Faced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown strategy to contrast the spread of virus. Many studies have already documented that the COVID-19 control actions have resulted in improved air quality locally and around the world. Following these lines of research, we focus on air quality changes in the urban territory of Chieti-Pescara (Central Italy), identified as an area of criticality in terms of air pollution. Concentrations of $$\hbox {NO}_{{2}}$$NO 2 , $$\hbox {PM}_{{10}}$$PM 10 , $$\hbox {PM}_{2.5}$$PM 2.5 and benzene are used to evaluate air pollution changes in this Region. Data were measured by several monitoring stations over two specific periods: from 1st February to 10 th March 2020 (before lockdown period) and from 11st March 2020 to 18 th April 2020 (during lockdown period). The impact of lockdown on air quality is assessed through functional data analysis. Our work makes an important contribution to the analysis of variance for functional data (FANOVA). Specifically, a novel approach based on multivariate functional principal component analysis is introduced to tackle the multivariate FANOVA problem for independent measures, which is reduced to test multivariate homogeneity on the vectors of the most explicative principal components scores. Results of the present study suggest that the level of each pollutant changed during the confinement. Additionally, the differences in the mean functions of all pollutants according to the location and type of monitoring stations (background vs traffic), are ascribable to the $$\hbox {PM}_{{10}}$$PM 10 and benzene concentrations for pre-lockdown and during-lockdown tenure, respectively. FANOVA has proven to be beneficial to monitoring the evolution of air quality in both periods of time. This can help environmental protection agencies in drawing a more holistic picture of air quality status in the area of interest.

Particular Matter (PM) data are the most used for the assessment of air quality, but it is also useful to monitor VOC and CO. The health impact of PM increases with decreasing aerodynamic dimensions, therefore most of the monitoring is aimed at PM10 (fraction of PM with aerodynamic dimensions smaller than 10 µm) and PM2.5 (fraction with aerodynamic dimensions lower than 2.5 µm). Generally, anthropogenic emissions contribute mainly to PM2.5 levels, whereas natural sources can largely affect PM10 concentrations. PM2.5/PM10 ratio can be used as a proxy of the origin (anthropogenic vs natural) of the PM, providing a useful indication about the main sources of PM that characterizes a specific geographical or urban setting. This paper presents the results of the analysis of continuous measurements of PM10 and PM2.5 concentrations at eight stations of the regional air quality monitoring network in Abruzzo (Central Italy), in the period 2017–2018. The application of models based on machine learning technique shows that PM2.5/PM10 ratio can be used to classify PM emissions and to know the nature of the emission source (natural and anthropogenic), under determinate conditions, and properly taking into account the meteorological parameters.

The tropospheric production of O₃ is complex, depending on nitrogen oxides (NO_x = NO + NO₂), volatile organic compounds (VOCs), and solar radiation. We present a case study showing that the O₃ concentration is higher in a rural area, 14 km downwind from a coastal town in Central Italy, compared with the urban environment. The hypothesis is that the O₃ measured inland results from the photochemical processes occuring in air masses originating at the urban site, which is richer in NO_x emissions, during their transport inland.To demonstrate this hypothesis, a feed forward neural network (FFNN) is used to model the O₃ measured at the rural site, comparing the modeled O₃ and the measured O₃ in different scenarios, which include both input parameters related to local O₃ production by photochemistry and input parameters associated with regional transport of O₃ precursors. The simulation results show that the local NO_x concentration is not a good input to model the observed O₃ (R = 0.17); on the contrary including the wind speed and direction as input of the FFNN model, the modelled O₃ is well correlated with that measured O₃ (R = 0.82).