1. Introduction
As similar to many other countries in the rest of the world, starting with the identification of the first case of SARS-CoV2 on 31 January 2020, Italy was hit by the pandemic wave with the consequent lockdown of all activities [
1]. During this period, which took place between February and April 2020, the world has become open-air and it was possible to verify the effects of the most polluting anthropogenic activities reduction on air quality [
2].
The air quality improvements caused by the 2020 lockdown were unprecedented in many parts of the world. The restrictions imposed by the pandemic on transportation and many production sectors have not prevented the values of particulate matter (PM) from exceeding the limits set by the World Health Organization. Only 24 countries have managed to stay below the safety threshold set by the WHO, out of the 106 countries surveyed by a new report prepared by IQAir and the United Nation agency [
3]. According to the NU report, the worst countries for air quality are Bangladesh, Pakistan, India, Mongolia, and Afghanistan, where the pollution surveys record average values of PM2.5 ranging from 47 to 77 μg/m
3. The UN report indicates that in 2020, all Indian cities improved in air quality compared to 2018, while 63% recorded improvements compared to 2019. However, India continues to occupy a prominent place among the most polluted cities, and New Delhi is confirmed as the urban area with the worst air pollution in the world. The report highlights that in 2020, half of the European cities exceeded the WHO parameters for PM2.5, recording the highest levels in Eastern and Southern Europe, with Bosnia and Herzegovina, Macedonia, and Bulgaria. Italy suffered the lockdown effect in a diversified way in the various regions [
4], remaining in the average of European cities.
Italy is characterized by a complex orography by different climatic zones, where, depending on the seasonal period, very favorable conditions for the pollutants accumulation and formation in the atmosphere can occur [
5]. A striking example is the river Po basin and some areas of the central-southern region in the winter period, when the generated pollutants accumulate and coastal areas that, despite of the population, present conditions that generally favor dispersion and reduce the possibility of secondary pollutants forming.
To assess the lockdown effects, it should be considered that the period of March is usually less favorable to the accumulation of pollutants than the months of January and February, when conditions of thermal inversion at low altitude and atmospheric stability often occur with high values of the main pollutants [
6]. In this perspective, the lockdown effect should be found by comparing the pollutant levels observed during the lockdown with the observations in the same late winter–spring period of the previous year. The report from the National Service for Environmental Protection (SNPA) [
6] 2020 revealed that, throughout the Italian peninsula, there has been a reduction in the concentrations of nitrogen monoxide (emitted directly) and dioxide (partly emitted directly and partly formed in the atmosphere), carbon monoxide, and benzene. The nitrogen dioxide reduction was around 40% [
7], ranging from a few percentage points to values over 70% in some sites. If it was easy to define the changes for nitrogen oxides, the effects on the levels of particulate matter (PM10 and PM2.5) are less easy to read. Particulate matter is a complex mixture of solid and liquid particles dispersed in the atmosphere that may have different sources, depending on the season and the geographical area. Some are of natural origin, such as the marine aerosol and the particles that originate from the long-distance transport of desert sands [
8]. Particulates are generated locally and may have natural origin, such as wind-lifted terrigenous particles, or anthropic origin, such as the particles emitted from vehicles due to combustion, re-suspended emissions, and friction phenomena. They could be produced by industrial and energetic combustion processes or other primary (agriculture and mining) or secondary (civil and industrial construction) activities. Particulate matter concentrations vary, not only for changes in meteorology or changes in anthropogenic emissions but also due to the influence of natural emissions. These emissions are hard to predict, due to the high variability over the years and the complex relationship to its gas precursors emitted from different sources. Hence, the correlation between PM observed levels and emission sources during lockdown is complex. In the regions where people have been forced to stay indoors, there may have been an increase in primary PM emissions from the domestic burning of coal or wood, while traffic PM emissions have been significantly reduced. Particulate matter from agricultural emissions was probably not affected by the lockdown, while some industrial emissions (e.g., PM, NO
X, and SO
X) were reduced in various sites due to the temporary stop of many factories [
9].
