Environmental Science and Pollution Research (2021) 28:33120–33132
https://doi.org/10.1007/s11356-021-12735-x
RESEARCH ARTICLE
Can particulate matter be identified as the primary cause of the rapid
spread of CoViD-19 in some areas of Northern Italy?
Maria Cristina Collivignarelli 1,2 & Alessandro Abbà 3 & Francesca Maria Caccamo 1
Roberta Pedrazzani 4 & Marco Baldi 5 & Paola Ricciardi 1 & Marco Carnevale Miino 1
&
Giorgio Bertanza 3
&
Received: 27 July 2020 / Accepted: 26 January 2021 / Published online: 26 February 2021
# The Author(s) 2021
Abstract
Northern Italy was the most affected by CoViD-19 compared to other Italian areas and comprises zones where air pollutants
concentration was higher than in the rest of Italy. The aim of the research is to determine if particulate matter (PM) has been the
primary cause of the high CoViD-19 spread rapidity in some areas of Northern Italy. Data of PM for all the 41 studied cities were
collected from the local environmental protection agencies. To compare air quality data with epidemiological data, a statistical
analysis was conducted identifying the correlation matrices of Pearson and Spearman, considering also the possible incubation
period of the disease. Moreover, a model for the evaluation of the epidemic risk, already proposed in literature, was used to
evaluate a possible influence of PM on CoViD-19 spread rapidity. The results exclude that PM alone was the primary cause of the
high CoVid-19 spread rapidity in some areas of Northern Italy. Further developments are necessary for a better comprehension of
the influence of atmospheric pollution parameters on the rapidity of spread of the virus SARS-CoV-2, since a synergistic action
with other factors (such as meteorological, socio-economic and cultural factors) could not be excluded by the present study.
Keywords PM10 . PM2.5 . SARS-CoV-2 . Doubling time . Coronavirus . Epidemic
Introduction
A strong correlation between air particulate pollution and the
increase of autoimmune and respiratory diseases has been
confirmed by several studies (Cruz-Sanchez et al. 2013;
Horne et al. 2018; Tateo et al. 2019; Xu et al. 2016; Zhou
et al. 2015). Moreover, recent studies highlighted a positive
Responsible editor: Philippe Garrigues
* Marco Carnevale Miino
marco.carnevalemiino01@universitadipavia.it
1
Department of Civil Engineering and Architecture, University of
Pavia, via Ferrata 3, 27100 Pavia, Italy
2
Interdepartmental Centre for Water Research, University of Pavia,
via Ferrata 3, 27100 Pavia, Italy
3
Department of Civil, Environmental, Architectural Engineering and
Mathematics, University of Brescia, via Branze 43,
25123 Brescia, Italy
4
Department of Mechanical and Industrial Engineering, University of
Brescia, via Branze 38, 25123 Brescia, Italy
5
Department of Chemistry, University of Pavia, viale Taramelli 10,
27100 Pavia, Italy
correlation between the mortality rate for CoViD-19 and longterm exposure to high concentrations of pollutants such as
particulate matter (PM), SO2, CO, NO2 and O3 (Coccia
2020; Ogen 2020; Coker et al. 2020; Wu et al. 2020a,
2020b; Perone 2021; Yari and Moshammer 2020). For instance, Cole et al. (2020) observed a link between long-term
PM2.5 exposure and CoViD-19 cases, hospital admissions and
deaths in 355 municipalities in the Netherlands. Moreover,
Isphording and Pestel (2020) statistically analysed the proliferation and aggressiveness of CoViD-19 in Germany
highlighting a strong correlation between short-term exposure
to air pollution and severe clinical reactions. However, the air
pollution may have influenced not only the aggressiveness of
CoViD-19.
