and COVID-19 lethality rates (panels G–I, x-axis), in the period from February 20^th to March 31^st. Incidence and mortality rates are reported for 100,000 inhabitants. Data references are available in Table 1.

Fig. 4

Profiles of PM_2.5 (green line) and PM₁₀ (red line) atmospheric pollutant levels in three of the most affected territorial areas in Lombardy, from January 1st to March 31st (x-axis). Panels A, B and C, report the concentrations for Milan, Brescia, and Bergamo, respectively. The y-axis reports the absolute levels of PMs in μg/m³. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5

Profile of the PM10 atmospheric pollutant levels (using a 7-day moving average) and COVID-19 incidence rate (per 100,000 inhabitants) variation in three of the most affected territorial areas in Lombardy, from March 1^st to March 31^st. Panels A, B and C, report the PM₁₀ concentrations for Milan, Brescia, and Bergamo, respectively. PM₁₀ profiles are represented by a red line with values on the y-axis, right side. Incidence rate variations are represented by blue bars with values on the y-axis, left side. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6

COVID-19 incidence rate (for 100,000 inhabitants) registered for each day in the period from February 24^th to June 2^nd (x-axis), in Milan (in green), Bergamo (in orange) and Brescia (in blue). The incidence rate values are reported on the y-axis. Data references are available in Table 1. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)