Certainly, during the lockdown period starting from the middle of March, very significant reductions in traffic flows were observed on a national basis [
10,
11], down to 70% for light vehicles and 38% for heavy vehicles. These levels gradually returned to previous levels in the first half of June. This traffic reduction consequently affected the particulate levels, but sometimes was compensated by emission due to domestic heating, especially for a month of March that was, on average, colder than usual. This may explain the slight increase in the particulate level observed in same areas, with respect to the season average. The contributions related to industrial and livestock activities should also be taken into account. The particulate transported by the Saharan dust should be added in some cases to the one already produced in the urban context. All these considerations may constitute a first motivation for the insignificant PM10 and PM2.5 reduction levels recorded during the lockdown period in Italy.
The lockdown resulted in a significant reduction in air pollution that has always been a peculiarity of northern Italy, due to the large concentration of industrial activities. In this perspective, the metropolitan city of Milan (Italy) appears to be particularly representative, due to the characteristics of the urban context and variety of the industrial activities. Data analysis in Milan is possible due to the public and private air quality monitoring networks installed in the area, allowing for the monitoring of the pollution levels with high space and time resolution. The need to install air quality monitoring networks arise from the need to adequately know the state of the air breathed by the population. Information on air quality is useful to public authorities, such as the government, which deals with environmental monitoring with the help of regional environmental protection agencies (ARPA). Municipalities, individuals, or companies support the government and the ARPA installing their networks for air quality monitoring [
12] in the optimal points of the city context [
13]. To carry out detailed analysis it is necessary to have a good quantity of measured data distributed over the territory with a high spatial resolution, otherwise the analysis is limited [
14]. The monitoring networks installed in Milan have low cost but were developed according to the internet of things (IoT) standards and can provide real-time data with a high temporal resolution that is easily accessible by users. The possibility of relying on data from air quality monitoring networks is fundamental for the definition of pollution levels but also for implementing models that allow, for example, the pollution evolution forecast [
15,
16] and the analysis of accidental events, such as explosions or fires [
17]. The data reliability in the latest generation of smart measuring devices can be ensured with the use of blockchain technology [
18]. Monitoring network data is also useful to the institutions for the development of air quality improvement [
19,
20].
In the present study, air quality, high time–space resolution network data, and ARPA network air quality data in Milan are analyzed to assess on the lockdown on the air quality. To this end, the concentrations of the different pollutants in the pre- and post-lockdown scenario are compared and discussed.
The Case Study
The metropolitan city of Milan is one of the largest and most populous cities in northern Italy, with approximately 1,400,000 inhabitants in the municipality alone and more than 3 million inhabitants in the metropolitan area. The air quality is strongly affected by the town activities of different nature, by which it is daily interested. The many industrial poles and massive urbanization (together with an orography that is not favorable to the pollutants dispersion) determine the frequent exceedances of the PM10 levels, with respect to the law limits. In Italy, a condition is shared with other industrial conurbations such as Turin, Venice, Naples, and Cagliari. The mountains surround the valley to the north and west (Alps), as well as south (Apennines), while the east side is open to the Adriatic Sea. This topography favors air stagnation and is accompanied by frequent winter thermal inversions and diffuse fog events that lead to the accumulation of particulate (PM) pollution [
21,
22].
4. Conclusions
The lockdown influence on air quality, due to the multiple influencing factors, is not simple to analyze. To correctly understand how the current situation has affected pollution, it is necessary to investigate these multiple factors. The analysis of pollution levels evolution in 2010–2020 highlighted the general trend of air quality improvement. In more detail, it was possible to define the PM10 specific trends for the years 2010–2020 from January to June, the period including the first lockdown of the city. Considering that February was partially involved by the 2020 lockdown, the last three years were analyzed in detail for the variations in particulate concentrations for March and April. From the analysis, is it clear that there was a progressive decrease in the PM10 average concentration of 8.15% in March, from 2019 to 2020, and 16%, compared to 2018. April, compared with 2018 and 2019, showed a reduction of 11%, with respect to 2018, but an increase of about 10%, compared to 2019. The analysis of vehicular traffic flows showed a decrease of 60%, in line with the measured particulate concentrations values, underlining the reduction of the contribution of vehicular traffic to pollution. ROM network data were used to define pollutant trends on a NIL basis, reporting the worst and identifying the km2 and NIL, where the greatest pollution reduction occurred. The maximum reduction measured in March, compared to January and February, is higher than 70% for both PM10 and PM2.5. Ultimately, the data from the ROM network were compared with the ARPA Lombardia air quality monitoring network data, showing a congruence between the datasets.