The Italian Society of Environmental Medicine (SIMA)
(SIMA 2020) supposed for the first time a possible correlation
between the significant spread of coronavirus disease
(CoViD-19), caused by Severe Acute Respiratory Syndrome
Coronavirus 2 (SARS-CoV-2) (Collivignarelli et al. 2020b),
in Northern Italy and the high levels of PM10 and PM2.5. Setti
et al. (2020a, 2020b) and several other authors supposed that
PM could acts as a support for novel SARS coronavirus
(SARS-CoV-2), allowing the spread and the transport even
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Fig. 1 Map of the area analysed in the study and its location in Italy. The
capital city for each province is highlighted in grey. The map has been
realised with QGIS (2020). AL: Alessandria; AO: Aosta; AT: Asti; BG:
Bergamo; BI: Biella; BL: Belluno; BO: Bologna; BS: Brescia; CN:
Cuneo; CO: Como; CR: Cremona; FC: Forlì and Cesena; FE: Ferrara;
GE: Genoa; LC: Lecco; LO: Lodi; MB: Monza; MI: Milan; MN:
Mantova; MO: Modena; NO: Novara; PC: Piacenza; PD: Padua; PR:
Parma; PV: Pavia; RA: Ravenna; RE: Reggio Emilia; RI: Rimini; RO:
Rovigo; SO: Sondrio; SP: La Spezia; SV: Savona; TN: Trento; TO:
Turin; TV: Treviso; VA: Varese; VB: Verbania; VC: Vercelli; VE:
Venice; VI: Vicenza; VR: Verona
for significant distances. According to this thesis, PM could
represent a substrate that allows the virus to remain in the air in
a contagious form for hours or days, promoting its diffusion
(Sanità di Toppi et al. 2020; SIMA 2020). On the contrary,
Belosi et al. (2021) showed that the probability of transmission in the outdoor environment of SARS-CoV-2 is not subjected to a substantial increase even in the presence of a high
concentration of PM. This would seem to deny the presence of
a possible strong correlation between the high rapidity of
CoViD-19 spread in some areas of northern Italy and atmospheric PM. Therefore, due to contrasting results observed,
this aspect is still the subject of analysis and discussion by
the scientific community.
To date, no studies evaluated whether PM10 and PM2.5
influence the diffusion rapidity of the virus (intended as doubling time and seeding time (DT and ST), respectively),
playing or not a key role in the massive spread of CoViD19. This study aims to explore this relationship in Northern
Italy, which has been the most affected area by CoViD-19
(INCP 2020), and is also the portion of the country presenting
some areas with the highest amount of atmospheric PM often
exceeding the legislative limit (Ionescu et al. 2013; Torretta
et al. 2013; Masiol et al. 2015). Together with Poland and
Bulgaria, Northern Italy has the worst air quality in Europe
in terms of PM (EEA 2019, 2018, 2017). Data of PM10 and
PM2.5 were analysed and, considering an incubation time of
10–15 d, were compared with the CoViD-19 rapidity of
spread, evaluated using the ST and DT, in 41 cities of
Northern Italy. In order to compare air quality data with epidemiological data, a statistical analysis was conducted and
also the CoViD-19 ST/DT model proposed by Zhou et al.
(2020) for the evaluation of the epidemic risk was used to
investigate a possible correlation with PM.
Methods
Area of the study
Considering that CoViD-19 has broken out in Northern Italy,
this part of the country has been selected in order to detect a
possible correlation between epidemic spread and PM in air.
According to the most recent available data, the area of the
study was larger than 100,000 km2 (ISTAT 2020a) and divided in 41 provinces, in seven different Regions (Piedmont,
Valle d’Aosta, Lombardy, Liguria, Veneto, Trentino and
Emilia-Romagna) totally accounting for around 25.8 million
of inhabitants (ISTAT 2020b). The analysis has been applied
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Fig. 2 Selected periods for air quality monitoring and epidemiological
data collection. T0 represents the first case identified in each province and
reported by official data and T1 the day before the rise of the
epidemiological curve. For the determination of T0 and T1, please refer
to “Epidemiological analysis”. AL: Alessandria; AO: Aosta; AT: Asti;
BG: Bergamo; BI: Biella; BL: Belluno; BO: Bologna; BS: Brescia; CN:
Cuneo; CO: Como; CR: Cremona; FC: Forlì and Cesena; FE: Ferrara;
Environ Sci Pollut Res (2021) 28:33120–33132
GE: Genoa; LC: Lecco; LO: Lodi; MB: Monza; MI: Milan; MN:
Mantova; MO: Modena; NO: Novara; PC: Piacenza; PD: Padua; PR:
Parma; PV: Pavia; RA: Ravenna; RE: Reggio Emilia; RI: Rimini; RO:
Rovigo; SO: Sondrio; SP: La Spezia; SV: Savona; TN: Trento; TO:
Turin; TV: Treviso; VA: Varese; VB: Verbania; VC: Vercelli; VE:
Venice; VI: Vicenza; VR: Verona
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Fig. 3
city were calculated with the median, the standard deviation
and the confidence interval.
Value of seeding time (ST) and doubling time (DT) for each city.
AL: Alessandria; AO: Aosta; AT: Asti; BG: Bergamo; BI: Biella; BL:
Belluno; BO: Bologna; BS: Brescia; CN: Cuneo; CO: Como; CR:
Cremona; FC: Forlì and Cesena; FE: Ferrara; GE: Genoa; LC: Lecco;
LO: Lodi; MB: Monza; MI: Milan; MN: Mantova; MO: Modena; NO:
Novara; PC: Piacenza; PD: Padua; PR: Parma; PV: Pavia; RA: Ravenna;
RE: Reggio Emilia; RI: Rimini; RO: Rovigo; SO: Sondrio; SP: La
Spezia; SV: Savona; TN: Trento; TO: Turin; TV: Treviso; VA: Varese;
VB: Verbania; VC: Vercelli; VE: Venice; VI: Vicenza; VR: Verona
on the capital of each province. In Fig. 1, the map of the
selected provinces and the location of capital cities are
reported.
Epidemiological data collection and processing
The epidemiological evolution of the new SARS-CoV-2 cases
was not available at city level. Therefore, the epidemiological
data referred to each single province, provided by the Italian
National Civil Protection (INCP 2020), were considered. In
order to compare the spread rapidity of the CoViD-2019,
seeding time (ST) and doubling time (DT) were used. As
proposed by Zhou et al. (2020), two authors independently
selected the date on which each epidemic curve seemed to rise
and, in case of discrepancy, a third author defined which of the
two dates to choose. The median of the cumulative cases reported up to the day before the rise of the curve (T1) was
called seeding number (SN). The ST was considered equivalent to the time that elapses between the first case identified in
each province and reported by official data (T0) and the
achievement of a number of cases equal to SN. To quantify
the DT, the new cumulative cases from T1 were fitted with
exponential curve (Eqs. (1) and (2)):
I ¼ I 0 þ a1 *et=a2
ð1Þ
DT ½d ¼ a2 *lnð2Þ
ð2Þ
where I represents the number of new infections and t [d] is the
progressive number of days (considering T1 as the first day).
Particulate matter data collection and processing
Data of PM10 and PM2.5, for all cities, were collected from the
local environmental protection agencies (APPA Trento 2020;
ARPA Emilia-Romagna 2020; ARPA Liguria 2020; ARPA
Lombardia 2020; ARPA Piemonte 2020; ARPA Valle
d’Aosta 2020; ARPA Veneto 2020). All air quality control
units, located in the capital cities, which measured PM in the
selected periods, were used (Table S1) in order to obtain PM10
and PM2.5 concentrations. Forlì and Cesena are co-capitals of
their province and were considered as a single city. Data of
PM2.5 in Belluno, Ferrara and Reggio Emilia were not available. The daily averages (24 h) of the air pollutants for each
Comparison of the data
In order to compare air quality data (PM10 and PM2.5) with
epidemiological data (ST and DT), a statistical analysis was
conducted, and the correlation matrices of Pearson and
Spearman were identified. Moreover, three different fittings
(linear, quadratic and cubic) were used to investigate a correlation between the CoViD-19 spread rapidity in Northern Italy
and PM. Finally, the CoViD-19 model, already proposed by
Zhou et al. (2020) for the evaluation of the epidemic risk of a
given area based on the DT and the ST, was used and the
results were compared with the concentration of PM to investigate the possible influence on CoViD-19 spread rapidity.
Determination of periods
The choice of the periods in which to select the epidemiological and the air quality data has been made considering the
average incubation time of the SARS-CoV-2 to evaluate the
actual period during which contagion among people could
have occurred. Several studies determined that the incubation
time could be up to 10–15 d (Backer et al. 2020; Lai et al.
2020; Li et al. 2020). Therefore, the air quality data have been
selected anticipating by 15 d the T0 and ending, as a maximum precautionary limit, in the 8th March 2020 (for the determination of T0, please refer to “Epidemiological analysis”).
In the 8th March 2020, several restrictions were imposed in
part of Northern Italy, and in 9th March, 2020, they were
extended at the rest of the country (DPCM 2020a, b).
Following the further increase in the number of infections,
the restrictions were made even more severe starting from
March 11th, 2020 (DPCM 2020c). In order to study only the
exponential tract of the contagion curve, no epidemiological
data after the 18th March 2020 were considered (Fig. 2). In
some red areas (around the city of Lodi), the restrictions have
been imposed earlier than in the rest of the region. The different lockdown timing could have direct repercussions on the
epidemiological curve, and therefore in Lodi, the selected period ended in 1 March 2020 (IMH 2020a, 2020b). Also in
Bergamo and Lecco, the influence due to lockdown was already visible before the 18th March 2020. In these cases, the
selected periods were shortened.
Results and discussion
Epidemiological analysis
The official number of infections has been used to determine
T0 and T1 for each province (Table S2), and fitting the
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Fig. 4 PM10 and PM2.5 concentrations during air quality monitoring
period for each city. In brackets, numbers of data are reported. Boxplots
represent the distance between the first and third quartiles while whiskers
are set as the most extreme (lower and upper) data point not exceeding 1.5
times the quartile range from the median. AL: Alessandria; AO: Aosta;
AT: Asti; BG: Bergamo; BI: Biella; BL: Belluno; BO: Bologna; BS:
Brescia; CN: Cuneo; CO: Como; CR: Cremona; FC: Forlì and Cesena;
FE: Ferrara; GE: Genoa; LC: Lecco; LO: Lodi; MB: Monza; MI: Milan;
MN: Mantova; MO: Modena; NO: Novara; PC: Piacenza; PD: Padua;
PR: Parma; PV: Pavia; RA: Ravenna; RE: Reggio Emilia; RI: Rimini;
RO: Rovigo; SO: Sondrio; SP: La Spezia; SV: Savona; TN: Trento; TO:
Turin; TV: Treviso; VA: Varese; VB: Verbania; VC: Vercelli; VE:
Venice; VI: Vicenza; VR: Verona
cumulative number of total infections from T1 by an exponential curve (Fig. S1 and Tab. S3), the ST and DT have been
calculated for each city considered in the study (Fig. 3). These
values varied significantly.
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Table 1
PM2.5.
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Pearson and Spearman correlations for DT, ST, PM10 and
DT
ST
DT
Pearson
1
ST
Spearman
Pearson
Spearman
1
−0.0398b
−0.2307a
PM10
Pearson
0.0065b
PM2.5
b
Spearman
Pearson
0.0436
0.0646b
Spearman
0.2361a
a
p-value >0.05.
b
p-value < 0.05
PM10
PM2.5
1
1
0.9122b
0.8667a
1
1
1
1
−0.3887a
−0.3785a
−0.3969a
−0.3990a
Regarding the ST, the areas that have values equal to 1, and
which a surge in infections has been highlighted right from the
start, are those that showed the first clusters of CoViD-19
(mainly south—Lombardy and Padua in Veneto). Other provinces, on the other hand, showed an effective growth of the
epidemic curve only several days after the first recorded cases
(e.g. 12 days in Sondrio, 11 days in La Spezia, 8 days in
Ravenna and 5 days in Como and Varese).
Asti and Savona showed the higher DT, 14.9 d and 11.4
d, respectively, representing a slower spread of the CoViD19 infection among the population. On the contrary, other
cities such as Lodi, Aosta, Novara and Turin were
characterised by a DT fewer than 2.5 d. In these cases, the
transmission of the virus among the population was faster.
As expected, the minor DT (1.2 d) belongs to Lodi which
was the first area of contagion and outbreak of the CoViD19 in Italy. The DT obtained in this study is in accordance
with other results reported in scientific literature. D’Arienzo
and Coniglio (2020) identified DT equals to 3.1 d for Italy in
the period February 25th–March 12th, 2020. In the period
February 20th–March 24th, in the Italian regions of
Lombardy and Emilia Romagna, Riccardo et al. (2020)
evaluated DT equals to 2.7 d. Setti et al. (2020b) highlighted
that in Milan, before March 13th, DT was 2.0 d.
Particulate matter
In Fig. 4, the average and median values of PM10 and PM2.5 in
each city for the selected periods are shown. Among the 41
cities, the situation was very heterogeneous. Cremona, Lodi,
Milan, Modena, Padua, Parma, Pavia, Rovigo, Turin, Treviso,
Venice and Vicenza presented a mean value of PM10 above
40 μg m−3 and the highest mean value of PM10 (48.8 μg m−3)
was reached in Turin. A similar trend was observed for PM2.5,
where a concentration higher than 30 μg m−3 was found in
Cremona, Lodi, Monza, Padua, Pavia, Rovigo, Treviso,
Venice and Vicenza. In this case, Padua showed the highest
mean value of PM2.5 (37.4 μg m−3). On the contrary, the
lowest mean values of PM10 and PM2.5 were detected in
Aosta and were equal to 13.8 μg m−3 and 9.5 μg m−3, respectively. Other areas with the low mean values of PM10 and
PM2.5 were the seaside cities of Genoa (20.6 μg m−3 and
11.8 μg m −3 , respectively), Savona (20.6 μg m −3 and
12.5 μg m−3, respectively) and La Spezia (21.2 μg m−3 and
10.3 μg m−3, respectively), also in this case probably due to
weather conditions, such as wind and precipitation, that could
have positively influenced the air quality. In fact, the concentration of atmospheric PM is highly sensitive to weather conditions and factors such as wind and precipitation can strongly
influence its concentration in the air (Baklanov et al. 2016;
Collivignarelli et al. 2020a).
Statistical analysis and discussion
To assess a possible dependence between the rapidity of
spread of CoViD-19 among the population and the concentration of PM10 and PM2.5, air quality and epidemiological data
were analysed and Pearson and Spearman correlations were
calculated (Table 1). The results show that Spearman’s R is
higher for DT-PM10 and DT-PM2.5: 0.0436 and 0.2361, respectively. Pearson’s and Spearman’s R are substantially
equal for ST-PM10 and ST-PM2.5. Moreover, as confirmed
by the literature (Andrée 2020), a very strong positive correlation between PM10 and PM2.5 exists (R= 0.9122 and 0.8667
with Pearson and Spearman, respectively). It is however evident that the correlation indices between PM and the DT of the
number of infected people (inversely proportional to propagation speed of the epidemic) are substantially very low. The
results highlighted also low negative values of Pearson and
Spearman correlation indices between PM and the ST (also in
this case, inversely proportional to the rapidity of CoViD-19
spread). The fact that ST and DT are unrelated to each other
(both for Pearson and for Spearman) represent an aspect that
highlights how these two parameters are strictly influenced by
other aspects, such as sociability and living conditions,
resulting from the presence of the coronavirus.
In order to better study the behaviour of ST and DT as a
function of PM10 and PM2.5, these values have been fitted
with linear, quadratic and cubic functions (Fig. 5). In the STPM10 case, the function that best approximates the points is
that of 3rd degree (R2 = 0.227), while in the case of ST-PM2.5,
all functions return substantially equal R2 (0.158–0.162). In
the DT-PM10 and DT-PM2.5 analysis, the R2 remained always
nearly to zero. In all cases, two aspects can be highlighted: (i)
the 2nd- and 3rd-degree functions do not substantially improve the fitting of the points with respect to the linear function and (ii) the R2 values remain decidedly low in order to
define the accurate fitting.
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Fig. 5 Fitting of ST and DT with PM10 and PM2.5 with linear (red), parabolic (green) and cubic (blue) functions. The coloured bands represent the 95%
confidence interval. n: number of data
Zhou et al. (2020) proposed a model for the assessment
of epidemic risk in different countries of the world determining four risk categories (high risk, moderately high
risk, moderately low risk and low risk) based on the values
of ST and DT. This model has been applied on the data
processed in the current study, and the results were compared with the mean concentration of PM to investigate a
possible correlation (Fig. 6).
According to the model of Zhou et al. (2020), the results
show that almost all the cities in Northern Italy analysed were
in the high, moderately high, and moderately low risk
bands in the initial phase of CoViD-19 spread. In the
low epidemic risk band, the measured PM10 concentration was slightly lower than in the other bands.
However, this last result should be considered partial
given the limited sample size (n = 3). Comparing the
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Fig. 6 Map of the risk using the
ST/DT model proposed by Zhou
et al. (2020). α: high risk; β:
moderately high risk; γ: moderately low risk; δ: low risk
PM concentrations in the cities located in the other
bands, no substantial differences were highlighted. For
instance, the average concentration of PM10 and PM2.5
in the areas identified at moderately low risk was slightly higher than that measured in the areas considered at
high risk (Fig. 7). Therefore, also using the risk model
proposed by Zhou et al. (2020), no strong influence of
PM on CoViD-19 spread rapidity can be highlighted.
The strength of this study lies in immediacy and practicality.
Comparing the epidemiological and air quality data of 41 cities in
Northern Italy in the most affected regions from CoViD-19 and
taking also into account the possible incubation period of the
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Fig. 7 PM10 and PM2.5
concentrations by risk bands.
Boxplots represent the distance
between the first and third
quartiles while whiskers are set as
the most extreme (lower and
upper) data point not exceeding
1.5 times the quartile range from
the median. n: number of data; α:
high risk; β: moderately high risk;
γ: moderately low risk; δ: low
risk
disease, the results allowed excluding that high concentration of
PM was the primary cause of the high rapidity of CoViD-19
spread. This agrees with what Belosi et al. (2021) found. By
estimating the concentration of SARS-CoV-2 in outdoor air in
Lombardy using a box model, they excluded that the presence of
high concentrations of PM could act as a vehicle for the infection
(Belosi et al. 2021).
However, the opinion of the scientific community on the influence of PM on the infected population rate is conflicting.
Other studies obtained positive correlations among the number
of infected, mortality, and the concentration of PM in the air (Ma
et al. 2020; Zhu et al. 2020). For instance, Setti et al. (2020b)
would seem to have obtained an opposite result compared to the
present study, highlighting a clear correlation between PM and
SARS-CoV-2 infections. However, the results of the present
study are only partially comparable with literature (e.g., Setti
et al. (2020b)), where in most cases the correlation between
PM and the rate of infected people (cases/population) was
assessed, instead of the rapidity of contagion with the use of
DT. This difference in result could be attributed to different
methodological approaches and also to different scopes. In fact,
the present paper aims to evaluate the possible primary role of
PM in the rapidity of virus transmission rather than investigating
the influence of average daily PM10 exceedances on the rate of
infections. Also, Delnevo et al. (2020) observed that a possible
statistical correlation between air pollution and CoViD-19 infections, in Emilia-Romagna (Italy), could exist. However, even in
this case, the results are only partially comparable with the present study as the new daily infections were used to evaluate the
epidemiological situation, instead of the rapidity of contagion
intended as DT.
Despite the results of the present study exclude that PM
alone was the primary the high CoViD-19 spread rapidity in
some areas of Northern Italy, it is becoming increasingly clear,
also by following the epidemic trends worldwide, that several
other aspects should also be considered. Focusing exclusively
on air pollution can lead to spurious associations, because
socio-economic and cultural factors often play a concurrent
role. Andree (2020) presents a detailed analysis of the Dutch
situation, by studying areas, hotspots, pollution loads, social
links, habits, age, gender, household composition and
lifestyle. In addition, the variability in healthcare systems
and identification practises of infected people highly affect
the data about pandemic behaviour. Liu et al. (2020) observed
that also meteorological factors play a role in the CoViD-19
transmission and SARS-CoV-2 transmission was likely
favoured by low temperature, mild diurnal temperature range
and low humidity. Therefore, a synergistic action of PM with
other factors, e.g., other air pollutants, meteorological conditions and socio-economic aspects, could not be excluded.
Discussion about the possible limitations
of the present study
These results should be considered in the light of some possible limitations. In this study, the Authors did not consider the
synergistic action of PM with other factors. As for other viral
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pathogens, high particulate concentration plays a crucial role
in weakening the immune system (Glencross et al. 2020). In
case of chronic exposure, the atmospheric particulate has
proved to indirectly promote the diffusion of SARS-CoV-2,
e.g., by enhancing its adhesion to angiotensin-converting enzyme 2 (ACE2) (Comunian et al. 2020; Tung et al. 2021).
Together with particulate matter, the long-term exposure to
other atmospheric pollutants increases the susceptibility not
only to respiratory viral (and bacterial) pathogens but also,
exerting a chronic inflammatory stimulus, to cardiovascular
and neoplastic diseases (Andersen et al. 2017; Kim et al.
2017; Yang et al. 2019; Conticini et al. 2020; Fattorini and
Regoli 2020; Iriti et al. 2020). These represent co-morbidity
factors in case of CoViD-19 (Sanyaolu et al. 2020). Indeed,
several other authors considered also the parallel contribution
of other socio-economic aspects (e.g., density and age of population, mobility of population and healthcare expenditures)
(e.g., Andree 2020). However, our goal was assessing the
possible primary role of PM in SARS-CoV-2 transmission
in Northern Italy, hence in epidemic spread, rather than investigating the severity of cases. In fact, in this study also, the
results of swabs of asymptomatic subjects have been counted.
Failure to consider social distancing measures adopted by
Italian population independently from Government actions could
represent another limitation of this work because this could make
difficult to compare the differences in DTs. However, to overcome this aspect focusing on the massive increase occurred before the blockade was imposed, the authors neglected the data
(i.e., the number of positive swabs) of the period in which the
effects of the lockdown started to be visible.
Moreover, the possibility that a person residing in one province may be infected in another following a travel for work, study
or leisure purposes was also not considered and could represent a
limitation of the study. Mobility between provinces and regions
is an aspect that could be studied in the future by adopting an
estimate of the mobility rate despite this being opposed to the
immediacy and practicality of the current model.
Finally, the geographical area that the authors chose can
appear slightly exiguous knowing that the use of macroscopic
statistical models to small territories involves a great variability. In the initial phase of CoViD-19 spread, Italy was almost
divided in two parts, being Centre and South all but free from
infection nuclei. Therefore, including a broader area, beside
North, would have led to inaccuracy. Northern Italy represented a grave and unique reality in Europe, because, there, first,
the pandemics burst and grew exponentially. After this heavy
onset, the lockdown national restrictions prescribed by the
Government prevented the virus from spreading massively
also in Central and Southern Italy. This scenery maintained
“double” until the end of Italian first wave. Our criterion of
data selection proved to be consistent again considering that in
Centre and South the growth of the pandemic was detected
after lockdown was imposed and therefore affected by the
Environ Sci Pollut Res (2021) 28:33120–33132
government measures. Northern Italy accounts for 34% of
the Italian land and presents a huge heterogeneity in terms of
PM concentration. The precise objective of the present investigation was to focus on the regions where people were heavily hit by the pandemics.
Conclusion
In this work, considering an incubation time of 10–15 d, data
on the concentration of PM10 and PM2.5 were analysed and
compared with the rapidity of spread of CoViD-19 (intended
as ST and DT) in 41 cities of Northern Italy. This work excludes that PM alone was the primary the high CoViD-19
spread rapidity. Pearson’s and Spearman’s indices did not
highlight any correlation between PM and epidemiological
data. Moreover, three different fittings (linear, quadratic and
cubic) were used and in all cases of comparison two aspects
can be highlighted: (i) the 2nd- and 3rd-degree functions do
not substantially improve the fitting of the points with respect
to the linear function and (ii) the R2 values remain decidedly
low in order to define the accurate fitting. A model ST/DT for
the evaluation of the epidemic risk, already proposed in literature, was applied but no strong influence of PM has been
found. However, the authors do not exclude that a synergistic
action with other factors (such as meteorological, socioeconomic and cultural factors) could exist and therefore other
studies to further comprehend, for instance, the influence of
other atmospheric pollution parameters (e.g., NOx), meteorological conditions (e.g., temperature, solar irradiance and humidity), lifestyle and social links on the rapidity of spread of
the SARS-CoV-2 are strongly suggested.
Supplementary Information The online version contains supplementary
material available at https://doi.org/10.1007/s11356-021-12735-x.
Author contribution Maria Cristina Collivignarelli: Conceptualisation,
methodology, supervision, validation, writing
Alessandro Abbà: Methodology, validation, visualisation, writing
Francesca Maria Caccamo: Data curation and formal analysis, writing
Giorgio Bertanza: Methodology, validation, writing
Roberta Pedrazzani: Data curation and formal analysis, validation,
writing
Marco Baldi: Visualisation, validation
Paola Ricciardi: Visualisation, writing
Marco Carnevale Miino: Conceptualisation, data curation and formal
analysis, methodology, supervision, validation, writing
Funding Open access funding provided by Università degli Studi di
Pavia within the CRUI-CARE Agreement.
Availability of data and materials All data generated or analysed during
this study are included in this published article
Code Availability Not applicable
Environ Sci Pollut Res (2021) 28:33120–33132
Declarations
Ethical approval Not applicable
Consent to participate Not applicable
Consent to publish Not applicable
Competing interests The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were
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in the article's Creative Commons licence, unless indicated otherwise in a
credit line to the material. If material is not included in the article's
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